- Email: [email protected]
A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria
Journal of Building Engineering 24 (2019) 100753
Contents lists available at ScienceDirect
Journal of Building Engineering journal homepage: www.elsevier.com/locate/jobe
A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria
T
Desmond Eseoghene Ighravwea, Sunday Ayoola Okeb,∗ a b
Department of Mechanical and Biomedical Engineering, The Bells University of Technology, Ota, Nigeria Department of Mechanical Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria
ARTICLE INFO
ABSTRACT
Keywords: Sustainability criteria Multi-criteria decision-making tools Public buildings maintenance Maintenance strategies
This article tackles how to select an adequate maintenance strategy for public buildings by using novel multicriteria decision-making (MCDM) models to validate their appropriateness for sustainable practices. The new innovation is the unique advantage of fuzzy axiomatic design (FAD) to consider the design requirements for public buildings. The stepwise weight assessment ratio analysis (SWARA), weighted additive sum product assessment (WASPAS), fuzzy axiomatic design (FAD) and the additive ratio assessment (ARAS) are competently integrated. Questionnaire data from a public building was used to evaluate and order the criteria in the rehabilitation exercise. The principal results are: For environmental and social criteria, the most and least suitable maintenance strategies are condition-based (S3) and preventive maintenance (S1) strategies, respectively. The most and least suitable maintenance strategies are corrective maintenance (S4) and S1 (ARAS), S4 and S1 (FAD), S1 and S3 (weighted additive method), respectively. Based on the aggregated criteria and the solution methods, the most suitable order for the case study maintenance strategies is S2 > S1 > S3 > S4. This study shifts attention to MCDM from the old literature perspectives of risks and life-cycle methods in the strategic choice of maintenance for public buildings.
1. Introduction The maintenance of public buildings is associated with considerable expenditure but intuition still remains the order-of-the-day in public buildings [1,2]. Consequently, public buildings continuously incur elevated cost of building operations and maintenance [3,4]. Nevertheless, scientific methods to determine what maintenance policy to adopt for public building are still under-reported in literature [5]. The consequence of this is that substantial cost is lost annually to the wrong choice of maintenance strategies for buildings [5,6]. It is therefore important to develop scientific methods to guide decision-making and sustainability of public building [7]. This gap has been recognised in the current research and pursued rigorously with the use of novel multicriteria decision models since many criteria are involved in this problem. Quite a substantial number of multicriteria approaches have been adopted to tackle scientific and engineering problems [8–12]. However, insignificant contributions to multicriteria models have been made in public building maintenance and sustainable building problems for the past years. The unique approaches were grouped according to the kind of decision approach they are relevant to, according to Polatidis et al.
∗
[13]. The novel contribution of Polatidis et al. [13] carefully recognised these kinds of approaches as four: outranking [8–12], utility-rooted, programming approaches and others. Outranking approaches include ELECTRE, REGIME and PROMETHEE [8–12]. For the utility-rooted approaches, examples include AHP, MAUT, SAW and SMART [14–16]. The programming approaches are mainly multi-objective programming supports [17,18]. Lastly, the other methods include SMAA, NAIADE and FLAG [19–21]. In the multicriteria modelling domain, often circumstances arise that options which may be contemplated are in original terms infinite in nature. However, Pokharel and Chandrashekar [22], Ramanathan and Gunesh [23] argued that the employment of multiobjective programming approaches is recognised to competently address the situation in building strategic choices and sustainable practices. Nonetheless, experience from the literature clearly reveals that these methods confront substantial limitations: Occasionally they result in an infeasible option [23]. Given this drawback, there is a requirement to recommend MCDM approaches as tools to conveniently and reliability tackle the building strategic choices and sustainable practices [22]. It is compelling to contemplate on the important criteria of maintenance strategies in order to make worthwhile decisions. The criteria
Corresponding author. E-mail address: [email protected] (S.A. Oke).
https://doi.org/10.1016/j.jobe.2019.100753 Received 19 September 2018; Received in revised form 13 March 2019; Accepted 24 March 2019 Available online 03 April 2019 2352-7102/ © 2019 Published by Elsevier Ltd.
The AHP is an organised method to examine complicated decisions. With foundation in psychology and mathematics, it emerged only in the 1970s as a decision making method used by teams globally for a broad array of subjects, including maintenance, facility design, quality management and safety engineering [36]. The world's re-known scientist, Thomas Saaty is credited to have originated the method [36,37]. Instead of stating the precise decision, AHP assists researchers to obtain the decision that most likely yields to their objectives and offers insights into the problem being solved [37] The PROMOTHEE has a complementary model referred to as geometrical analysis for literature aid (GAIA) used to analyse decision scenarios. It has foundation in sociology and mathematics and emerged in the early 1980s, and used by professionals in healthcare, government organizations, educational sector and the transportation industry. The popular Brussels Professor, Jean-Pierre Brans is known to have introduced the method in 1982 [39]. Nonetheless, the collaborative developmental effort of Brans was complemented by Professor Bertrand Maresehal that made PROMETHEE to be closely tied to GAIA in decision analysis. The popularity of PROMETHEE was due to the success of the technique in offering researchers and decision makers with total and partial ordering of issues. Complimentarily, the GAIA permits researchers to visualize the principal characteristics of a research problem. Thus, it is possible to establish conflicts and promote synergies among criteria in a straightforward manner. It is also easy to establish clusters of issues as well as indicate substantial accomplishments. PROMETHEE are of versions, including PROMETHEE I an II [39,40] The ELECTRE is a two-phase technique: construction of single or multiple outranking associations and to elaborate on the suggestions of the construction stage through an exploitation process. The principal goal of the first stage is to weigh each pair of issues again each other. The method evolved in 1965 by a team at SEMA consult, headed by the popular Bernard Roy whose name is often strongly associated with the radical changes of the technique from its original form ELECTRE to its expansions as ELECTRE I, II, III, IV and ELECTRE IS as well as ELECTRE TRI. The applications of the method has been well pronounced in small hydropower systems, design and development projects [41]
Analytical hierarchy process (AHP)
Elimination Et ChroixTraduisant la REalite or Elimination and choice Expressing Reality (ELECTRE)
Preference ranking organisation method for enrichment evaluation (PROMETHEE)
Brief information (history)
Methodical description
Table 1 Summary of the various MCDM models.
antagonistic, mutual strength and mutual weakness [41]
vagueness and uncertainty in • Considers computations [34] structure of the technique is designed to • The consider three kinds of interferences:
to apply; do not assume the • Straightforward proportionate state of the criteria [34]
is flexibility to compute the outcome • There with the Excel sheet [38]
[38]
result [38]
are plausible in that often members of • Results the group agree on the resulting preferences
of hierarchical structuring in the • Presence decision problem identified to integrate many inputs from • Ability experienced experts to form a consolidate
Advantages
2
•
•
•
layman. Outranking results in the strengths as well as weaknesses of the options [34] Technique not suitable to use to reflect a substantial amount of interferes among criteria [41] Only applicable to choose superior options which empowers order in a pool of options [36] The method fails to be competent as a chosen group of options comprises of circuits and the chosen group is not unique [36]
procedure to obtain results may be • The complicated to understand by the
approach to assign weight is not • The clear [34]
challenges confronted by the researcher to describe the request to give a second consideration to inputs [38]
relate a group of items [38]
that the index of consistency • Provided is greater than 10%, these are usually
one factor against the other • Weighing is definitely an artificial manner to
Disadvantages
(continued on next page)
Transportation, energy, environmental and water management [34]
Agriculture, hydrology, finance, transportation, water management, logistics, manufacturing and assembly environment [34]
Choice, ranking, prioritization, resource allocation, benchmarking, quality management, conflict resolution [38]
Areas of applications
D.E. Ighravwe and S.A. Oke
Journal of Building Engineering 24 (2019) 100753
A case-based reasoners, attempts to provide novel solutions to problems from a previously developed data-base of cases, through the use of, or by means of adopting solutions employed to tackle old problems [42,43]. The case-based reasoner as well provides a reasoning platform, which is the same with the manner that a lot of problem solvers commonly use to solve problems. The principal field on which case-based reasoning relies on is psychology, while the specific theories that are fundamental to CBR are the theories of working of the human memory. The two important events in the history of CBR are the episodic memory and the schema that were credited to Tulving in 1972 and Rumelhart in 1977, respectively. The schema acts as a reasoning procedure to use information chunks in novel circumstances. However, the episodic memory offers a way to store and recall huge information associated with occurrences, events, stories and scenes.
DEMATEL, which stands for Decision making trial and evaluation laboratory, is a competent technique to establish the root causes and the effects aspects for a complicated scheme [48,49]. The principal concern of DEMATEL is to appraise the interdependence of associations within the parameters/factors [48]. Further efforts are committed to searching for the critical factors by way of a visual structural replication [48]. The research center called Geneva, which is located at the Battelle Memorial Institute, has the credit of first proposing DEMATEL as an effective multicriteria methodology [48]. The chief illustrative tools for gaining insight into the working of DEMATEL are the workings of DEMATEL are the digraphs and matrices [48,49]. DEMATEL is built to confirm interdependence within parameters and could assist to build up a map which reveals comparative association among them as well as capable to study and tackling complex and inter-wined challenges [48,49] The original development of SAW is closely linked to Churchman and Ackoff in 1954 [36,50]. The major activity performed by SAW at its initial stage was to tackle the problem of choice concerning portfolio [36]. A number of authors claim that the SAW approach is the most superior, well-known and broadly employed [36,51,52] due to its straight-forward nature and capability to establish non-enhanced weakness of options. The SAW is largely intuitive in nature and a straight-forward approach to tackle challenges in multicriteria problem solving [36,52]. The reason behind this is that the linear additive scheme could reflect the preferences of the decision maker on individual basis. The basis of SAW lies in the calculation of the weighted total of the accomplishment ratings of all options for every criterion [36].
Case-based reasoning (CBR)
DEMATEL
Simple Additive Weighing (SAW)
Brief information (history)
Methodical description
Table 1 (continued)
Capability to reward within criteria; it is intuitive in nature to those who make decisions, straightforward computation and there is no need for complicated computer algorithms [34]
•
•
to build up issues [34,42] CBR creates a straightforward maintenance procedure in that it needs little maintenance; for the database to exist an insignificant upkeep is required [34,42] Establishes associations among criteria; these could be through direct method or indirect means [41]
not necessarily need knowledge to build up • Do rules or approaches creates in a straightforward way and • CBR requires minimum efforts to obtain extra data
the database [34]
application
could enhance with the passage of time • CBR particularly when extra cases are appended to
is structured to obtain justification by • CBR precedence in nature – mimics the way that we • Intuitive operate building maintenance scheme learns • The through the process of obtaining novel cases by
Advantages
SAW employs merely increasing appraisal measure, whereas reducing appraisal measure ought to be transformed into the reducing ones of the individual method before their uses [53]
indirect association is obtained is currently not clear at all [41]
procedure through which the • The combination of direct as well as
adaptation
of CBR by standard are • Schemes limited to reasonable solutions to irregularities in the • Itdatais sensitive being analysed [34,43] cases may be challenging • ToCBRadapt restriction in being unable to • offer has the most advantageous solution of occupying huge storage • Capable compartment in every case studied to generate cases through • Possibility hand requires algorithms on case-base, • CBR case choice and most likely, case-
Disadvantages
3
(continued on next page)
Business, waste management and financial management [34]
Technology park, railways, ecotourism, finance [48], Aviation transport [49]
Petroleum refinery and offshore [44,45], supplier selection [46], engine-oil design [47]
Areas of applications
D.E. Ighravwe and S.A. Oke
Journal of Building Engineering 24 (2019) 100753
TOPSIS is often called a multi-criteria tool with credit of development due to two notable experts, Ching-Lai Hwang and Yoon during the year 1981 [34,36]. As Yoon afterwards further developed it in the year 1987, Hwang, Lai and Liu committed more energy to its further development in 1993. The basis of the idea on TOPSIS is that the selected option ought to exhibit the smallest geometric distance among the criteria to the positive ideal solution (PIS) as well as the furthest geometric distance away to negative ideal solution [34]. TOPSIS is an approach that compensates in an aggregation procedure in which a group of options are weight against one another by first establishing the weights for all the criteria [34]. For TOPSIS to work, it is assured that criteria grows or shrinks in a monotonic manner. Since the parameters are of different dimensions, there is a requirement to normalise. TOPSIS encourages trade-offs among the criteria which implies that as a criterion performs poorly, correction could be made by the other criterion [34]. This is a straightforward, yet comprehensive, quantitative and qualitative in nature. It often exhibits the linear additive representation by mathematical nature. The meaning is that a total value of a stated option is computed as the total sum of the accomplishment value for every criterion considered as a product operation with the criterion's weight [34] This multi-criteria tool is rooted in the anticipated utility conjecture. The honour of original development is credited to Keeney in 1971 and a year later, to Savage. The principal aim of MAUT is to assist decision makers attach utility values rooted in their choices [36]. To attain an intersection of decision making, MAUT is often displayed as a structure in hierarchy which disintegrates the utility function to real-life nature [36,52]. In the MAUT scheme, the total utility value for every option may be computed by combining the preferences of every decision maker in the direction of all appraisal criteria [36]. The superior option may be established rooted in the utmost utility value [36].
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
Multi-attribute utility theory (MAUT)
Simple Multi-Attribute Rating Technique (SMART)
Brief information (history)
Methodical description
Table 1 (continued)
Capability to integrate features of prediction and customer satisfaction approximate values in obtaining solutions to appraisal problems [36]
Straightforward, permits all kinds of weight assignment method; minimal effort by users (decision makers) [34]
•
computerize the number of stages, which remains constant irrespective of the amount of attributes considered [34] TOPSIS′ ability to compensate the criteria's poor performance by the correction of another, referred to as trade-off, is often taken as an advantage and realistic approach to modelling.
procedure to apply this tool is • The straightforward. It is as well straightforward to
Advantages
4
•
decision makers is a challenging issue [36,54] MAUT is without the standard checks for consistency of the appraisal scheme [52,55]
develop a correct function for • ToMAUT which reflects the choices of the
The process could be in-convenient when the structure is contemplated [34]
By applying Eucelidean distance, the procedure fails to contemplate on the association of the attributes; challenging to weigh and maintain uniform decisions [34]
Disadvantages
Nuclear energy, software reliability
Manufacturing assembly tasks, construction, military, environmental, transportation as well as logistics [34]
Engineering, water resources, supply claims, human resources, manufacturing [34]
Areas of applications
D.E. Ighravwe and S.A. Oke
Journal of Building Engineering 24 (2019) 100753
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 2 Some previous studies on integrations of SWARA. Authors
Hybrid models
Karabasevic et al. [56], Stanujkic et al. [57] Karabasevic et al. [58] Zolfani et al. [59] Sarfaraz and Bahrami [60] Aghdaie et al. [31] Karabašević et al. [32] Ruzgys et al. [61] Krylovas et al. [62]
SWARA and MOORA (Multi-Objective Optimisation on basis of Ratio Analysis) SWARA and ARAS SWARA and Yin-Yang balance theory SWARA-COPRAS (Complex Proportional Assessment) SWARA and COPRAS-G (Complex Proportional Assessment of alternatives with Grey relations) SWARA and WASPAS SWARA and TODIM SWARA and VIKOR (VIsekriterijumska optimizacijai KOmpromisno Resenje)
are individually associated with how to reduce the use of energy emissions of CO2, building expenses, attaining utmost degree of thermal conflict and working within the budget for the financial year. Nonetheless, at times, the criteria could conflict and the most desirable option is to consider a trade-off association. Due to this motivation, the manager is saddled with the choice of appropriate maintenance strategy that will work [6]. From the bank of knowledge in building research literature, intensive interests have been shown by scholars on building maintenance but priority has excluded public buildings in reported cases [3,24–27]. In addition, despite the quantitative nature of building maintenance strategy formulation and implication, the use of empirical models for the proper evaluation of the situation has been less influential [28]. Even the available empirical models have not adequately treated the subject in a rigorous quantitative manner as to permit inferences based on fuzzy assumptions on models [28]. In the foregoing, it was decided to bridge this knowledge gap by developing a quantitative model that visualises maintenance strategy from the dimension of problem formulation and solution of multi-criteria decision-making (MCDM). Hackman and Scott [29] reported empirical frameworks to identify the rationale behind the selection of particular maintenance strategy to maintain a building. This problem is more challenging when considering sustainability criteria for building maintenance [28]. Although, Jolaoso et al. [30] identified the best maintenance strategy for estates in a developing economy based on government agency, self-tenants, private agents maintenance strategies in term of planned or unplanned maintenance strategies, their study did not provide information. The aim of this study is to select the most suitable maintenance strategy for public buildings maintenance using weighted additive sum product assessment (WASPAS), fuzzy axiomatic design principles, additive ratio assessment (ARAS) and sustainability criteria. In this study, step-wise weight assessment ratio analysis (SWARA) method is used to determine weights for the sustainability criteria and sub-criteria. To the best of our understanding, no study has reported the hybridisation of FAD, WASPAS, ARAS and SWARA methods, especially for maintenance strategies ranking for public buildings. The application of FAD principles and ARAS helps to relate maintenance strategy with sustainability criteria for buildings. The results from FAD principles are compared with ARAS results. We then use a WASPAS method to determine the final ranks of different maintenance strategies. The remaining sections of the current study are organised as follows: Section 2 is the literature review. Section 3 presents the proposed FADSWARA methodology. In section 4, the application of proposed methodology in a case study is presented coupled with the major contributions of the paper. The conclusions of this study are presented in section 5.
reality several objectives and the accomplishment of the system is evaluated by attributes [31]. In this section, the review of literature is based on an analysis of several multi-criteria schemes, hybrids of SWARA and building sustainability. The SWARA hybrid is presented to show the potentials of SWARA as a veritable tool for hybrid models. Hybridisation is an idea, which permits us to take advantage of SWARA and the other multicriteria tools. This idea is synonymous to crossbreeding in science and new multi-criteria structures are formed which are hybrids of the two multicriteria tools. The hybrid has advantages over individual techniques and could help decision makers to handle information in a better manner. 2.1. Comparative examination of MCDM approaches As noted from the literature review, several excellent reviews on multi-criteria models have been contributed. Such extra-ordinary discussions always involve elaborations on their history, advantages and short-comings of the models as noted in Velasquez and Hester [34], Campos-Guzmán et al. [35] and Alabool et al. [36]. However, a summary of this information is offered in Table 1. A large number of studies have been performed tangential of this work (Table 2). Table 3 contains a summary of significant papers on building sustainability. 2.2. Conclusions arising from the literature survey In an attempt to gain in depth insight into the literature on public building a broad array of survey was conducted. The subsequent conclusions are informed from the research:
• A broad database is available in the building sustainability knowl• • •
2. Literature review Decision making emerges in building maintenance as a critical stage that decides the success or failure of building policy implementation in public buildings [1,3]. It involves through analysis of influential options, complications, elevated-risk consequences as well as uncertainties and concerns about inter-personal interactions [31–33]. In
• • 5
edge domain for the various types of buildings, including public buildings Significant efforts have been directed to improve building sustainability (Table 3). However, there is a gap on how to apply hybrid multi-criteria tools, such as SWARA integrated models (Table 2), to building sustainability concerns. Maintenance impacts on the sustainability of buildings positively and its effectiveness largely dictates the degree of building sustainability. Various approaches to maintenance strategies of buildings are feasible. A number of these approaches are more widely used than others. For instance, the preventive maintenance strategy is the dominant approach in most building systems. Commonly, the building owners adopt the strategy with the least cost and one that is viable commercially for wide application in a group of building. Thus, the preventive maintenance scheme offers a significant part of maintenance expenditures in most building systems. Quantitative preventive maintenance models can be employed to estimate the future breakdowns of building and when preventive maintenance activities should be carried out. Mathematical optimisation schemes could be used to search for the
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 3 Summary of important studies on building sustainability. Author
Work done
Yu and Kim [63]
Reviews schemes of building condition to appraise the quality of indoor air that impacts on the wellbeing as well as health of occupants, considering buildings and homes to be sustainable. Asserts that the representations based on LEED and BREEAM are broadly employed globally. While the LEED representation is biased towards health and well being, including indoor environmental quality, the focus of BREEAM is towards the efficient usage of energy. The paper advocates for a management plan on indoor air quality for the development of buildings, entailing an approval on indoor air quality while the living spaces are contemplated. Further asserts that depleted levels of emissions are required from the materials employed in new buildings. The indoor air in new building plan is as well advocated to include continuous maintenance of the systems of ventilation as well as HVAC Discusses case examples of old buildings that are recycled into libraries from Germany as well as European nations. The problems and the prospects to sustain the library buildings were elaborated upon with a focus on planning. The importance of this recycling process was emphasized to lessen the ecological foot-print of the buildings and from the perspectives of cost-effectiveness and efficiency. Advance and builds up a representation to appraise the sustainability of a building from the lens of building information modelling technique coupled with its permissible know-how. The span of sustainability entails production of materials, operation, demolition, disposal, construction and maintenance. All these are the life-cycle elements of a building and the approach may be as well described as life-cycle based. Discusses policies, which could cause a movement of the world economy to a largely sustainable direction. The focus of discussion was on a largely politically sustainable group of put forward idea towards green pursuits preferable to the existing pursuits. Elaborates on the operations of a group of trainers that adopts evidence-rooted method to build up and offer programmes (training) on sustainability with target on professional in built environment in Wales. The approach tackles the buildup of stall requirement that entails the training anticipation in the confines of the sector, view from short, middle and long ranges in planning. The advantages of the implementation of the structure to the Wales region are elaborated from the viewpoint of policy appraisal. Reviews diverse research approaches from the survey of literature, paying keen attention to the advantages and problems failed by the building information modelling (BIM) with reference to software application. Treated a case application in design to appraise diverse scenarios on a put-forward manifold-application building. It was stated that the software offers information required to improve design as well as the accomplishment of buildings. Elaborates on the function on the function of building information modelling (BIM) to lessen the fragmentation within the profession, involving professionals, considered for all the phases of building delivery with evidence obtained from the academic literature. Assets that BIM offers a repository which permits straightforward opportunity to and interfaces with information as well as knowledge considered in real-time. Concludes the BIM offers a structure for professionals to operate in a joint circumstance in all phases of delivery of building services. Applies an appraisal method to neigh in u- use of energy against data from environmental accomplishment from building situated in England that are planned to operate at elevated sustainability thresholds as well as depleted heating requirement. It was found that despite that the two buildings attained evaluated rates of air permeability, they experienced common concerns associated with weak documentation regarding the as-built drawings, challenges relevant to direction and weak handover, challenges relating to joining novel know-how, poor sub-meter calibration and weak controls of windows. Reveals the outcome of the appraisal conducted, by means of worldwide known BSRS, that is Building sustainability rating systems, on varied housing schemes developed within the frame work of the BSRS. The adopted approach implemented a normalisation scheme because of the complication they offer in conducting a comparative examination was discussed. A case examination was contributed that reflects that buildings built up using the funding program for housing solutions (FPHS) attained depleted assessment values with respect to the BSRS. The challenge was in the substantial weakness of materials, efficiency of energy, quality of the indoor environment, as well as management. It was concluded from the research that a number of concerns with the FPHS assessment procedure work against the joining of sustainable features of the Mexican social housing scheme. Establishes the idea of sustainable building and the function that the application of building and the function that the application of building integrated photovoltaic (BTP) schemes plays to sustain structures. The validity of idea was tested using countries in the southern part of Asia as examples. A comprehensive survey of the application of the above mention scheme for the southern Asia countries together with the problems and barriers that the BIP scheme pose were elaborated upon. Discussed the spread of building coverage in geographical terms based on non-linear joint influence representation. The outcome was show cased in terms of unrestricted access data that evaluates the geographical spreading focusing on predictions, standard deviation and mean. The research was noted to offer quantitative insights concerning the models employed for prediction. Tackles the sizing problems of establishing the most advantageous sizing coupled with the choice of the commonest know-how for water supply and energy considering buildings that are sustainable. The solution was attained by manifold-objective and manifoldsituation joint integer non-linear optimisation technique. An attractive feature of the presented model is that it permits the user to establish the wishes of the designer rooted in a pre-established weight to attain the trade off results employing a route of dimensional analysis. The outcome of the research reveals the necessity of incorporating the non-linear feature of the know how's to establish an appropriate strategy for operations. Examine the sustainability accomplishment of modular buildings for the lens of life-cycle. The appropriate measures for sustainability were hinged on accomplishment factors based on unit buildings, built and ordered. The advanced representation was used in practice with convincing data using unit building from Canada. The research serves good purposes for practicing construction experts from the methodical structure proposed.
Hanke and Werner [64] Yung and Wang [65]
Bartolini [66] Hopkinson and Gwilliam [67]
Oduyemi and Okoroh [68]
Fadeyi [69]
Gupta et al. [70]
Saldana-Marquez et al. [71]
Shukla et al. [72]
Soliman et al. [73] Fuentes-cortes and Flores-Tlacuahuac [74]
Kamali et al. [75]
• • • •
• Local Nigerian data with unique characteristics in terms of costs,
utmost maintenance cost for buildings and the prediction of time estimates to carry out building maintenance activities. Sustainable maintenance of buildings has been at the central of research and the themes of a few scholarly journals are oriented towards promoting sustainable building maintenance. Sustainable maintenance practices are a necessary focus of the current research Very sparse information on the sustainability of public facilities through maintenance exists in literature on developing countries context but more exists in the Nigerian perspective Multi-criteria mathematical modelling of building maintenance has been scantily pursued
•
cultural influence on work behaviour, and management style associated with building maintenance practices have not been adequately explored. Training as a vital element in the knowledge upgrade of building maintenance manager has not been exploited fully in developing countries.
3. Methodology In order to account for experts' judgement in building maintenance strategy selection, this study presents a multi-criteria decision-making 6
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Fig. 1. Conceptual framework to select maintenance strategies for public building sustainability.
(MCDM) framework based on social, technical, economic and environmental criteria. This creates the opportunity for conflicting criteria to be analysed simultaneously, for example, maintenance cost and the quality of maintenance work. The framework also considers design requirements and expected outcomes from maintenance activities. It makes the expected outcomes to be defined using fuzzy numbers and uses more than one expert's judgement for solution to this selection problem. It allows experts to use linguistic terms when analysing maintenance strategies for building maintenance (Fig. 1). Over the years, different maintenance strategies have been used to maintenance public building [30]. Preventive (S1), predictive (S2), condition-based (S3) and corrective (S4) maintenance strategies are among the frequently used maintenance strategy for public buildings [29,30]. To determine the most suitable maintenance strategy for a public building, four sustainability criteria are considered (Table 4). The information on the sub-criteria for each of the sustainability criteria is based on the knowledge obtained from literature [28]. The
maintenance strategies and the sustainability criteria are connected together using FAD principles and ARAS method (Fig. 1). The generation of information contents for the FAD principles selection process is based on two sets of weights. The first set of weights is obtained from sub-criteria, while the second set of weight is for the sustainability criteria (Table 4). 3.1. SWARA method The application of SWARA entails five steps. Brief explanations of the steps that are required for SWARA application are as follows [56,106]: Step 1: Arrangement of criteria in descending order. The arrangement depends on the criteria expected significances. Step 2: Evaluation of the comparative importance of average value (sj). This involves the determination of the relative importance of the criterion j with respect to previous criterion (j-1). For the 7
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 4 Criteria for maintenance strategy selection of buildings. Criteria Environmental (x1)
Economical (x2)
Social (x3)
Technical (x4)
Sub-criteria
Description
Energy efficiency (x11) Emission reduction (x12) Waste reduction (x13) Water usage reduction (x14) Improved building value (x21) Better rental returns (x22) Reduced operational cost (x23) Extended building life-span (x24) Improved productivity (x31) Occupants' health and safety (x32) Human traffic (x33) Occupants' satisfaction (x34) Maintenance failure and downtime (x41) Spare parts quality and materials (x42) Staff expertise (x43) Business operations (x44)
Aslani et al. [76], Lidelöw et al. [77], Danish et al. [78], Silvero et al. [79], Shadram et al. [80] Moschetti et al. [81], Almeida et al. [82] Burlacu et al. [83], Sáez et al. [84] Fuentes et al. [85], Marinoski et al. [86] Gunay et al. [87], Pham et al. [88] Cajias et al. [89] Hu et al. [90], Nzukam et al. [91] Cao et al. [92], Hamdan et al. [93] Krarti et al. [94], Gunay et al. [95] Cross et al. [96], Littlewood et al. [97], Hon et al. [98] Sarlo et al. [99] Awada et al. [100], Geng et al. [101] Yang et al. [102] Mahmoudkelaye et al. [103], Cao et al. [104] Puķīte and Geipele [105] Puķīte and Geipele [105]
Step 4: Evaluation of the recalculated weight (qij ) using Equation (4)
Table 5 Triangular fuzzy numbers for comparative importance of the sustainability criteria and sub-criteria. Linguistic terms
Triangular fuzzy numbers
Fairly important Moderately important Highly important Extremely important
0.1, 0.3, 0.5, 0.7,
0.3, 0.5, 0.7, 0.9,
qij =
0.5 0.7 0.9 1.0
wij =
x ij =
ij
ij
=
K k=1
ij
K
,
K k = 1 ij
K
,
K k = 1 ij
K
+ 4 ij+ij 6
1 j=1 x ij + 1 j > 1
∀i(4)
qij n q j = 1 ij
∀i(5)
3.2. Hierarchical fuzzy axiomatic design (HFAD) principles The underlying information on the strategic choice of building maintenance, particularly public buildings has captured the attention of researchers and building practitioners in the past few years. Acquiring such information from maintenance policy models can offer public building owners competitive benefits to manage buildings competently. This paper has brought about an innovative method that utilizes fuzzy axiomatic design as an appropriate method to solve this problem due to the substantial benefits it offers the building community. Erden et al. [107] declared the benefits of the axiomatic design component of the fuzzy axiomatic design structure to include the incentive to make the researcher more innovative and dynamic. The authors also advanced the advantages of the method to include a platform to minimize unwarranted procedures and enhance design tasks [107]. The above advantages are the motivations for our interest in the fuzzy axiomatic design. Thus, we assume that the method of fuzzy axiomatic design should be advantageous to the development of a multi-criteria decision making model for public building maintenance strategy selection. The selected maintenance strategies are ranked based on two hierarchies of sustainability concepts. First hierarchy deals with general classification of sustainability criteria, while the second hierarchy considered sub-criteria of each of the sustainability criteria. In order to integrate the two hierarchies, HFAD methods are considered. The procedures for the HFAD methodology are explained as follows: The initial steps for HFAD implementation involve the design of membership functions for evaluation the system requirements of each of the alternatives based on selected criteria (Table 5) - the fuzzy numbers are trapezoidal fuzzy numbers (see Fig. 2). When considering more than one decision-maker, the different membership function intervals for each decision-maker are aggregated into a single interval (Equations (6) and (9)).
(1) (2)
Step 3: Evaluation of the coefficient (kij ) using Equation (3)
kij =
j>1
where wij represents the weight of criterion i and sub-criterion j.
Since the current study is based on multi-criteria decision making, the aggregation of decision-makers responses is expressed as Equation (1). The crisp value for an aggregated fuzzy number is expressed as Equation (2) ij ,
j=1
Step 5: Determination of the relative weights (wij ) of the evaluation criteria using Equation (5). The sum of the weights for all the criteria must be equal to 1.
building maintenance selection problem considered here, three possible options can reflect the fuzzy environment considered: the use of fuzzy numbers, linguistic terms or fuzzy sets. However, due to the nature of the problem, linguistic terms are considered appropriate and hence chosen for the research. For this choice, it is required to translate these linguistic terms into fuzzy numbers for further processing. This entails the process of defining the vague expressions by the use of triangular fuzzy numbers (TFNs). Then the TFNs of the design range, ratings and system range may then be considered for further processing. The TFNs are on other words the tools to reflect the design goal and the attributes of the option. In this study, the comparative importance of the sustainability criteria and sub-criteria are expressed in linguistic terms (Table 5). The linguistic terms in Table 5 is used to compare the importance of a criterion to another criterion. For example, how important is emission reduction to energy efficiency during the selection of a sustainable maintenance strategy? The conversion of the linguistic terms to crisp values is done using fuzzy numbers. The partitioning of these numbers is based on researcher's experience. This implies that a linguistic term can be partitioned into different fuzzy numbers.
ij,
1 kij 1 kij
∀i(3) 8
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
ij
ij
= min(
=
k ij )
(6)
2
K k k = 1 ij
(7)
K K k k = 1 ij
ij
=
ij
= max( ijk )
(8)
K
(9)
ij ij
+
1 ( 3 ij
ij ij
+
)2 ij
1 ( 3 ij
ij ij ij
ij
Pij =
1 pij
DR ij
DR ij
(13)
wij Iijs
(14)
m
wi Isi
Is =
(15)
i=1
where Iijs represents the information content of criterion i sub-criterion j for maintenance strategy s, Isi represents the information content of sustainability criterion i with respect to maintenance strategy s, and Is represents the information content of maintenance strategy s.
(10)
3.3. Additive ratio assessment (ARAS) During the normalisation of parameters during the implementation of ARAS, the preference directions of parameters are considered. For parameters that the smaller-the-better is preferred, two equations are considered (Equations (16) and (17)). Equation (16) is used to convert a smaller-the-better parameter to a bigger-the-better form [110]. Parameters whose preferred values are bigger-the-better are normalised directly using Equation (18).
x˜i = x¯i = x¯i =
1 xi
(16)
x˜i n x˜ i=1 i
(17)
xi
n x i=1 i
(18)
The normalised parameters are used to form a normalised decision matrix. Based on the weights for the various criteria in the normalised decision matrix, a weighted decision matrix is formed (Equation (19)). During the formation of a normalised decision-matrix, provision is usually made for ideal values (i.e., ideal optimisation direction) of the criteria. The ideal values are usually placed in the first column or row of a normalised decision matrix [110]. The ideal solutions for the criteria are often by considering the preferred values of the criteria. Since the values in the normalised decision matrix are usual within 0 and 1, the maximum preferred solution is 1, while the minimum preferred solution is the minimum values among the set of alternatives [110]. In the current study, the weights that are obtained using the SWARA method are considered during the implementation of the ARAS method. Other weights determination approaches can also be considered (fuzzy
(11)
CR SR
+
j=1
The information content of each of the sustainability sub-criteria is expressed as Equation (11). The value of Pij is obtained by considering the system requirement (SR) and the common range (CR) between design (DR) and system requirements for a sustainability sub-criterion (Fig. 4) [108]. In the typical design scheme for the building maintenance strategy selection, the success of the design, measured in probabilistic form, is strongly influenced by the inclination of the building maintenance manager, who acts as the designer of the public building being examined. This is expressed with respect to the level of tolerance that should be permitted. However, it is also influenced by the capability of the system from the perspective of delivery. From Fig. 4, the attainment of tolerance and system capabilities are respectively termed design range and system range. Nonetheless, the intersection (overlapping portion) of the design range with the system range gives what is referred to as the common range. The permissible design resolution is restricted to the shaded area, the common range. The same explanation applies in all cases irrespective of the shape of the function used: trapezoidal, triangular or the normal curve. Also note that the terms “designer's appraisal extent” and “design range” are used interchangeably in literature and “system capability range” is sometimes replaced with “system range” in scientific discussions. Equation (13) is used to determine the crisp value of CR [108].
Icij = log2
SR ij
n
Isi =
)2
ij
SR ij
DR 2 ij )
SR ij
where Pij is the probability of satisfying sustainability criterion i with respect to sustainability sub-criterion j. The weighted information content [109] for the different sub-criteria is expressed as Equation (14). The best maintenance strategy for building maintenance is the maintenance strategy that generates the lowest value for Equation (15).
where K denotes the total number of experts. The crisp values for aggregated values of the trapezoidal fuzzy number are expressed as Equation (10). This study considered trapezoidal fuzzy number when considering the responses from the different decision-makers (Table 6 and Fig. 3).
x ij =
(
CRij =
(12)
Table 6 Trapezoidal fuzzy numbers for the effectiveness of maintenance strategies.
Fig. 2. Trapezoidal fuzzy numbers. 9
Linguistic terms
Abbreviation
Trapezoidal fuzzy numbers
Ineffective Slightly ineffective Moderate effective Highly effective Extremely effective
I S M H E
(0.0, (0.2, (0.4, (0.6, (0.8,
0.1, 0.3, 0.5, 0.7, 0.9,
0.2, 0.4, 0.6, 0.8, 1.0,
0.3) 0.5) 0.7) 0.9) 1.0)
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
normalisation. The normalisation expressions that are presented in Equations (16)–(18) are also considered in the implementation of WASPAS for the current study. In order to determine the ranks of alternative, the normalised values are combined by considering the weights of each criterion [112]. The combination process is done from two perspectives. The first perspective deals with the determination of weighted additive values of the alternatives (Equation (22)), while the estimation of the weighted product values of the alternatives represents the second perspective (Equation (23)). n
Qi1 =
x ij wij
(22)
j=1 n
Qi2 =
Fig. 3. Membership functions for system requirements.
w
xij ij
(23)
j =1
To complete WASPAS operations, a control parameter for the contributions of either the weighted sum values or weighted product values is considered. The value of the control parameters lies between 0 and 1. The WASPAS value of an alternative is obtained through a linear combination of the weighted sum and weighted product values (Equation (24)). The most suitable alternative for a problem is taken as the alternative with the highest WASPAS value [111].
Qi = Qi1 + (1
) Qi2
(24)
where λ denotes contribution factor, and it its range is between 0 and 1. When λ = 0, WASPAS reduces to weighted product method. And when λ = 1, WASPAS method reduces to weighted sum method [113]. 4. Case study and contributions 4.1. Case examination Fig. 4. System requirements, common range and design requirements for the sustainability sub-criteria [108].
The choice of maintenance policies for buildings offers substantial chances to attain sustainable growth for public facilities in developing countries [2]. These chances ought to be periodically assessed since they are tools for progress in the built environment segment of national economy. For instance, the overwhelming value of CO2 emissions in the sector, representing the built environment (i.e., 47%) may be successfully curtailed if sustainable practices in public facilities and other buildings could be pursued [67]. Furthermore, this value is subject to the fluctuations of influencing variables such as the experience and competence of the manager in controlling these emissions and the government policies, which are uncertain in nature. Despite the understanding that some research has been carried out to appraise the sustainable form of public facilities in developing countries, substantial rigorous efforts have been largely limited [114,115]. Nonetheless, public facilities are often associated with several conflicting values located under the domain of economic, social and environmental perspectives [7]. The principal aim of the case study is to reveal the procedure through which choices among four maintenance strategies (corrective, condition-based, predictive and preventive) could be made. The situation warrants gathering experts' opinions in a scientific manner, through questionnaire distribution. The questionnaire information guides in the development of linguistic responses arising from the interaction with the experts and importance rating of the criteria will be obtained. The aggregation of the experts’ opinions is made while fuzzy mathematics offers guidance on the estimation of the sub-criteria weights. Comparative examination is then embarked upon to place each criterion of sustainability in its proper place using linguistic terms. The system range and common areas with respect to the maintenance strategies are evaluated and a decision on choice of the best maintenance strategies is given. To fully appreciate the decisions taken, briefs on the various maintenance strategies are given.
entropy weighting method, analytical hierarchy process, and analytical network process).
x i = x¯i wi
(19)
From the weighted normalised decision matrix, the optimality function of each alternative is determined (Equation (20)). The ideal solution (S0) for a solution process is used to determine the utility degree of the alternatives based on the results from Equation (21). The most suitable alternative for a problem is taken as the alternative that has the utility degree value (Equation (20)). m
Si =
xi i=1
ki =
Si S0
(20) (21)
i
where S and ki denotes the overall performance index and utility degree of alternative i, respectively. 3.4. WASPAS method The use of WASPAS methodology as a decision support tool in maintenance systems has been adopted in literature [111]. This study uses the final outputs from the FAD principles and ARAS method to determine the ranks of the different maintenance strategies. WASPAS method is a multi-criteria method for ranks determination that considered the weights of the criteria (i.e., information content and utility degree). Like other multi-criteria decision-making tools (e.g. TOPSIS and ARAS), the initial process of WASPAS implementation is data 10
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 7 Design range of the sustainability sub-criteria. Sub-criteria
Design range
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
0.6, 0.4, 0.4, 0.4, 0.6, 0.6, 0.4, 0.6, 0.6, 0.6, 0.4, 0.6, 0.6, 0.6, 0.6, 0.6,
0.7, 0.5, 0.5, 0.5, 0.7, 0.7, 0.5, 0.7, 0.7, 0.7, 0.5, 0.7, 0.7, 0.7, 0.7, 0.7,
0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6, 0.8, 0.8, 0.8, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8,
Table 9 Aggregated fuzzy number and crisp values for the sustainability sub-criteria comparative importance in linguistic terms. 0.9 0.7 0.7 0.7 0.9 0.9 0.7 0.9 0.9 0.9 0.7 0.9 0.9 0.9 0.9 0.9
Corrective maintenance with respect to public buildings is any maintenance carried out to return the building facility to the usual working mode. It includes the diverse building services to public buildings: supply and distribution of electrical energy in the building, activities of detection, protection and safety with respect to fire in public buildings. Others are the installation of controls for public buildings and the issue of escalator and lift corrective maintenance activities. Corrective maintenance permits those saddled with the building reliability task to direct attention to other activities in the building services domain pending a situation that gives rise to the service breakdown. Condition-based building maintenance is the maintenance carried out as needs arise [116]. It receives warnings of a forthcoming failure with the aim of spending the building maintenance budget on activities that matter. The benefits of condition-based maintenance when weighed against preventive maintenance include enhanced system reliability and drastically reduced costs. The predictive maintenance for buildings is a cheap, less labourdemanding strategy of public building maintenance that manages a large pool of accomplished data and forecast the needs of maintenance as well as incoming building failures in a little period [117]. The significant advantages of an adequately implemented building preventive maintenance plan include minimised downtime of service equipment (for instance escalators and lifts), and the reduction of the amount of substantial repairs. Other advantages include preservation of buildings and growth in the life-span of buildings. The current research advances a structure to appraise public facility sustainability to aid government in maintenance decisions in the populated country, Nigeria. This structure contains important elements of
D1
D2
D3
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
– H H M – M H H – E M M – M H H
– H M H – H H H – H M M – H M M
– M F M – M F M – H H H – H H H
Fuzzy numbers
Crisp values
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
– 0.433, 0.633, 0.833 0.300, 0.500, 0.700 0.367, 0.567, 0.767 – 0.367, 0.567, 0.767 0.367, 0.567, 0.767 0.433,0.633,0.833 – 0.567, 0.767, 0.933 0.367, 0.567, 0.767 0.367, 0.567, 0.767 – 0.433,0.633,0.833 0.433,0.633, 0.833 0.433, 0.633, 0.833
– 0.633 0.500 0.567 – 0.567 0.567 0.633 – 0.761 0.567 0.567 – 0.633 0.633 0.633
Table 10 SWARA results for the sustainability sub-criteria. Sub-criteria x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
s1j 0.633 0.500 0.567 s2j 0.567 0.567 0.633 s3j 0.761 0.567 0.567 s4j 0.633 0.633 0.633
K1j
q1j
W1j
1.000 1.633 1.500 1.567 K2j 1.000 1.567 1.567 1.633 K3j 1.000 1.761 1.567 1.567 K4j 1.000 1.633 1.633 1.633
1.000 0.612 0.408 0.261 q2j 1.000 0.638 0.407 0.249 q3j 1.000 0.568 0.362 0.231 q4j 1.000 0.612 0.375 0.229
0.438 0.268 0.179 0.114 W2j 0.436 0.278 0.178 0.109 W3j 0.463 0.263 0.168 0.107 W4j 0.451 0.276 0.169 0.104
environmental, economic, social and technical frameworks [118]. The applicability of the proposed framework in the current study was verified by collecting information from an agency that is in-charge of monitoring public buildings in Sango-Ota, Nigeria (Latitude: 7° 56′ 59.99″ N, Longitude: 4° 46′ 59.99″ E). Information was obtained using a well-structured questionnaire that captured relevant information that is required. In this case study, three senior staff (D1, D2 and D3) in the agency was asked to fill the questionnaire and information on the design requirements were also discussed with these experts (Table 7). The values in Table 7 are based on the experts’ opinion of the expected design requirements for their system. Thus, different experts can assign different design requirements for these values. The first part of the questionnaire was used to obtain linguistic responses from the experts on the importance of the sub-criteria (Table 8). Based the information in Table 8, Equations (6)–(9) are used to aggregate the experts' responses (Table 9). Using Equations (3)–(5), the fuzzy numbers in Table 9 are used to determine the weights for the sub-criteria and presented in Table 10. The weights for the sustainability criteria were determined based on the linguistic responses of the experts (Table 11) that were converted to aggregated fuzzy numbers (Table 12); this was achieved using Equations (6)–(9). However, Equations (3)–(5) are used to calculate the actual weights of the sustainability criteria (Table 13). The section part of the questionnaire was used to capture
Table 8 Comparative importance of the sustainability sub-criteria in linguistic terms. Sub-criteria
Sub-criteria
11
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
showed that the most suitable maintenance strategy in terms of economic, environmental and technical criteria is corrective maintenance strategy. Apart from social criterion, preventive maintenance strategy is considered as the least suitable maintenance strategy for building maintenance (Table 19). Based on the results in Table 19, FAD method ranks the maintenance strategies as S4 > S3 > S2 > S1 with respect to the economic, environmental and technical issues in sustainability. Thus, the maintenance strategies, from the most suitable to the least suitable, are ranked as corrective (S4) condition-based (S3), predictive (S2) and preventive (S1) maintenance strategies. During the application of the ARAS method, the information in Table 14 is considered. First, the normalised values of the aggregated responses are determined. Given that the questionnaire used in this study was designed in a way that seeks the maximum values of the criteria - for example, when using preventive maintenance strategy to maintain a building, how effective is this strategy to the waste reduction? - The bigger-the-better is considered. Thus, Table 20 presents the normalised decision matrix for the current problem (see Table 21). Fig. 5 shows the utilities of the maintenance strategies with respect to different criteria. Recall that the most important alternative, which for this study is maintenance strategy, is one with the highest utility value. Bearing this in mind, Fig. 5 shows that in terms of environmental and social criteria, the most and least suitable maintenance strategies are the condition-based (S3) and preventive (S1) maintenance strategies, respectively. On the other hand, condition-based and corrective maintenance (S4) strategies are the most and least suitable maintenance strategies, respectively. Furthermore, the ARAS results showed that most and least suitable maintenance strategies are corrective and preventive maintenance strategies, respectively (Fig. 5). The hybrid selection for the maintenance selection problems is carried out using the outputs from the FAD (information contents) and ARAS (utility degrees) methods (Table 22). In order to implement WASPAS methods for this problem, the importance of the solutions from the FAD and ARAS methods are considered equal (see Table 23). Since a FAD method ranks alternative based on the smaller-thebetter, Equation (17) were used to normalise the information in Table 22. The normalised utility degrees in Table 24 are obtained using the bigger-the-better approach, Equation (18). The WASPAS outputs in Table 25 were obtained when λ = 0.5. Based on the results obtained using the FAD method, the most suitable maintenance strategy for public buildings maintenance for the case study is corrective maintenance strategy. The FAD results showed that preventive maintenance strategy is the least suitable maintenance strategy for the case study. The ARAS results showed that predictive maintenance strategy is the most suitable strategy for the case study, while corrective maintenance strategy is the least suitable strategy (Fig. 6). In terms of weighted additive method, the most suitable strategy is S1, and S3 is the most suitable strategy in terms of weighted product method (Table 25). Based on the aggregated criteria and the solution methods, it can be inferred that the most suitable order for the case study maintenance strategies is S2 > S1 > S3 > S4.
Table 11 Comparative importance of the sustainability criteria in linguistic terms. Criteria
D1
D2
D3
x1 x2 x3 x4
– M M M
– H H H
– H M H
Table 12 Aggregated fuzzy number and crisp values for the sustainability criteria comparative importance in linguistic terms. Criteria
Fuzzy numbers
Crisp values
x1 x2 x3 x4
– 0.433, 0.633, 0.833 0.367, 0.567, 0.767 0.433, 0.633, 0.833
– 0.633 0.567 0.633
Table 13 SWARA results for the sustainability criteria. Criteria
Si
ki
qi
wi
x1 x2 x3 x4
0.633 0.567 0.633
1.000 1.633 1.567 1.633
1.000 0.612 0.391 0.239
0.446 0.273 0.174 0.107
information on the effectiveness of using a particular maintenance strategy for public buildings maintenance in the case study area. The information that was obtained is in linguistic form (Table 14). Based on the information in Table 14, the aggregated values of the fuzzy numbers for the effectiveness of the maintenance strategies that were considered were determined (Table 15). The aggregated fuzzy numbers for the sub-criteria were used to determine the system areas of the maintenance strategies effectiveness for public building maintenance (Table 16). The common areas of the maintenance strategies were determined using the information in Tables 7 and 15. By considering Equation (12), the information contents of the subcriteria for the maintenance strategies were determined (Table 17), while Equation (14) was used to compute the weighted information content (Table 18). In terms of the total weighted information contents, the FAD results Table 14 System range in linguistic terms.
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
S1 D1 E E H H E H H E E E H H E H E H
D2 H H H H E E E E E E E E E H H E
D3 M M M M M M M M M M M M M M M M
S2 D1 E E E H H H H E H E H H E E H H
D2 H H H H E E E E E E H E H H H E
D3 H H M M M M M M M M M M M M M M
S3 D1 E E E E E E E E E H H H H H H H
D2 H H E E E H H H H H M H M H H M
D3 M M M M M M M M M M M M M M M M
S4 D1 E H H E H E H E E E E H H H E H
D2 H E E E E E H H M H M H M M M H
D3 S S S I I M M M M M M M M M M M
4.2. Research contributions This study re-looks maintenance selection as it relates to public buildings from a new perspective. Until now, the principal perspectives have been the risk-rooted and life-cycle lens [119]. Specifically, the following contributions are made in the research. First, this study offers a framework to examine the issue of maintenance selection in public buildings. On every occasion that the issue of maintenance of public buildings is debated, there is trend to encourage critical reflection on building risks and assume a narrow view towards maintenance discussions. This is a widespread experience in debates on maintenance of public buildings. While this study was prepared, there was an attempt to modify this insight on maintenance of public buildings. The issue of multi-criteria decision making is revealed, tested and confirmed as a 12
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 15 Aggregated fuzzy numbers for the maintenance strategies. S1 x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
S2
0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
0.700, 0.700, 0.633, 0.633, 0.767, 0.700, 0.700, 0.767, 0.767, 0.767, 0.700, 0.700, 0.767, 0.633, 0.700, 0.700,
0.800, 0.800, 0.733, 0.733, 0.867, 0.800, 0.800, 0.867, 0.867, 0.867, 0.800, 0.800, 0.867, 0.733, 0.800, 0.800,
1.000 1.000 0.900 0.900 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.900 1.000 1.000
S3
0.600, 0.600, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
0.767, 0.767, 0.700, 0.633, 0.700, 0.700, 0.700, 0.767, 0.700, 0.767, 0.633, 0.700, 0.700, 0.700, 0.633, 0.700,
0.867, 0.867, 0.800, 0.733, 0.800, 0.800, 0.800, 0.867, 0.800, 0.867, 0.733, 0.800, 0.800, 0.800, 0.733, 0.800,
1.000 1.000 1.000 0.900 1.000 1.000 1.000 1.000 1.000 1.000 0.900 1.000 1.000 1.000 0.900 1.000
Table 16 System and common areas of the maintenance strategies. System area Sub-criteria x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
S1 0.805 0.805 0.723 0.723 0.873 0.805 0.805 0.873 0.873 0.873 0.805 0.805 0.873 0.723 0.805 0.805
S2 0.807 0.807 0.719 0.663 0.719 0.719 0.719 0.745 0.719 0.745 0.663 0.719 0.719 0.719 0.663 0.719
S4 0.631 0.631 0.631 0.567 1.390 0.745 0.550 0.719 0.694 0.719 0.694 0.663 0.637 0.637 0.694 0.663
S1 0.090 0.090 0.104 0.104 0.220 0.250 0.090 0.220 0.220 0.220 0.090 0.250 0.220 0.289 0.250 0.250
S2 0.014 0.014 0.090 0.104 0.250 0.250 0.090 0.220 0.250 0.220 0.104 0.250 0.250 0.250 0.289 0.250
0.567, 0.567, 0.767, 0.767, 0.767, 0.700, 0.700, 0.700, 0.700, 0.633, 0.567, 0.633, 0.567, 0.633, 0.633, 0.567,
0.667, 0.667, 0.867, 0.867, 0.867, 0.800, 0.800, 0.800, 0.800, 0.733, 0.667, 0.733, 0.667, 0.733, 0.733, 0.667,
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.900 0.900 0.900 0.900 0.900 0.900 0.900
0.200, 0.200, 0.200, 0.000, 0.600, 0.400, 0.200, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
0.633, 0.633, 0.633, 0.633, 0.567, 0.767, 0.500, 0.700, 0.633, 0.700, 0.633, 0.633, 0.567, 0.567, 0.633, 0.633,
0.733, 0.733, 0.733, 0.733, 0.667, 0.867, 0.600, 0.800, 0.733, 0.800, 0.733, 0.733, 0.667, 0.667, 0.733, 0.733,
1.000 1.000 1.000 1.000 1.000 1.000 0.900 1.000 1.000 1.000 1.000 0.900 0.900 0.900 1.000 0.900
Table 18 Weighted sub-criteria information index for the maintenance strategies.
Common area S3 0.325 0.325 0.659 0.659 0.659 0.690 0.690 0.690 0.690 0.697 0.737 0.697 0.737 0.697 0.697 0.737
0.200, 0.200, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
S4
S3 0.220 0.220 0.079 0.079 0.220 0.250 0.090 0.250 0.250 0.289 0.123 0.289 0.341 0.289 0.289 0.341
S4 0.197 0.197 0.197 0.294 0.269 0.220 0.250 0.250 0.289 0.250 0.104 0.289 0.341 0.341 0.289 0.289
Sub-criteria
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
1.384 0.847 0.501 0.319 0.866 0.469 0.563 0.216 0.919 0.522 0.531 0.180 0.895 0.366 0.285 0.175
2.579 1.578 0.537 0.305 0.665 0.424 0.534 0.191 0.706 0.462 0.449 0.163 0.687 0.421 0.203 0.159
0.244 0.149 0.546 0.348 0.688 0.407 0.523 0.160 0.679 0.334 0.435 0.136 0.502 0.351 0.215 0.116
0.734 0.449 0.300 0.108 0.624 0.488 0.202 0.166 0.585 0.401 0.460 0.128 0.408 0.249 0.214 0.125
Table 19 Total weighted information contents and ranking order of the strategies.
Table 17 Sub-criteria information index for the maintenance strategies. Sub-criteria
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
3.161 3.161 2.799 2.799 1.985 1.687 3.161 1.985 1.985 1.985 3.161 1.687 1.985 1.325 1.687 1.687
5.888 5.888 2.998 2.673 1.524 1.524 2.998 1.756 1.524 1.756 2.673 1.524 1.524 1.524 1.199 1.524
0.558 0.558 3.053 3.053 1.579 1.466 2.940 1.466 1.466 1.271 2.587 1.271 1.113 1.271 1.271 1.113
1.675 1.675 1.675 0.946 2.367 1.756 1.138 1.524 1.264 1.524 2.738 1.199 0.904 0.904 1.264 1.199
Criteria x1 x2 x3 x4
Weighted information content S1 S2 S3 3.051 4.999 1.287 2.114 1.814 1.778 2.152 1.780 1.584 1.721 1.470 1.184
S4 1.591 1.480 1.574 0.996
Ranking order S3 > S4 > S2 > S1 S4 > S3 > S2 > S1 S4 > S3 > S2 > S1 S4 > S3 > S2 > S1
Table 20 Normalised decision matrix for ARAS method.
unique contribution to the selection of maintenance strategy for public buildings. In the authors’ opinion, the new approach of multi-criteria decision making offers a novel reference point to debate the issue of maintenance selection. Second, it offers a useful basis for public buildings to build up maintenance strategies converging on sustainability issued through the introduction of sustainability criteria in determining the most suitable maintenance strategy for public buildings. A major challenge facing 13
Sub-criterion
S0
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 X42 X43 X44
0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200
0.226 0.226 0.194 0.200 0.188 0.203 0.214 0.217 0.220 0.216 0.206 0.207 0.220 0.191 0.209 0.205
0.226 0.226 0.193 0.184 0.155 0.182 0.191 0.185 0.181 0.185 0.170 0.185 0.181 0.190 0.172 0.183
0.091 0.091 0.177 0.182 0.142 0.174 0.183 0.171 0.174 0.173 0.189 0.179 0.186 0.185 0.181 0.188
0.177 0.177 0.169 0.157 0.300 0.188 0.146 0.179 0.175 0.178 0.178 0.171 0.161 0.169 0.180 0.169
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 21 Weighted normalised matrix for ARAS method. Sub-criterion
S0
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
0.088 0.088 0.088 0.088 0.087 0.087 0.087 0.087 0.093 0.093 0.093 0.093 0.055 0.055 0.055 0.055
0.099 0.060 0.035 0.023 0.082 0.057 0.038 0.024 0.102 0.057 0.035 0.022 0.099 0.053 0.035 0.021
0.099 0.061 0.034 0.021 0.068 0.050 0.034 0.020 0.084 0.049 0.029 0.020 0.082 0.053 0.029 0.019
0.040 0.024 0.032 0.021 0.062 0.048 0.033 0.019 0.080 0.045 0.032 0.019 0.084 0.051 0.031 0.020
0.077 0.047 0.030 0.018 0.131 0.052 0.026 0.019 0.081 0.047 0.030 0.018 0.072 0.047 0.030 0.018
Table 22 The maintenance strategies ranking order results for the ARAS method. Criteria
Ranking order
x1 x2 x3 x4
S3 > S4 > S2 > S1 S3 > S2 > S1 > S4 S3 > S4 > S2 > S1 S4 > S2 > S3 > S1
Table 23 Final outputs of the selection methods for the different maintenance strategies. Variable
S1
S2
S3
S4
Ranking order
Information content Utility degree
9.038 0.835
10.063 1.111
5.833 0.749
5.641 0.743
S1 > S3 > S1 > S2 S2 > S1 > S3 > S4
Table 24 Normalised decision matrix of the final outputs of the selection methods for the different maintenance strategies.
public buildings nowadays is sustainability. This concern is somewhat complex and crucial to the continued existence of buildings and their environment. It is understood that public buildings contribute carbondioxide emission as well as greenhouse effects that intimidate humans, their health and natural resources. Concern about sustainable public building is as well linked to buildings that are energy efficient, environmentally-healthy, considerate for natural surroundings and easy for office accommodation. So, in this research, an important contribution is to take a new position in sustainability of buildings with respect to the selection of maintenance strategy that attempts to reduce the harmful environmental effects of public buildings through improving efficiency and control of material usage, energy and spaces for development. Thus, in the viewpoint of the authors, selecting an appropriate maintenance strategy for public buildings would require a rigorous treatment of sustainability criteria. Third, it offers a structure to build up the principles of FAD to select maintenance strategy for public buildings. A most significant contribution of this study is that it brought on the need to capture the uncertainty elements in maintenance. Thus, a paradigm shift in maintenance evaluation and strategic analysis of schedules is the incorporation of fuzzy axiomatic design that has enhanced features over fuzzy sets alone. The issue that a substantial number of researchers endorse the application of fuzzy mathematics of maintenance analysis is in itself a signal to the promising nature of fuzzy axiomatic design in public building maintenance. Fourth, the unique adoption of SWARA method to determine the relative importance of sub-criteria of sustainability criterion and the use of WASPAS method as a decision tool in an outstanding manner to combine information and utility degree are important contributions in the public building maintenance literature.
Variable
S1
S2
S3
S4
Information content Utility degree
0.198 0.243
0.178 0.323
0.307 0.218
0.317 0.216
Table 25 Analysis of the maintenance strategies using WASPAS. S1
S2
S3
S4
Ranking order
Qi1
0.481
0.392
0.153
0.103
0.491
0.756
0.943
0.822
S1 > S2 > S3 > S4
Qi2 Qi
0.726
0.770
0.624
0.514
S3 > S4 > S2 > S1 S2 > S1 > S3 > S4
4.3. Research implications The research tackles the call for building maintenance investigations from the quantitative perceptions in the extant literature [119]. At first, the research employs the fuzzy axiomatic design to capture the uncertainty elements in building maintenance schedules. In addition, occasionally, quantitative analysis using multi-objective optimisation results in an infeasible option that is challenging to manage. These issues were not previously discussed in the building maintenance strategy literature. In fact, rather than focusing on these dimensions of problems in the literature, the risk analysis of building maintenance and how they impact on the life-cycle of the buildings have been the major focus of previous research [119]. In addition, previous literature has relegated sustainability in building maintenance strategy choice to the
Fig. 5. Utility degrees of the maintenance strategies. 14
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Fig. 6. Ranking of the maintenance strategies using different methods.
maintenance strategies ranking were different when considering the combined sustainability criteria values. In terms of economic sustainability criterion, both methods identified corrective maintenance strategy as the most suitable strategy for public buildings maintenance. Furthermore, the combination of the FAD and ARAS methods final outputs using WASPAS identified corrective maintenance strategy as the most suitable strategy for public buildings maintenance, while preventive maintenance strategy as the least suitable strategy for public building in the case study area.
background. It is now clear that the emergence of a sustainable-focused model and a study on building maintenance strategic choice with fuzzy axiomatic design at its core of analysis add new knowledge and insight and richness to building maintenance selection in some ways. By considering sustainability, the research highlights how the choice of strategy could be affected by social and environmental criteria for the most informed green decisions. The current research also shows that the most acceptable maintenance strategy selection is guide by considering every details of imprecision and certainties that could come from both the employees and the employers and even the government in the society. These issues could have significant implications for selection as well as research in maintenance.
5.2. Limitations of the study and future directions By considering the nature of this research, the outcomes are most related and useful for building managers in public buildings such as hospitals, research institutions, universities and poly-techniques. Nonetheless, the study may be considered restricted in light of methods and the interactions among criteria. To avoid this problem, one must select the criteria and related sub-criteria to be completely independent (which is almost infeasible in real-life). Thus, the Choquest method can be investigated to consider these interactions among criteria in future research. Considering this research, the outcomes are largely relevant to building managers in public institutions, particularly, agencies of governments responsible for building monitoring in Nigeria. Though this is consistent with the objective of this research, it as well restricts the relevance of the research outcomes to options in perception of building maintenance. It is likely that some sustainability criteria could have escaped our interest. A case in point is research institutes that are engineering focused, producing prototype factories as their research outcomes. Prototypes and incubation factories are small manufacturing plants with different set of criteria on sustainability from those applied in this research. So, additional probing of the sustainability issue would prove a rewarding research endeavour, especially considering the paucity of studies in this domain of knowledge at present. Apart from the building maintenance manager obtaining advantages from the proposed structure, the scheme of the maintenance policy presented can be further adjusted to usage as a check programme for director generals of government agencies, from which building manager's performance attributes could be checked. The obtained data could as well serve as audit evidence for expenditures during the annual organizational audit programmes. The gathered field-observation functional data may as well be important to examine the association among the structure.
5. Conclusions, limitations and future studies 5.1. Conclusions This research advances a multi-criteria maintenance scheme, which permits building maintenance managers to be informed about the various factors associated with building maintenance policy decision with particular emphasis on the scenario surrounding public building maintenance. An approach is adopted to capture information about sustainability criteria while carefully spreading considerations to social, environmental, economic and technical aspects. Equipped by means of the advanced knowledge, the building maintenance managers can function in the choice of the most appropriate maintenance strategy and be informed of the right combination of factors that result in the optimal decision making for public facilities. The complete design of the presented framework from the perspective of field-observed functional data, a questionnaire is built up and administer in a system of public facilities located in Ota, Ogun state, Nigeria. Furthermore, the on-site data experimentation of the field-observed data was carried out to establish and validate the structural framework presented. The nature of the structural design of the presented representation would permit building maintenance managers to adjust the parameters of the system in such a manner to favours the effectiveness in the working of the system. This study extends the applicability of FAD principles to public buildings maintenance strategy selection using economic, environmental, social and technical sustainability criteria. Furthermore, the extension of SWARA for determining the relative importance of public building sustainability sub-criteria was also reported in this study. The applicability of the proposed framework that combined FAD and SWARA was verified using information obtained from a developing country. Information from building experts in an organisation that is incharge of public buildings monitoring was used during the implementation of the proposed framework. The results obtained were compared with ARAS method results. It was observed that the FAD and ARAS methods results for the
Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 15
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Appendix A. Supplementary data [24]
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jobe.2019.100753.
[25]
References
[26]
[1] E.E. Chidi, M. Shamsudeen, F.T. Oladipupo, A.J. Owolabi, An assessment of maintenance culture on public building in Nigeria (A case study of Osun State), IOSR J. Mech. Civ. Eng. 14 (5) (2017) 53–57. [2] O.O. Ugwu, C.C. Okafor, C.U. Nwoji, Assessment of building maintenance in Nigerian university: a case study of University of Nigeria, Nsukka, Niger. J. Technol. 37 (1) (2018) 44–52 https://doi.org/10.4314/njt.v37i1.6. [3] H. Krstic, S. Marenjak, Analysis of buildings operation and maintenance costs, Gradevinar 64 (4) (2012) 293–303. [4] J.-R. Kim, Y.-H. Jung, J.-H. Son, A study on reliability analysis model of the repair and replacement cycle of a building which utilizes Monte Carlo simulation, J. Korea Inst. Build. Constr. 10 (2) (2010) 41–50 https://doi.org/10.5345/JKIC. 2010.10.2.041. [5] C. de la Cruz-lovera, A.-J., Perea-Morewo, J.-L. de la Cruz-Fernandez, J.A. AlvarezBermejo, F. Manzano-Agugliaro, Worldwide research on energy efficiency and sustainability in public buildings, Sustain. Times 9 (2017) 1–20 https://doi.org/ 10.3390/su9081294. [6] H. Son, C. Kim, Evolutionary many-objective optimization for retrofit planning in public buildings: a comparative study, J. Clean. Prod. 190 (2018) 403–410 https://doi.org/10.1016/j.jclepro.2018.04.102. [7] O.A. Adenuga, Maintenance management practices in public hospital built environment: Nigerian case study, J. Sustain. Dev. Afr. 14 (1) (2010) 185–201. [8] M. Doumpos, J.R. Figueira, A multicriteria outranking approach for modelling corporate credit ratings: an application of the Electre Tri-nC method, Omega 82 (2019) 166–180 https://doi.org/10.1016/j.omega.2018.01.003. [9] M. Doumpos, Y. Marinakis, M. Marinaki, C. Zopounidis, An evolutionary approach to construction of outranking models for multicriteria classification: the case of the ELECTRE TRI method, Eur. J. Oper. Res. 199 (2) (2009) 496–505 https://doi.org/ 10.1016/j.ejor.2008.11.035. [10] P. Haurant, P. Oberti, M. Muselli, Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: a real case study using the ELECTRE outranking framework, Energy Policy 39 (2) (2011) 676–688 https:// doi.org/10.1016/j.enpol.2010.10.040. [11] F. Lolli, E. Balugani, A. Ishizak, R. Gamberini, M.A. Butturi, S. Marinello, B. Rimini, On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application, Expert Syst. Appl. 120 (2019) 217–227 https:// doi.org/10.1016/j.eswa.2018.11.030. [12] D.E. Ighravwe, S.A. Oke, A multi-criteria multi-hierarchical framework for ranking maintenance sustainability strategies based on PROMETHEE and fuzzy entropy weighting methods, J. Build. Pathol. Rehabil. 2 (9) (2017) 1–18 https://doi.org/ 10.1007/s41024-017-0028-7. [13] G. Polatidis, D.A. Haralamboponlos, G. Munda, R. Vreeker, Selection an appropriate multi-criteria decision analysis technique for renewable energy planning, Energy Sources, Part B 1 (2006) 181–193 https://doi.org/10.1080/ 009083190881607. [14] N. Khalil, S.N. Kamaruzzaman, M.R. Baharum, Ranking the indicators of building performance and the users' risk via analytical hierarchy process (AHP): case of Malaysia, Ecol. Indicat. 71 (2016) 567–576 https://doi.org/10.1016/j.ecolind. 2016.07.032. [15] V. Kutut, E.K. Zavadskas, M. Lazauskas, Assessment of priority alternatives for preservation of historic buildings using model based on ARAS and AHP methods, Arch. Civ. Mech. Eng. 14 (2) (2014) 287–294 https://doi.org/10.1016/j.acme. 2013.10.007. [16] J.K.W. Wong, H. Li, Application of the analytic hierarchy process (AHP) in multicriteria analysis of the selection of intelligent building systems, Build. Environ. 43 (1) (2008) 108–125 https://doi.org/10.1016/j.buildenv.2006.11.019. [17] P. Shen, W. Braham, Y. Yi, E. Eaton, Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit, At. Energ. 172 (2019) 892–912 https://doi.org/10.1016/j.energy.2019. 01.164. [18] H. Golpîra, S.A. Khan, A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty, At. Energ. 170 (2019) 1113–1129 https://doi.org/10.1016/j. energy.2018.12.185. [19] S. Angilella, P. Catalfo, S. Corrente, A. Giarlotta, M. Rizzo, Robust sustainable development assessment with composite indices aggregating interacting dimensions: the hierarchical-SMAA-Choquet integral approach, Knowl. Base Syst. 158 (2018) 136–153 https://doi.org/10.1016/j.knosys.2018.05.041. [20] W. Zhang, Y. Ju, L.F.A.M. Gomes, The SMAA-TODIM approach: modeling of preferences and a robustness analysis framework, Comput. Ind. Eng. 114 (2017) 130–141 https://doi.org/10.1016/j.cie.2017.10.006. [21] S. Corrente, J.R. Figueira, S. Greco, The SMAA-PROMETHEE method, Eur. J. Oper. Res. 239 (2) (2014) 514–522 https://doi.org/10.1016/j.ejor.2014.05.026. [22] S. Pokharel, M. Chandrashekar, A multiobjective approach to rural energy policy analysis, At. Energ. 23 (1998) 325–336 https://doi.org/10.1016/S0360-5442(97) 00103-5. [23] R. Ramanathan, L.S. Ganesh, Energy resource allocation incorporating qualitative and quantitative criteria: an integrated model using goal programming and AHP,
[27]
[28] [29] [30] [31] [32] [33] [34] [35]
[36] [37]
[38] [39] [40] [41] [42]
[43] [44] [45]
[46] [47] [48] [49] [50] [51] [52]
16
Soc. Econ. Plann. Sci. 29 (1995) 197–218 https://doi.org/10.1016/00380121(95)00013-C. M.F. Khamidi, O.A. Lateef, A. Idris, Building maintenance: a path towards sustainability, Malays. Constr. Res. J. 7 (2010). X. Zhang, Markov-based optimization model for building facilities management, J. Constr. Eng. Manag. 132 (11) (2006) 1203–1212 (2006)132:11(1203), https:// doi.org/10.1061/(ASCE)0733-9364. R. Ruparathna, K. Hewage, R. Sadiq, Economic evaluation of building energy retrofits: a fuzzy based approach, Energy Build. 139 (2017) 395–406 https://doi. org/10.1016/j.enbuild.2017.01.031. Y. Fan, X. Xia, A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with root top PV system installation and maintenance, Appl. Energy 189 (2017) (2017) 327–335 https://doi.org/10.1016/j.apenergy. 2016.12.077. E. Chan, Building maintenance strategy: a sustainable refurbishment perspective, Univers. J. Manag. 2 (1) (2014) 19–25 https://doi.org/10.13189/ujm.2014. 020103. H.Y.L. Hackman, D. Scott, Development of a conceptual framework for the study of building maintenance operation processes in the context of facility management, Surv. Built. Environ. 19 (1) (2008) 81–101. B.A. Jolaoso, N.A. Musa, O.A. Oriola, Appraisal of the maintenance of public residential estates in Ogun state: case study of Ibara housing estate, Abeokuta, J. Emerg. Trends Econ. Manag. Sci. 3 (5) (2012) 509–516. M.H. Aghdaie, S.H. Zolfani, E.K. Zavadskas, Decision making in machine tool selection: an integrated approach with SWARA and COPRAS-G methods, Inzinerine Ekonomika-Eng. Econ. 24 (1) (2013) 5–17. D. Karabašević, D. Stanujkić, S. Urošević, M. Maksimović, An approach to personnel selection based on SWARA and WASPAS methods, J. Econ. Manag. Inf. (2016) 1–11 https://doi.org/10.5937/bizinfo1601001k. M. Li, Extension of axiomatic design method for fuzzy linguistic multiple criteria group decision making with incomplete weight information, Math. Probl Eng. (2012) 1–17 https://doi.org/10.1155/2012/634326. M. Velasquez, P.T. Hester, An analysis of multi-criteria decision making methods, Int. J. Oper. Res. 10 (2) (2013) 56–66. V. Campos-Guzmán, M.S. García-Cáscales, N. Espinosa, A. Urbina, Life cycle analysis with multi-criteria decision making: a review of approaches for the sustainability evaluation of renewable energy technologies, Renew. Sustain. Energy Rev. 104 (2019) 343–366 https://doi.org/10.1016/j.rser.2019.01.031. H. Alabool, A. Kamil, N. Arshad, D. Alarabiat, Cloud service evaluation methodbased multi-criteria decision-making: a systematic literature review, J. Syst. Softw. 139 (2018) 161–188 https://doi.org/10.1016/j.jss.2018.01.038. T.L. Saaty, Relative measurement and its generalization in decision making: why pairwise comparisons are central in mathematics for the measurement of intangible factors. The analytic hierarchy/network process, Rev Royal Acad Exact, Phys. Nat. Sci. Ser. A: Math. 102 (2) (2008) 251–318. K.D. Goepel, Implementation of an online software tool for the analytic hierarchy process (AHP-OS), Int. J. Anal. Hierar. Proc. 10 (3) (2018) 469–487 https://doi. org/10.13033/ijahp.v10i3.590. J.P. Brans, Lingenierie de la decision: elaboration d’instrumentsd’aide a la decision La method PROMOTHEE, Press de l’universite Laval, 1982. J.P. Brans, P. Vincke, A preference ranking organization method: the PROMETHEE method for MCDM, Manag. Sci. 31 (6) (1985) 647–656. B. Delibasic, R. Ribeiro, Special issue on multi-criteria decision making approaches, Int. J. Decis. Support Syst. Technol. 7 (1) (2015) iv–v. B. Cheetham, P. Cuddihy, K. Goebel, Applications of soft CBR at general electric, in: S.K. Pal, T.S. Dillon, D.S. Yeung (Eds.), Soft Computing in Case Based Reasoning, Springer, London, 2001, https://doi.org/10.1007/978-1-4471-06876_15. J. Daengdej, D. Lukose, R. Murison, Using statistical models and case-based reasoning in claims prediction experience from a real-world problem, Knowl. Base Syst. 12 (5–6) (1999) 239–245 https://doi.org/10.1016/S0950-7051(99)00015-5. J.R.P. Mendes, C.K. Morooka, I.R. Guilherme, Case-based reasoning in offshore well design, J. Pet. Sci. Eng. 40 (1–2) (2003) 47–60 https://doi.org/10.1016/ S0920-4105(03)00083-4. Z. Zhang, D. Chen, Y. Feng, Z. Yuan, J. Han, A strategy for enhancing the operational agility of petroleum refinery plant using case based fuzzy reasoning method, Comput. Chem. Eng. 111 (2018) 27–36 https://doi.org/10.1016/j. compchemeng.2017.12.021. K. Zhao, X. Yu, A case based reasoning approach on supplier selection in petroleum enterprises, Expert Syst. Appl. 38 (6) (2011) 6839–6847 https://doi.org/10. 1016/j.eswa.2010.12.055. Z. Shi, H. Zhou, J. Wang, Applying case-based reasoning to engine oil design, Artif. Intell. Eng. 11 (2) (1997) 167–172 https://doi.org/10.1016/S0954-1810(96) 00029-5. S.L. Si, X.Y. You, H.C. Liu, DEMATEL technique: a systematic review of the stateof-the-art literature on methodologies and applications, Math. Probl Eng. (2018) 33 3696457 https://doi.org/10.1155/2018/3696457. I.E. Onyegiri, S.A. Oke, Applying decision trial and evaluation laboratory as a decision tool for effective safety management system in aviation transport, KKU Eng. J. 43 (4) (2016) 166–171. C.W. Churchman, R.L. Ackoff, An approximate measure of value, J. Oper. Res. Soc. Am. 2 (2) (1954) 172–187 https://doi.org/10.1287/opre.2.2.172. E. Triantaphyllou, Multi-criteria decision making methods, Multi- Criteria Decision Making Methods: and Applications, a State-Of-The-Art Survey, 186, Springer Science and Business Media, 2012. C.L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
[53] [54] [55] [56]
[57] [58] [59]
[60] [61]
[62]
[63] [64] [65] [66] [67]
[68] [69] [70] [71]
[72] [73] [74] [75] [76] [77] [78] [79] [80] [81]
Applications a State-Of-The-Art Survey vol. 186, Springer Science & Business Media, 2012. V. Podvezko, The comparative analysis of MCDA methods SAW and COPRAS, Inzinerine Ekonomika-Eng. Econ. 22 (2) (2011) 134–146. M.J. Skibniewski, L.C. Chao, Evaluation of advanced construction technology with AHP method, J. Constr. Eng. Manag. 118 (3) (1992) 577–593 (1992)118:3(577), https://doi.org/10.1061/(ASCE)0733-9364. V. Betlon, A comparison of the analytic hierarchy process and a simple multiattribute value function, Eur. J. Oper. Res. 26 (1) (1986) 7–21 https://doi.org/10. 1016/0377-2217(86)90155-4. D. Karabasevic, D. Stanujkic, S. Urosevic, M. Maksimovic, Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods, Acta Montan. Slovaca 20 (2) (2015) 116–124 https:// doi.org/10.3390/ams20020116. D. Stanujkic, B. Djordjevic, D. Karabasevic, Selection of candidates in the process of recruitment and selection of personnel based on the SWARA and ARAS methods, Quaestus Multidisc Res. J. (2015) 53–64. D. Karabasevic, E.K. Zavadskas, Z. Turskis, D. Stanujkic, The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties, Informatica 27 (1) (2016) 49–65. S.H. Zolfani, E.K. Zavadskas, Z. Turskis, Design of products with both international and local perspectives based on Yin-Yang balance theory and SWARA method, Ekonomska istraživanja-Economic Res. 26 (2) (2013) 153–166 https://doi.org/10. 1080/1331677X.2013.11517613. H.Z. Sarfaraz, M. Bahrami, Investment prioritizing in high tech industries based on SWARA-COPRAS approach, Technol. Econ. Dev. Econ. 20 (3) (2014) 534–553 https://doi.org/10.3846/20294913.2014.881435. A. Ruzgys, R. Volvaciovas, C. Ignatavicius, Z. Turskis, Integrated evaluation of external wall insulation in residential buildings using SWARA-TODIM MCDM method, J. Civ. Eng. Manag. 20 (2014) 103–110 https://doi.org/10.3846/ 13923730.2013.843585. M. Alimardani, S. Hashemkhani Zolfani, M.H. Aghdaie, J. Tamošaitiene, A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment, Technol. Econ. Dev. Econ. 19 (3) (2013) 533–548 https://doi.org/10. 3846/20294913.2013.814606. C.W.F. Yu, J.T. Kim, Building environmental assessment schemes for rating of IAQ in sustainable buildings, Indoor Built Environ. 20 (1) (2011) 5–15 https://doi.org/ 10.1177/1420326X10397780. P. Hanke, K.U. Werner, The second hand library building: sustainable thinking through recycling old building into new libraries, Int Fed. Libr. Assoc. Inst. 38 (1) (2012) 60–67. P. Yung, X. Wang, A 6D CAD Model for automatic assessment of building sustainability, Int. J. Adv. Robot. Syst. (2014) 10.5772158446. S. Bartolini, Building sustainability through greater happiness, Econ. Lab. Relat. Rev. 25 (4) (2014) 587–602. L.L. Hopkinson, J.A. Gwilliam, Driving education and sustainable development potential within professional curricula: built environment sustainability training and training development for professionals in Wales, Local Econ. 30 (3) (2015) 280–291. O. Oduyemi, M. Okoroh, Building performance modeling for sustainable building design, Int. J. Sustain. Built. Environ. 5 (2016) (2016) 461–469. M.O. Fadeyi, The role of building information modeling (BIM) in delivering the sustainable binding value, Int. J. Sustain. Built.Environ. 6 (2017) 711–722. R. Gupta, M. Kapsali, M. Gregg, Comparative building performance evaluation of a “sustainable” community centre and a public library building, Build. Serv. Eng. Technol. 38 (6) (2017) 691–710. H. Saldana-Marquez, J.M. Gomez–Soberon, S.P. Arredondo-Rea, D.C. GamezGarcia, R. Corral-Higuera, Sustainable social housing: the comparison of the Mexican funding program for housing solutions and building sustainability rating systems, Build. Environ. 133 (2018) 103–122. A.K. Shukla, K. Sudhakar, P. Baredar, R. Mamat, BIPV based sustainable building in South Asian countries, Sol. Energy 170 (2018) 1162–1170. A. Soliman, A. Mackay, A. Schmidt, B. Allan, S. Wary, Quantifying the geographic distribution of building coverage across the US for urban sustainability studies, Comput, Environ. Urban Syst. 71 (2018) 199–208. L.F. Fuentes-Cortes, A. Flones-Tlacua-huac, Integration of distributed generation technologies on sustainable buildings, Appl. Energy 224 (2018) 582–601. M. Kamali, K. Hewage, A.S. Milani, Life cycle sustainability performance assessment framework for residential modular building: aggregated sustainability indices, Build. Environ. 138 (2018) 21–41. A. Aslani, A. Bakhtiar, M.H. Akbarzadeh, Energy-efficiency technologies in the building envelope: life cycle and adaptation assessment, J. Build. Eng. 21 (2019) 55–63. S. Lidelöw, T. Örn, A. Luciani, A. Rizzo, Energy-efficiency measures for heritage buildings: a literature review, Sustain. Cities Soc. 45 (2019) 231–242. M.S.S. Danish, T. Senjyu, A. Ibrahimi, M. Ahmadi, A. Howlader, A managed framework for energy-efficient building, J. Build. Eng. 21 (2019) 120–128. F. Silvero, F. Rodrigues, S. Montelpare, E. Spacone, H. Varum, The path towards buildings energy efficiency in South American countries, Sustain. Cities Soc. 44 (2019) 646–665. F. Shadram, J. Mukkavaara, Exploring the effects of several energy efficiency measures on the embodied/operational energy trade-off: a case study of Swedish residential buildings, Energy Build. 183 (2019) 283–296. R. Moschetti, H. Brattebø, M. Sparrevik, Exploring the pathway from zero-energy to zero-emission building solutions: a case study of a Norwegian office building, Energy Build. 188–189 (2019) 84–97.
[82] M. Almeida, M. Ferreira, Ten questions concerning cost-effective energy and carbon emissions optimization in building renovation, Build. Environ. 143 (2018) 15–23. [83] A. Burlacu, G. Sosoi, R.Ș. Vizitiu, M. Bărbuță, A.A. Șerbănoiu, Energy efficient heat pipe heat exchanger for waste heat recovery in buildings, Procedia. Manuf 22 (2018) 714–721. [84] P.V. Sáez, J.S. Cruz Astorqui, M.D.R. Merino, M.D.M. Moyano, A.R. Sánchez, Estimation of construction and demolition waste in building energy efficiency retrofitting works of the vertical envelope, J. Clean. Prod. 172 (2018) 2978–2985. [85] E. Fuentes, L. Arce, J. Salom, A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis, Renew. Sustain. Energy Rev. 81 (Part 1) (2018) 1530–1547. [86] A.K. Marinoski, R.F. Rupp, E. Ghisi, Environmental benefit analysis of strategies for potable water savings in residential buildings, J. Environ. Manag. 206 (2018) 28–39. [87] H.B. Gunay, W. Shen, G. Newsham, Data analytics to improve building performance: a critical review, Autom. ConStruct. 97 (2019) 96–109. [88] L. Pham, E. Palaneeswaran, R. Stewart, Knowing maintenance vulnerabilities to enhance building resilience, Procedia Eng. 212 (2018) 1273–1278. [89] M. Cajias, F. Fuerst, S. Bienert, Tearing down the information barrier: the price impacts of energy efficiency ratings for buildings in the German rental market, Energy Res. Soc. Sci. 47 (2019) 177–191. [90] M. Hu, Does zero energy building cost more? – an empirical comparison of the construction costs for zero energy education building in United States, Sustain. Cities Soc. 45 (2019) 324–334. [91] C. Nzukam, A. Voisin, E. Levrat, D. Sauter, B. Iung, A dynamic maintenance decision approach based on maintenance action grouping for HVAC maintenance costs savings in non-residential buildings, IFAC-PapersOnLine 50 (1) (2017) 13722–13727 https://doi.org/10.1016/j.ifacol.2017.08.2551. [92] Z. Cao, G. Liu, H. Duan, F. Xi, W. Yang, Unravelling the mystery of Chinese building lifetime: a calibration and verification based on dynamic material flow analysis, Appl. Energy 238 (2019) 442–452 https://doi.org/10.1016/j.apenergy. 2019.01.106. [93] S.B. Hamdan, A. Alwisy, B. Barkokebas, A. Bouferguene, M. Al-Hussein, A multicriteria lifecycle assessment framework for evaluating building systems design, J. Build. Eng. 23 (2019) 388–402 https://doi.org/10.1016/j.jobe.2019.02.010. [94] M. Krarti, K. Dubey, N. Howarth, Energy productivity analysis framework for buildings: a case study of GCC region, At. Energ. 167 (2019) 1251–1265 https:// doi.org/10.1016/j.energy.2018.11.060. [95] H.B. Gunay, W. Shen, G. Newsham, Data analytics to improve building performance: a critical review, Autom. ConStruct. 97 (2019) 96–109 https://doi.org/10. 1016/j.autcon.2018.10.020. [96] J.E. Cross, T.O. Shelley, A.P. Mayer, Putting the green into corrections: improving energy conservation, building function, safety and occupant well-being in an American correctional facility, Energy Res. Soc. Sci. 32 (2017) 149–163 https:// doi.org/10.1016/j.erss.2017.06.020. [97] J.R. Littlewood, M. Alam, S. Goodhew, G. Davies, The ‘Safety Gap’ in buildings: perceptions of Welsh fire safety professionals, Energy Procedia 134 (2017) 787–796 https://doi.org/10.1016/j.egypro.2017.09.586. [98] C.K.H. Hon, A.P.C. Chan, M.C.H. Yam, Relationships between safety climate and safety performance of building repair, maintenance, minor alteration, and addition (RMAA) works, Saf. Sci. 65 (2014) 10–19 https://doi.org/10.1016/j.ssci. 2013.12.012. [99] R. Sarlo, P.A. Tarazaga, M.E. Kasarda, High resolution operational modal analysis on a five-story smart building under wind and human induced excitation, Eng. Struct. 176 (2018) 279–292 https://doi.org/10.1016/j.engstruct.2018.08.060. [100] M. Awada, I. Srour, A genetic algorithm based framework to model the relationship between building renovation decisions and occupants' satisfaction with indoor environmental quality, Build. Environ. 146 (2018) 247–257 https://doi.org/ 10.1016/j.buildenv.2018.10.001. [101] Y. Geng, W. Ji, Z. Wang, B. Lin, Y. Zhu, A review of operating performance in green buildings: energy use, indoor environmental quality and occupant satisfaction, Energy Build. 183 (2019) 500–514 https://doi.org/10.1016/j.enbuild. 2018.11.017. [102] C. Yang, W. Shen, Q. Chen, B. Gunay, A practical solution for HVAC prognostics: failure mode and effects analysis in building maintenance, J. Build. Eng. 15 (2018) 26–32 https://doi.org/10.1016/j.jobe.2017.10.013. [103] S. Mahmoudkelaye, K.T. Azari, M. Pourvaziri, E. Asadian, Sustainable material selection for building enclosure through ANP method, Case Stud. Constr. Mater. 9 (2018) e00200https://doi.org/10.1016/j.cscm.2018.e00200. [104] Z. Cao, G. Liu, H. Duan, F. Xi, W. Yang, Unravelling the mystery of Chinese building lifetime: a calibration and verification based on dynamic material flow analysis, Appl. Energy 238 (2019) 442–452 https://doi.org/10.1016/j.apenergy. 2019.01.106. [105] I. Puķīte, I. Geipele, Different approaches to building management and maintenance meaning explanation, Procedia Eng. 172 (2017) 905–912 https://doi.org/ 10.1016/j.proeng.2017.02.099. [106] V. Kersuliene, E.K. Zavadskas, Z. Turskis, Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA), J. Bus. Econ. Manag. 11 (2) (2010) 243–258 https://doi.org/10.3846/jbem.2010.12. [107] C. Erden, F. Yener, I. Cil, F. Cerezci, Fuzzy axiomatic design for solving the facility layout problem of a furniture company, Int J. Oper. Logist. Manag. 5 (3) (2016) 145–153. [108] A.V. Khandekar, S. Chakraborty, Selection of material handling equipment using fuzzy axiomatic design principles, Inform 26 (2) (2015) 259–282 https://doi.org/ 10.15388/Informatica.2015.48.
17
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke [109] C. Kahraman, S. Cebi, A new multi-attribute decision making method: hierarchical fuzzy axiomatic design, Expert Syst. Appl. 36 (3) (2009) 4848–4861 https://doi. org/10.1016/j.eswa.2008.05.041. [110] E.K. Zavadskas, Z. Turskis, T. Vilutiene, Multiple criteria analysis of foundation instalment alternatives by applying additive ratio assessment (ARAS) method, Arch. Civil. Mech. Eng. 10 (3) (2010) 123–141 https://doi.org/10.1016/S16449665(12)60141-1. [111] D.E. Ighravwe, S.A. Oke, A fuzzy-grey-weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems, Int. J. Syst. Assur. Eng. Manag. 8 (2) (2016) 961–973 https://doi.org/10. 1007/s13198-016-0554-8. [112] S. Chakraborty, E.K. Zavadskas, Applications of WASPAS method in manufacturing decision making, Informatica 25 (1) (2014) 1–20. [113] P. Karande, E. Zavadskas, S. Chakraborty, A study on the ranking performance of some MCDM methods for industrial robot selection problems, Int. J. Ind. Eng. Comput. 7 (3) (2016) 399–422 https://doi.org/10.5267/j.ijiec.2016.1.001. [114] P. Ferrante, G. Pen, G. Rizzo, G. Scuccianoce, V. Vaccaro, Old or new occupants of energy rehabilitated buildings. Two different approaches for hierarchizing group
[115] [116] [117]
[118] [119]
18
of buildings, Sustain. Cities Soc. 34 (2017) (2017) 385–393 https://doi.org/10. 1016/j.scs.2017.07.008. E. Bottani, M.C. Gentilotti, M. Rinaldi, A fuzzy logic based tool for the assessment of corporate sustainability: a case study in the food machinery industry, Sustain. Times 9 (2017) (2017) 1–29 https://doi.org/10.3390/su9040583. S.A. Oke, Condition based maintenance: status and future directions, S. Afr. J. Ind. Eng. 15 (2) (2004) 27–43. D.E. Ighravwe, S.A. Oke, K.A. Adebiyi, Preventive maintenance task balancing with spare parts optimisation via big-bang big-crunch algorithm, Int. J. Syst. Assur. Eng. Manag. 8 (2) (2017) 811–822 https://doi.org/10.1007/s13198-0160529-9. E. Bottani, M.C. Gentilotti, M. Rinaldi, A fuzzy logic based tool for the assessment of corporate sustainability: a case study in the food machinery industry, Sustain. Times 9 (2017) (2017) 1–29 https://doi.org/10.3390/su9040583. M.N. Grussing, Life cycle management methodologies for buildings, J. Infrastruct. Syst. (2013), https:/doi.org//01061/(ASCE)JS.1943-555x.0000157 130404171828002.
Contents lists available at ScienceDirect
Journal of Building Engineering journal homepage: www.elsevier.com/locate/jobe
A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria
T
Desmond Eseoghene Ighravwea, Sunday Ayoola Okeb,∗ a b
Department of Mechanical and Biomedical Engineering, The Bells University of Technology, Ota, Nigeria Department of Mechanical Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria
ARTICLE INFO
ABSTRACT
Keywords: Sustainability criteria Multi-criteria decision-making tools Public buildings maintenance Maintenance strategies
This article tackles how to select an adequate maintenance strategy for public buildings by using novel multicriteria decision-making (MCDM) models to validate their appropriateness for sustainable practices. The new innovation is the unique advantage of fuzzy axiomatic design (FAD) to consider the design requirements for public buildings. The stepwise weight assessment ratio analysis (SWARA), weighted additive sum product assessment (WASPAS), fuzzy axiomatic design (FAD) and the additive ratio assessment (ARAS) are competently integrated. Questionnaire data from a public building was used to evaluate and order the criteria in the rehabilitation exercise. The principal results are: For environmental and social criteria, the most and least suitable maintenance strategies are condition-based (S3) and preventive maintenance (S1) strategies, respectively. The most and least suitable maintenance strategies are corrective maintenance (S4) and S1 (ARAS), S4 and S1 (FAD), S1 and S3 (weighted additive method), respectively. Based on the aggregated criteria and the solution methods, the most suitable order for the case study maintenance strategies is S2 > S1 > S3 > S4. This study shifts attention to MCDM from the old literature perspectives of risks and life-cycle methods in the strategic choice of maintenance for public buildings.
1. Introduction The maintenance of public buildings is associated with considerable expenditure but intuition still remains the order-of-the-day in public buildings [1,2]. Consequently, public buildings continuously incur elevated cost of building operations and maintenance [3,4]. Nevertheless, scientific methods to determine what maintenance policy to adopt for public building are still under-reported in literature [5]. The consequence of this is that substantial cost is lost annually to the wrong choice of maintenance strategies for buildings [5,6]. It is therefore important to develop scientific methods to guide decision-making and sustainability of public building [7]. This gap has been recognised in the current research and pursued rigorously with the use of novel multicriteria decision models since many criteria are involved in this problem. Quite a substantial number of multicriteria approaches have been adopted to tackle scientific and engineering problems [8–12]. However, insignificant contributions to multicriteria models have been made in public building maintenance and sustainable building problems for the past years. The unique approaches were grouped according to the kind of decision approach they are relevant to, according to Polatidis et al.
∗
[13]. The novel contribution of Polatidis et al. [13] carefully recognised these kinds of approaches as four: outranking [8–12], utility-rooted, programming approaches and others. Outranking approaches include ELECTRE, REGIME and PROMETHEE [8–12]. For the utility-rooted approaches, examples include AHP, MAUT, SAW and SMART [14–16]. The programming approaches are mainly multi-objective programming supports [17,18]. Lastly, the other methods include SMAA, NAIADE and FLAG [19–21]. In the multicriteria modelling domain, often circumstances arise that options which may be contemplated are in original terms infinite in nature. However, Pokharel and Chandrashekar [22], Ramanathan and Gunesh [23] argued that the employment of multiobjective programming approaches is recognised to competently address the situation in building strategic choices and sustainable practices. Nonetheless, experience from the literature clearly reveals that these methods confront substantial limitations: Occasionally they result in an infeasible option [23]. Given this drawback, there is a requirement to recommend MCDM approaches as tools to conveniently and reliability tackle the building strategic choices and sustainable practices [22]. It is compelling to contemplate on the important criteria of maintenance strategies in order to make worthwhile decisions. The criteria
Corresponding author. E-mail address: [email protected] (S.A. Oke).
https://doi.org/10.1016/j.jobe.2019.100753 Received 19 September 2018; Received in revised form 13 March 2019; Accepted 24 March 2019 Available online 03 April 2019 2352-7102/ © 2019 Published by Elsevier Ltd.
The AHP is an organised method to examine complicated decisions. With foundation in psychology and mathematics, it emerged only in the 1970s as a decision making method used by teams globally for a broad array of subjects, including maintenance, facility design, quality management and safety engineering [36]. The world's re-known scientist, Thomas Saaty is credited to have originated the method [36,37]. Instead of stating the precise decision, AHP assists researchers to obtain the decision that most likely yields to their objectives and offers insights into the problem being solved [37] The PROMOTHEE has a complementary model referred to as geometrical analysis for literature aid (GAIA) used to analyse decision scenarios. It has foundation in sociology and mathematics and emerged in the early 1980s, and used by professionals in healthcare, government organizations, educational sector and the transportation industry. The popular Brussels Professor, Jean-Pierre Brans is known to have introduced the method in 1982 [39]. Nonetheless, the collaborative developmental effort of Brans was complemented by Professor Bertrand Maresehal that made PROMETHEE to be closely tied to GAIA in decision analysis. The popularity of PROMETHEE was due to the success of the technique in offering researchers and decision makers with total and partial ordering of issues. Complimentarily, the GAIA permits researchers to visualize the principal characteristics of a research problem. Thus, it is possible to establish conflicts and promote synergies among criteria in a straightforward manner. It is also easy to establish clusters of issues as well as indicate substantial accomplishments. PROMETHEE are of versions, including PROMETHEE I an II [39,40] The ELECTRE is a two-phase technique: construction of single or multiple outranking associations and to elaborate on the suggestions of the construction stage through an exploitation process. The principal goal of the first stage is to weigh each pair of issues again each other. The method evolved in 1965 by a team at SEMA consult, headed by the popular Bernard Roy whose name is often strongly associated with the radical changes of the technique from its original form ELECTRE to its expansions as ELECTRE I, II, III, IV and ELECTRE IS as well as ELECTRE TRI. The applications of the method has been well pronounced in small hydropower systems, design and development projects [41]
Analytical hierarchy process (AHP)
Elimination Et ChroixTraduisant la REalite or Elimination and choice Expressing Reality (ELECTRE)
Preference ranking organisation method for enrichment evaluation (PROMETHEE)
Brief information (history)
Methodical description
Table 1 Summary of the various MCDM models.
antagonistic, mutual strength and mutual weakness [41]
vagueness and uncertainty in • Considers computations [34] structure of the technique is designed to • The consider three kinds of interferences:
to apply; do not assume the • Straightforward proportionate state of the criteria [34]
is flexibility to compute the outcome • There with the Excel sheet [38]
[38]
result [38]
are plausible in that often members of • Results the group agree on the resulting preferences
of hierarchical structuring in the • Presence decision problem identified to integrate many inputs from • Ability experienced experts to form a consolidate
Advantages
2
•
•
•
layman. Outranking results in the strengths as well as weaknesses of the options [34] Technique not suitable to use to reflect a substantial amount of interferes among criteria [41] Only applicable to choose superior options which empowers order in a pool of options [36] The method fails to be competent as a chosen group of options comprises of circuits and the chosen group is not unique [36]
procedure to obtain results may be • The complicated to understand by the
approach to assign weight is not • The clear [34]
challenges confronted by the researcher to describe the request to give a second consideration to inputs [38]
relate a group of items [38]
that the index of consistency • Provided is greater than 10%, these are usually
one factor against the other • Weighing is definitely an artificial manner to
Disadvantages
(continued on next page)
Transportation, energy, environmental and water management [34]
Agriculture, hydrology, finance, transportation, water management, logistics, manufacturing and assembly environment [34]
Choice, ranking, prioritization, resource allocation, benchmarking, quality management, conflict resolution [38]
Areas of applications
D.E. Ighravwe and S.A. Oke
Journal of Building Engineering 24 (2019) 100753
A case-based reasoners, attempts to provide novel solutions to problems from a previously developed data-base of cases, through the use of, or by means of adopting solutions employed to tackle old problems [42,43]. The case-based reasoner as well provides a reasoning platform, which is the same with the manner that a lot of problem solvers commonly use to solve problems. The principal field on which case-based reasoning relies on is psychology, while the specific theories that are fundamental to CBR are the theories of working of the human memory. The two important events in the history of CBR are the episodic memory and the schema that were credited to Tulving in 1972 and Rumelhart in 1977, respectively. The schema acts as a reasoning procedure to use information chunks in novel circumstances. However, the episodic memory offers a way to store and recall huge information associated with occurrences, events, stories and scenes.
DEMATEL, which stands for Decision making trial and evaluation laboratory, is a competent technique to establish the root causes and the effects aspects for a complicated scheme [48,49]. The principal concern of DEMATEL is to appraise the interdependence of associations within the parameters/factors [48]. Further efforts are committed to searching for the critical factors by way of a visual structural replication [48]. The research center called Geneva, which is located at the Battelle Memorial Institute, has the credit of first proposing DEMATEL as an effective multicriteria methodology [48]. The chief illustrative tools for gaining insight into the working of DEMATEL are the workings of DEMATEL are the digraphs and matrices [48,49]. DEMATEL is built to confirm interdependence within parameters and could assist to build up a map which reveals comparative association among them as well as capable to study and tackling complex and inter-wined challenges [48,49] The original development of SAW is closely linked to Churchman and Ackoff in 1954 [36,50]. The major activity performed by SAW at its initial stage was to tackle the problem of choice concerning portfolio [36]. A number of authors claim that the SAW approach is the most superior, well-known and broadly employed [36,51,52] due to its straight-forward nature and capability to establish non-enhanced weakness of options. The SAW is largely intuitive in nature and a straight-forward approach to tackle challenges in multicriteria problem solving [36,52]. The reason behind this is that the linear additive scheme could reflect the preferences of the decision maker on individual basis. The basis of SAW lies in the calculation of the weighted total of the accomplishment ratings of all options for every criterion [36].
Case-based reasoning (CBR)
DEMATEL
Simple Additive Weighing (SAW)
Brief information (history)
Methodical description
Table 1 (continued)
Capability to reward within criteria; it is intuitive in nature to those who make decisions, straightforward computation and there is no need for complicated computer algorithms [34]
•
•
to build up issues [34,42] CBR creates a straightforward maintenance procedure in that it needs little maintenance; for the database to exist an insignificant upkeep is required [34,42] Establishes associations among criteria; these could be through direct method or indirect means [41]
not necessarily need knowledge to build up • Do rules or approaches creates in a straightforward way and • CBR requires minimum efforts to obtain extra data
the database [34]
application
could enhance with the passage of time • CBR particularly when extra cases are appended to
is structured to obtain justification by • CBR precedence in nature – mimics the way that we • Intuitive operate building maintenance scheme learns • The through the process of obtaining novel cases by
Advantages
SAW employs merely increasing appraisal measure, whereas reducing appraisal measure ought to be transformed into the reducing ones of the individual method before their uses [53]
indirect association is obtained is currently not clear at all [41]
procedure through which the • The combination of direct as well as
adaptation
of CBR by standard are • Schemes limited to reasonable solutions to irregularities in the • Itdatais sensitive being analysed [34,43] cases may be challenging • ToCBRadapt restriction in being unable to • offer has the most advantageous solution of occupying huge storage • Capable compartment in every case studied to generate cases through • Possibility hand requires algorithms on case-base, • CBR case choice and most likely, case-
Disadvantages
3
(continued on next page)
Business, waste management and financial management [34]
Technology park, railways, ecotourism, finance [48], Aviation transport [49]
Petroleum refinery and offshore [44,45], supplier selection [46], engine-oil design [47]
Areas of applications
D.E. Ighravwe and S.A. Oke
Journal of Building Engineering 24 (2019) 100753
TOPSIS is often called a multi-criteria tool with credit of development due to two notable experts, Ching-Lai Hwang and Yoon during the year 1981 [34,36]. As Yoon afterwards further developed it in the year 1987, Hwang, Lai and Liu committed more energy to its further development in 1993. The basis of the idea on TOPSIS is that the selected option ought to exhibit the smallest geometric distance among the criteria to the positive ideal solution (PIS) as well as the furthest geometric distance away to negative ideal solution [34]. TOPSIS is an approach that compensates in an aggregation procedure in which a group of options are weight against one another by first establishing the weights for all the criteria [34]. For TOPSIS to work, it is assured that criteria grows or shrinks in a monotonic manner. Since the parameters are of different dimensions, there is a requirement to normalise. TOPSIS encourages trade-offs among the criteria which implies that as a criterion performs poorly, correction could be made by the other criterion [34]. This is a straightforward, yet comprehensive, quantitative and qualitative in nature. It often exhibits the linear additive representation by mathematical nature. The meaning is that a total value of a stated option is computed as the total sum of the accomplishment value for every criterion considered as a product operation with the criterion's weight [34] This multi-criteria tool is rooted in the anticipated utility conjecture. The honour of original development is credited to Keeney in 1971 and a year later, to Savage. The principal aim of MAUT is to assist decision makers attach utility values rooted in their choices [36]. To attain an intersection of decision making, MAUT is often displayed as a structure in hierarchy which disintegrates the utility function to real-life nature [36,52]. In the MAUT scheme, the total utility value for every option may be computed by combining the preferences of every decision maker in the direction of all appraisal criteria [36]. The superior option may be established rooted in the utmost utility value [36].
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
Multi-attribute utility theory (MAUT)
Simple Multi-Attribute Rating Technique (SMART)
Brief information (history)
Methodical description
Table 1 (continued)
Capability to integrate features of prediction and customer satisfaction approximate values in obtaining solutions to appraisal problems [36]
Straightforward, permits all kinds of weight assignment method; minimal effort by users (decision makers) [34]
•
computerize the number of stages, which remains constant irrespective of the amount of attributes considered [34] TOPSIS′ ability to compensate the criteria's poor performance by the correction of another, referred to as trade-off, is often taken as an advantage and realistic approach to modelling.
procedure to apply this tool is • The straightforward. It is as well straightforward to
Advantages
4
•
decision makers is a challenging issue [36,54] MAUT is without the standard checks for consistency of the appraisal scheme [52,55]
develop a correct function for • ToMAUT which reflects the choices of the
The process could be in-convenient when the structure is contemplated [34]
By applying Eucelidean distance, the procedure fails to contemplate on the association of the attributes; challenging to weigh and maintain uniform decisions [34]
Disadvantages
Nuclear energy, software reliability
Manufacturing assembly tasks, construction, military, environmental, transportation as well as logistics [34]
Engineering, water resources, supply claims, human resources, manufacturing [34]
Areas of applications
D.E. Ighravwe and S.A. Oke
Journal of Building Engineering 24 (2019) 100753
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 2 Some previous studies on integrations of SWARA. Authors
Hybrid models
Karabasevic et al. [56], Stanujkic et al. [57] Karabasevic et al. [58] Zolfani et al. [59] Sarfaraz and Bahrami [60] Aghdaie et al. [31] Karabašević et al. [32] Ruzgys et al. [61] Krylovas et al. [62]
SWARA and MOORA (Multi-Objective Optimisation on basis of Ratio Analysis) SWARA and ARAS SWARA and Yin-Yang balance theory SWARA-COPRAS (Complex Proportional Assessment) SWARA and COPRAS-G (Complex Proportional Assessment of alternatives with Grey relations) SWARA and WASPAS SWARA and TODIM SWARA and VIKOR (VIsekriterijumska optimizacijai KOmpromisno Resenje)
are individually associated with how to reduce the use of energy emissions of CO2, building expenses, attaining utmost degree of thermal conflict and working within the budget for the financial year. Nonetheless, at times, the criteria could conflict and the most desirable option is to consider a trade-off association. Due to this motivation, the manager is saddled with the choice of appropriate maintenance strategy that will work [6]. From the bank of knowledge in building research literature, intensive interests have been shown by scholars on building maintenance but priority has excluded public buildings in reported cases [3,24–27]. In addition, despite the quantitative nature of building maintenance strategy formulation and implication, the use of empirical models for the proper evaluation of the situation has been less influential [28]. Even the available empirical models have not adequately treated the subject in a rigorous quantitative manner as to permit inferences based on fuzzy assumptions on models [28]. In the foregoing, it was decided to bridge this knowledge gap by developing a quantitative model that visualises maintenance strategy from the dimension of problem formulation and solution of multi-criteria decision-making (MCDM). Hackman and Scott [29] reported empirical frameworks to identify the rationale behind the selection of particular maintenance strategy to maintain a building. This problem is more challenging when considering sustainability criteria for building maintenance [28]. Although, Jolaoso et al. [30] identified the best maintenance strategy for estates in a developing economy based on government agency, self-tenants, private agents maintenance strategies in term of planned or unplanned maintenance strategies, their study did not provide information. The aim of this study is to select the most suitable maintenance strategy for public buildings maintenance using weighted additive sum product assessment (WASPAS), fuzzy axiomatic design principles, additive ratio assessment (ARAS) and sustainability criteria. In this study, step-wise weight assessment ratio analysis (SWARA) method is used to determine weights for the sustainability criteria and sub-criteria. To the best of our understanding, no study has reported the hybridisation of FAD, WASPAS, ARAS and SWARA methods, especially for maintenance strategies ranking for public buildings. The application of FAD principles and ARAS helps to relate maintenance strategy with sustainability criteria for buildings. The results from FAD principles are compared with ARAS results. We then use a WASPAS method to determine the final ranks of different maintenance strategies. The remaining sections of the current study are organised as follows: Section 2 is the literature review. Section 3 presents the proposed FADSWARA methodology. In section 4, the application of proposed methodology in a case study is presented coupled with the major contributions of the paper. The conclusions of this study are presented in section 5.
reality several objectives and the accomplishment of the system is evaluated by attributes [31]. In this section, the review of literature is based on an analysis of several multi-criteria schemes, hybrids of SWARA and building sustainability. The SWARA hybrid is presented to show the potentials of SWARA as a veritable tool for hybrid models. Hybridisation is an idea, which permits us to take advantage of SWARA and the other multicriteria tools. This idea is synonymous to crossbreeding in science and new multi-criteria structures are formed which are hybrids of the two multicriteria tools. The hybrid has advantages over individual techniques and could help decision makers to handle information in a better manner. 2.1. Comparative examination of MCDM approaches As noted from the literature review, several excellent reviews on multi-criteria models have been contributed. Such extra-ordinary discussions always involve elaborations on their history, advantages and short-comings of the models as noted in Velasquez and Hester [34], Campos-Guzmán et al. [35] and Alabool et al. [36]. However, a summary of this information is offered in Table 1. A large number of studies have been performed tangential of this work (Table 2). Table 3 contains a summary of significant papers on building sustainability. 2.2. Conclusions arising from the literature survey In an attempt to gain in depth insight into the literature on public building a broad array of survey was conducted. The subsequent conclusions are informed from the research:
• A broad database is available in the building sustainability knowl• • •
2. Literature review Decision making emerges in building maintenance as a critical stage that decides the success or failure of building policy implementation in public buildings [1,3]. It involves through analysis of influential options, complications, elevated-risk consequences as well as uncertainties and concerns about inter-personal interactions [31–33]. In
• • 5
edge domain for the various types of buildings, including public buildings Significant efforts have been directed to improve building sustainability (Table 3). However, there is a gap on how to apply hybrid multi-criteria tools, such as SWARA integrated models (Table 2), to building sustainability concerns. Maintenance impacts on the sustainability of buildings positively and its effectiveness largely dictates the degree of building sustainability. Various approaches to maintenance strategies of buildings are feasible. A number of these approaches are more widely used than others. For instance, the preventive maintenance strategy is the dominant approach in most building systems. Commonly, the building owners adopt the strategy with the least cost and one that is viable commercially for wide application in a group of building. Thus, the preventive maintenance scheme offers a significant part of maintenance expenditures in most building systems. Quantitative preventive maintenance models can be employed to estimate the future breakdowns of building and when preventive maintenance activities should be carried out. Mathematical optimisation schemes could be used to search for the
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 3 Summary of important studies on building sustainability. Author
Work done
Yu and Kim [63]
Reviews schemes of building condition to appraise the quality of indoor air that impacts on the wellbeing as well as health of occupants, considering buildings and homes to be sustainable. Asserts that the representations based on LEED and BREEAM are broadly employed globally. While the LEED representation is biased towards health and well being, including indoor environmental quality, the focus of BREEAM is towards the efficient usage of energy. The paper advocates for a management plan on indoor air quality for the development of buildings, entailing an approval on indoor air quality while the living spaces are contemplated. Further asserts that depleted levels of emissions are required from the materials employed in new buildings. The indoor air in new building plan is as well advocated to include continuous maintenance of the systems of ventilation as well as HVAC Discusses case examples of old buildings that are recycled into libraries from Germany as well as European nations. The problems and the prospects to sustain the library buildings were elaborated upon with a focus on planning. The importance of this recycling process was emphasized to lessen the ecological foot-print of the buildings and from the perspectives of cost-effectiveness and efficiency. Advance and builds up a representation to appraise the sustainability of a building from the lens of building information modelling technique coupled with its permissible know-how. The span of sustainability entails production of materials, operation, demolition, disposal, construction and maintenance. All these are the life-cycle elements of a building and the approach may be as well described as life-cycle based. Discusses policies, which could cause a movement of the world economy to a largely sustainable direction. The focus of discussion was on a largely politically sustainable group of put forward idea towards green pursuits preferable to the existing pursuits. Elaborates on the operations of a group of trainers that adopts evidence-rooted method to build up and offer programmes (training) on sustainability with target on professional in built environment in Wales. The approach tackles the buildup of stall requirement that entails the training anticipation in the confines of the sector, view from short, middle and long ranges in planning. The advantages of the implementation of the structure to the Wales region are elaborated from the viewpoint of policy appraisal. Reviews diverse research approaches from the survey of literature, paying keen attention to the advantages and problems failed by the building information modelling (BIM) with reference to software application. Treated a case application in design to appraise diverse scenarios on a put-forward manifold-application building. It was stated that the software offers information required to improve design as well as the accomplishment of buildings. Elaborates on the function on the function of building information modelling (BIM) to lessen the fragmentation within the profession, involving professionals, considered for all the phases of building delivery with evidence obtained from the academic literature. Assets that BIM offers a repository which permits straightforward opportunity to and interfaces with information as well as knowledge considered in real-time. Concludes the BIM offers a structure for professionals to operate in a joint circumstance in all phases of delivery of building services. Applies an appraisal method to neigh in u- use of energy against data from environmental accomplishment from building situated in England that are planned to operate at elevated sustainability thresholds as well as depleted heating requirement. It was found that despite that the two buildings attained evaluated rates of air permeability, they experienced common concerns associated with weak documentation regarding the as-built drawings, challenges relevant to direction and weak handover, challenges relating to joining novel know-how, poor sub-meter calibration and weak controls of windows. Reveals the outcome of the appraisal conducted, by means of worldwide known BSRS, that is Building sustainability rating systems, on varied housing schemes developed within the frame work of the BSRS. The adopted approach implemented a normalisation scheme because of the complication they offer in conducting a comparative examination was discussed. A case examination was contributed that reflects that buildings built up using the funding program for housing solutions (FPHS) attained depleted assessment values with respect to the BSRS. The challenge was in the substantial weakness of materials, efficiency of energy, quality of the indoor environment, as well as management. It was concluded from the research that a number of concerns with the FPHS assessment procedure work against the joining of sustainable features of the Mexican social housing scheme. Establishes the idea of sustainable building and the function that the application of building and the function that the application of building integrated photovoltaic (BTP) schemes plays to sustain structures. The validity of idea was tested using countries in the southern part of Asia as examples. A comprehensive survey of the application of the above mention scheme for the southern Asia countries together with the problems and barriers that the BIP scheme pose were elaborated upon. Discussed the spread of building coverage in geographical terms based on non-linear joint influence representation. The outcome was show cased in terms of unrestricted access data that evaluates the geographical spreading focusing on predictions, standard deviation and mean. The research was noted to offer quantitative insights concerning the models employed for prediction. Tackles the sizing problems of establishing the most advantageous sizing coupled with the choice of the commonest know-how for water supply and energy considering buildings that are sustainable. The solution was attained by manifold-objective and manifoldsituation joint integer non-linear optimisation technique. An attractive feature of the presented model is that it permits the user to establish the wishes of the designer rooted in a pre-established weight to attain the trade off results employing a route of dimensional analysis. The outcome of the research reveals the necessity of incorporating the non-linear feature of the know how's to establish an appropriate strategy for operations. Examine the sustainability accomplishment of modular buildings for the lens of life-cycle. The appropriate measures for sustainability were hinged on accomplishment factors based on unit buildings, built and ordered. The advanced representation was used in practice with convincing data using unit building from Canada. The research serves good purposes for practicing construction experts from the methodical structure proposed.
Hanke and Werner [64] Yung and Wang [65]
Bartolini [66] Hopkinson and Gwilliam [67]
Oduyemi and Okoroh [68]
Fadeyi [69]
Gupta et al. [70]
Saldana-Marquez et al. [71]
Shukla et al. [72]
Soliman et al. [73] Fuentes-cortes and Flores-Tlacuahuac [74]
Kamali et al. [75]
• • • •
• Local Nigerian data with unique characteristics in terms of costs,
utmost maintenance cost for buildings and the prediction of time estimates to carry out building maintenance activities. Sustainable maintenance of buildings has been at the central of research and the themes of a few scholarly journals are oriented towards promoting sustainable building maintenance. Sustainable maintenance practices are a necessary focus of the current research Very sparse information on the sustainability of public facilities through maintenance exists in literature on developing countries context but more exists in the Nigerian perspective Multi-criteria mathematical modelling of building maintenance has been scantily pursued
•
cultural influence on work behaviour, and management style associated with building maintenance practices have not been adequately explored. Training as a vital element in the knowledge upgrade of building maintenance manager has not been exploited fully in developing countries.
3. Methodology In order to account for experts' judgement in building maintenance strategy selection, this study presents a multi-criteria decision-making 6
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Fig. 1. Conceptual framework to select maintenance strategies for public building sustainability.
(MCDM) framework based on social, technical, economic and environmental criteria. This creates the opportunity for conflicting criteria to be analysed simultaneously, for example, maintenance cost and the quality of maintenance work. The framework also considers design requirements and expected outcomes from maintenance activities. It makes the expected outcomes to be defined using fuzzy numbers and uses more than one expert's judgement for solution to this selection problem. It allows experts to use linguistic terms when analysing maintenance strategies for building maintenance (Fig. 1). Over the years, different maintenance strategies have been used to maintenance public building [30]. Preventive (S1), predictive (S2), condition-based (S3) and corrective (S4) maintenance strategies are among the frequently used maintenance strategy for public buildings [29,30]. To determine the most suitable maintenance strategy for a public building, four sustainability criteria are considered (Table 4). The information on the sub-criteria for each of the sustainability criteria is based on the knowledge obtained from literature [28]. The
maintenance strategies and the sustainability criteria are connected together using FAD principles and ARAS method (Fig. 1). The generation of information contents for the FAD principles selection process is based on two sets of weights. The first set of weights is obtained from sub-criteria, while the second set of weight is for the sustainability criteria (Table 4). 3.1. SWARA method The application of SWARA entails five steps. Brief explanations of the steps that are required for SWARA application are as follows [56,106]: Step 1: Arrangement of criteria in descending order. The arrangement depends on the criteria expected significances. Step 2: Evaluation of the comparative importance of average value (sj). This involves the determination of the relative importance of the criterion j with respect to previous criterion (j-1). For the 7
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 4 Criteria for maintenance strategy selection of buildings. Criteria Environmental (x1)
Economical (x2)
Social (x3)
Technical (x4)
Sub-criteria
Description
Energy efficiency (x11) Emission reduction (x12) Waste reduction (x13) Water usage reduction (x14) Improved building value (x21) Better rental returns (x22) Reduced operational cost (x23) Extended building life-span (x24) Improved productivity (x31) Occupants' health and safety (x32) Human traffic (x33) Occupants' satisfaction (x34) Maintenance failure and downtime (x41) Spare parts quality and materials (x42) Staff expertise (x43) Business operations (x44)
Aslani et al. [76], Lidelöw et al. [77], Danish et al. [78], Silvero et al. [79], Shadram et al. [80] Moschetti et al. [81], Almeida et al. [82] Burlacu et al. [83], Sáez et al. [84] Fuentes et al. [85], Marinoski et al. [86] Gunay et al. [87], Pham et al. [88] Cajias et al. [89] Hu et al. [90], Nzukam et al. [91] Cao et al. [92], Hamdan et al. [93] Krarti et al. [94], Gunay et al. [95] Cross et al. [96], Littlewood et al. [97], Hon et al. [98] Sarlo et al. [99] Awada et al. [100], Geng et al. [101] Yang et al. [102] Mahmoudkelaye et al. [103], Cao et al. [104] Puķīte and Geipele [105] Puķīte and Geipele [105]
Step 4: Evaluation of the recalculated weight (qij ) using Equation (4)
Table 5 Triangular fuzzy numbers for comparative importance of the sustainability criteria and sub-criteria. Linguistic terms
Triangular fuzzy numbers
Fairly important Moderately important Highly important Extremely important
0.1, 0.3, 0.5, 0.7,
0.3, 0.5, 0.7, 0.9,
qij =
0.5 0.7 0.9 1.0
wij =
x ij =
ij
ij
=
K k=1
ij
K
,
K k = 1 ij
K
,
K k = 1 ij
K
+ 4 ij+ij 6
1 j=1 x ij + 1 j > 1
∀i(4)
qij n q j = 1 ij
∀i(5)
3.2. Hierarchical fuzzy axiomatic design (HFAD) principles The underlying information on the strategic choice of building maintenance, particularly public buildings has captured the attention of researchers and building practitioners in the past few years. Acquiring such information from maintenance policy models can offer public building owners competitive benefits to manage buildings competently. This paper has brought about an innovative method that utilizes fuzzy axiomatic design as an appropriate method to solve this problem due to the substantial benefits it offers the building community. Erden et al. [107] declared the benefits of the axiomatic design component of the fuzzy axiomatic design structure to include the incentive to make the researcher more innovative and dynamic. The authors also advanced the advantages of the method to include a platform to minimize unwarranted procedures and enhance design tasks [107]. The above advantages are the motivations for our interest in the fuzzy axiomatic design. Thus, we assume that the method of fuzzy axiomatic design should be advantageous to the development of a multi-criteria decision making model for public building maintenance strategy selection. The selected maintenance strategies are ranked based on two hierarchies of sustainability concepts. First hierarchy deals with general classification of sustainability criteria, while the second hierarchy considered sub-criteria of each of the sustainability criteria. In order to integrate the two hierarchies, HFAD methods are considered. The procedures for the HFAD methodology are explained as follows: The initial steps for HFAD implementation involve the design of membership functions for evaluation the system requirements of each of the alternatives based on selected criteria (Table 5) - the fuzzy numbers are trapezoidal fuzzy numbers (see Fig. 2). When considering more than one decision-maker, the different membership function intervals for each decision-maker are aggregated into a single interval (Equations (6) and (9)).
(1) (2)
Step 3: Evaluation of the coefficient (kij ) using Equation (3)
kij =
j>1
where wij represents the weight of criterion i and sub-criterion j.
Since the current study is based on multi-criteria decision making, the aggregation of decision-makers responses is expressed as Equation (1). The crisp value for an aggregated fuzzy number is expressed as Equation (2) ij ,
j=1
Step 5: Determination of the relative weights (wij ) of the evaluation criteria using Equation (5). The sum of the weights for all the criteria must be equal to 1.
building maintenance selection problem considered here, three possible options can reflect the fuzzy environment considered: the use of fuzzy numbers, linguistic terms or fuzzy sets. However, due to the nature of the problem, linguistic terms are considered appropriate and hence chosen for the research. For this choice, it is required to translate these linguistic terms into fuzzy numbers for further processing. This entails the process of defining the vague expressions by the use of triangular fuzzy numbers (TFNs). Then the TFNs of the design range, ratings and system range may then be considered for further processing. The TFNs are on other words the tools to reflect the design goal and the attributes of the option. In this study, the comparative importance of the sustainability criteria and sub-criteria are expressed in linguistic terms (Table 5). The linguistic terms in Table 5 is used to compare the importance of a criterion to another criterion. For example, how important is emission reduction to energy efficiency during the selection of a sustainable maintenance strategy? The conversion of the linguistic terms to crisp values is done using fuzzy numbers. The partitioning of these numbers is based on researcher's experience. This implies that a linguistic term can be partitioned into different fuzzy numbers.
ij,
1 kij 1 kij
∀i(3) 8
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
ij
ij
= min(
=
k ij )
(6)
2
K k k = 1 ij
(7)
K K k k = 1 ij
ij
=
ij
= max( ijk )
(8)
K
(9)
ij ij
+
1 ( 3 ij
ij ij
+
)2 ij
1 ( 3 ij
ij ij ij
ij
Pij =
1 pij
DR ij
DR ij
(13)
wij Iijs
(14)
m
wi Isi
Is =
(15)
i=1
where Iijs represents the information content of criterion i sub-criterion j for maintenance strategy s, Isi represents the information content of sustainability criterion i with respect to maintenance strategy s, and Is represents the information content of maintenance strategy s.
(10)
3.3. Additive ratio assessment (ARAS) During the normalisation of parameters during the implementation of ARAS, the preference directions of parameters are considered. For parameters that the smaller-the-better is preferred, two equations are considered (Equations (16) and (17)). Equation (16) is used to convert a smaller-the-better parameter to a bigger-the-better form [110]. Parameters whose preferred values are bigger-the-better are normalised directly using Equation (18).
x˜i = x¯i = x¯i =
1 xi
(16)
x˜i n x˜ i=1 i
(17)
xi
n x i=1 i
(18)
The normalised parameters are used to form a normalised decision matrix. Based on the weights for the various criteria in the normalised decision matrix, a weighted decision matrix is formed (Equation (19)). During the formation of a normalised decision-matrix, provision is usually made for ideal values (i.e., ideal optimisation direction) of the criteria. The ideal values are usually placed in the first column or row of a normalised decision matrix [110]. The ideal solutions for the criteria are often by considering the preferred values of the criteria. Since the values in the normalised decision matrix are usual within 0 and 1, the maximum preferred solution is 1, while the minimum preferred solution is the minimum values among the set of alternatives [110]. In the current study, the weights that are obtained using the SWARA method are considered during the implementation of the ARAS method. Other weights determination approaches can also be considered (fuzzy
(11)
CR SR
+
j=1
The information content of each of the sustainability sub-criteria is expressed as Equation (11). The value of Pij is obtained by considering the system requirement (SR) and the common range (CR) between design (DR) and system requirements for a sustainability sub-criterion (Fig. 4) [108]. In the typical design scheme for the building maintenance strategy selection, the success of the design, measured in probabilistic form, is strongly influenced by the inclination of the building maintenance manager, who acts as the designer of the public building being examined. This is expressed with respect to the level of tolerance that should be permitted. However, it is also influenced by the capability of the system from the perspective of delivery. From Fig. 4, the attainment of tolerance and system capabilities are respectively termed design range and system range. Nonetheless, the intersection (overlapping portion) of the design range with the system range gives what is referred to as the common range. The permissible design resolution is restricted to the shaded area, the common range. The same explanation applies in all cases irrespective of the shape of the function used: trapezoidal, triangular or the normal curve. Also note that the terms “designer's appraisal extent” and “design range” are used interchangeably in literature and “system capability range” is sometimes replaced with “system range” in scientific discussions. Equation (13) is used to determine the crisp value of CR [108].
Icij = log2
SR ij
n
Isi =
)2
ij
SR ij
DR 2 ij )
SR ij
where Pij is the probability of satisfying sustainability criterion i with respect to sustainability sub-criterion j. The weighted information content [109] for the different sub-criteria is expressed as Equation (14). The best maintenance strategy for building maintenance is the maintenance strategy that generates the lowest value for Equation (15).
where K denotes the total number of experts. The crisp values for aggregated values of the trapezoidal fuzzy number are expressed as Equation (10). This study considered trapezoidal fuzzy number when considering the responses from the different decision-makers (Table 6 and Fig. 3).
x ij =
(
CRij =
(12)
Table 6 Trapezoidal fuzzy numbers for the effectiveness of maintenance strategies.
Fig. 2. Trapezoidal fuzzy numbers. 9
Linguistic terms
Abbreviation
Trapezoidal fuzzy numbers
Ineffective Slightly ineffective Moderate effective Highly effective Extremely effective
I S M H E
(0.0, (0.2, (0.4, (0.6, (0.8,
0.1, 0.3, 0.5, 0.7, 0.9,
0.2, 0.4, 0.6, 0.8, 1.0,
0.3) 0.5) 0.7) 0.9) 1.0)
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
normalisation. The normalisation expressions that are presented in Equations (16)–(18) are also considered in the implementation of WASPAS for the current study. In order to determine the ranks of alternative, the normalised values are combined by considering the weights of each criterion [112]. The combination process is done from two perspectives. The first perspective deals with the determination of weighted additive values of the alternatives (Equation (22)), while the estimation of the weighted product values of the alternatives represents the second perspective (Equation (23)). n
Qi1 =
x ij wij
(22)
j=1 n
Qi2 =
Fig. 3. Membership functions for system requirements.
w
xij ij
(23)
j =1
To complete WASPAS operations, a control parameter for the contributions of either the weighted sum values or weighted product values is considered. The value of the control parameters lies between 0 and 1. The WASPAS value of an alternative is obtained through a linear combination of the weighted sum and weighted product values (Equation (24)). The most suitable alternative for a problem is taken as the alternative with the highest WASPAS value [111].
Qi = Qi1 + (1
) Qi2
(24)
where λ denotes contribution factor, and it its range is between 0 and 1. When λ = 0, WASPAS reduces to weighted product method. And when λ = 1, WASPAS method reduces to weighted sum method [113]. 4. Case study and contributions 4.1. Case examination Fig. 4. System requirements, common range and design requirements for the sustainability sub-criteria [108].
The choice of maintenance policies for buildings offers substantial chances to attain sustainable growth for public facilities in developing countries [2]. These chances ought to be periodically assessed since they are tools for progress in the built environment segment of national economy. For instance, the overwhelming value of CO2 emissions in the sector, representing the built environment (i.e., 47%) may be successfully curtailed if sustainable practices in public facilities and other buildings could be pursued [67]. Furthermore, this value is subject to the fluctuations of influencing variables such as the experience and competence of the manager in controlling these emissions and the government policies, which are uncertain in nature. Despite the understanding that some research has been carried out to appraise the sustainable form of public facilities in developing countries, substantial rigorous efforts have been largely limited [114,115]. Nonetheless, public facilities are often associated with several conflicting values located under the domain of economic, social and environmental perspectives [7]. The principal aim of the case study is to reveal the procedure through which choices among four maintenance strategies (corrective, condition-based, predictive and preventive) could be made. The situation warrants gathering experts' opinions in a scientific manner, through questionnaire distribution. The questionnaire information guides in the development of linguistic responses arising from the interaction with the experts and importance rating of the criteria will be obtained. The aggregation of the experts’ opinions is made while fuzzy mathematics offers guidance on the estimation of the sub-criteria weights. Comparative examination is then embarked upon to place each criterion of sustainability in its proper place using linguistic terms. The system range and common areas with respect to the maintenance strategies are evaluated and a decision on choice of the best maintenance strategies is given. To fully appreciate the decisions taken, briefs on the various maintenance strategies are given.
entropy weighting method, analytical hierarchy process, and analytical network process).
x i = x¯i wi
(19)
From the weighted normalised decision matrix, the optimality function of each alternative is determined (Equation (20)). The ideal solution (S0) for a solution process is used to determine the utility degree of the alternatives based on the results from Equation (21). The most suitable alternative for a problem is taken as the alternative that has the utility degree value (Equation (20)). m
Si =
xi i=1
ki =
Si S0
(20) (21)
i
where S and ki denotes the overall performance index and utility degree of alternative i, respectively. 3.4. WASPAS method The use of WASPAS methodology as a decision support tool in maintenance systems has been adopted in literature [111]. This study uses the final outputs from the FAD principles and ARAS method to determine the ranks of the different maintenance strategies. WASPAS method is a multi-criteria method for ranks determination that considered the weights of the criteria (i.e., information content and utility degree). Like other multi-criteria decision-making tools (e.g. TOPSIS and ARAS), the initial process of WASPAS implementation is data 10
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 7 Design range of the sustainability sub-criteria. Sub-criteria
Design range
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
0.6, 0.4, 0.4, 0.4, 0.6, 0.6, 0.4, 0.6, 0.6, 0.6, 0.4, 0.6, 0.6, 0.6, 0.6, 0.6,
0.7, 0.5, 0.5, 0.5, 0.7, 0.7, 0.5, 0.7, 0.7, 0.7, 0.5, 0.7, 0.7, 0.7, 0.7, 0.7,
0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6, 0.8, 0.8, 0.8, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8,
Table 9 Aggregated fuzzy number and crisp values for the sustainability sub-criteria comparative importance in linguistic terms. 0.9 0.7 0.7 0.7 0.9 0.9 0.7 0.9 0.9 0.9 0.7 0.9 0.9 0.9 0.9 0.9
Corrective maintenance with respect to public buildings is any maintenance carried out to return the building facility to the usual working mode. It includes the diverse building services to public buildings: supply and distribution of electrical energy in the building, activities of detection, protection and safety with respect to fire in public buildings. Others are the installation of controls for public buildings and the issue of escalator and lift corrective maintenance activities. Corrective maintenance permits those saddled with the building reliability task to direct attention to other activities in the building services domain pending a situation that gives rise to the service breakdown. Condition-based building maintenance is the maintenance carried out as needs arise [116]. It receives warnings of a forthcoming failure with the aim of spending the building maintenance budget on activities that matter. The benefits of condition-based maintenance when weighed against preventive maintenance include enhanced system reliability and drastically reduced costs. The predictive maintenance for buildings is a cheap, less labourdemanding strategy of public building maintenance that manages a large pool of accomplished data and forecast the needs of maintenance as well as incoming building failures in a little period [117]. The significant advantages of an adequately implemented building preventive maintenance plan include minimised downtime of service equipment (for instance escalators and lifts), and the reduction of the amount of substantial repairs. Other advantages include preservation of buildings and growth in the life-span of buildings. The current research advances a structure to appraise public facility sustainability to aid government in maintenance decisions in the populated country, Nigeria. This structure contains important elements of
D1
D2
D3
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
– H H M – M H H – E M M – M H H
– H M H – H H H – H M M – H M M
– M F M – M F M – H H H – H H H
Fuzzy numbers
Crisp values
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
– 0.433, 0.633, 0.833 0.300, 0.500, 0.700 0.367, 0.567, 0.767 – 0.367, 0.567, 0.767 0.367, 0.567, 0.767 0.433,0.633,0.833 – 0.567, 0.767, 0.933 0.367, 0.567, 0.767 0.367, 0.567, 0.767 – 0.433,0.633,0.833 0.433,0.633, 0.833 0.433, 0.633, 0.833
– 0.633 0.500 0.567 – 0.567 0.567 0.633 – 0.761 0.567 0.567 – 0.633 0.633 0.633
Table 10 SWARA results for the sustainability sub-criteria. Sub-criteria x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
s1j 0.633 0.500 0.567 s2j 0.567 0.567 0.633 s3j 0.761 0.567 0.567 s4j 0.633 0.633 0.633
K1j
q1j
W1j
1.000 1.633 1.500 1.567 K2j 1.000 1.567 1.567 1.633 K3j 1.000 1.761 1.567 1.567 K4j 1.000 1.633 1.633 1.633
1.000 0.612 0.408 0.261 q2j 1.000 0.638 0.407 0.249 q3j 1.000 0.568 0.362 0.231 q4j 1.000 0.612 0.375 0.229
0.438 0.268 0.179 0.114 W2j 0.436 0.278 0.178 0.109 W3j 0.463 0.263 0.168 0.107 W4j 0.451 0.276 0.169 0.104
environmental, economic, social and technical frameworks [118]. The applicability of the proposed framework in the current study was verified by collecting information from an agency that is in-charge of monitoring public buildings in Sango-Ota, Nigeria (Latitude: 7° 56′ 59.99″ N, Longitude: 4° 46′ 59.99″ E). Information was obtained using a well-structured questionnaire that captured relevant information that is required. In this case study, three senior staff (D1, D2 and D3) in the agency was asked to fill the questionnaire and information on the design requirements were also discussed with these experts (Table 7). The values in Table 7 are based on the experts’ opinion of the expected design requirements for their system. Thus, different experts can assign different design requirements for these values. The first part of the questionnaire was used to obtain linguistic responses from the experts on the importance of the sub-criteria (Table 8). Based the information in Table 8, Equations (6)–(9) are used to aggregate the experts' responses (Table 9). Using Equations (3)–(5), the fuzzy numbers in Table 9 are used to determine the weights for the sub-criteria and presented in Table 10. The weights for the sustainability criteria were determined based on the linguistic responses of the experts (Table 11) that were converted to aggregated fuzzy numbers (Table 12); this was achieved using Equations (6)–(9). However, Equations (3)–(5) are used to calculate the actual weights of the sustainability criteria (Table 13). The section part of the questionnaire was used to capture
Table 8 Comparative importance of the sustainability sub-criteria in linguistic terms. Sub-criteria
Sub-criteria
11
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
showed that the most suitable maintenance strategy in terms of economic, environmental and technical criteria is corrective maintenance strategy. Apart from social criterion, preventive maintenance strategy is considered as the least suitable maintenance strategy for building maintenance (Table 19). Based on the results in Table 19, FAD method ranks the maintenance strategies as S4 > S3 > S2 > S1 with respect to the economic, environmental and technical issues in sustainability. Thus, the maintenance strategies, from the most suitable to the least suitable, are ranked as corrective (S4) condition-based (S3), predictive (S2) and preventive (S1) maintenance strategies. During the application of the ARAS method, the information in Table 14 is considered. First, the normalised values of the aggregated responses are determined. Given that the questionnaire used in this study was designed in a way that seeks the maximum values of the criteria - for example, when using preventive maintenance strategy to maintain a building, how effective is this strategy to the waste reduction? - The bigger-the-better is considered. Thus, Table 20 presents the normalised decision matrix for the current problem (see Table 21). Fig. 5 shows the utilities of the maintenance strategies with respect to different criteria. Recall that the most important alternative, which for this study is maintenance strategy, is one with the highest utility value. Bearing this in mind, Fig. 5 shows that in terms of environmental and social criteria, the most and least suitable maintenance strategies are the condition-based (S3) and preventive (S1) maintenance strategies, respectively. On the other hand, condition-based and corrective maintenance (S4) strategies are the most and least suitable maintenance strategies, respectively. Furthermore, the ARAS results showed that most and least suitable maintenance strategies are corrective and preventive maintenance strategies, respectively (Fig. 5). The hybrid selection for the maintenance selection problems is carried out using the outputs from the FAD (information contents) and ARAS (utility degrees) methods (Table 22). In order to implement WASPAS methods for this problem, the importance of the solutions from the FAD and ARAS methods are considered equal (see Table 23). Since a FAD method ranks alternative based on the smaller-thebetter, Equation (17) were used to normalise the information in Table 22. The normalised utility degrees in Table 24 are obtained using the bigger-the-better approach, Equation (18). The WASPAS outputs in Table 25 were obtained when λ = 0.5. Based on the results obtained using the FAD method, the most suitable maintenance strategy for public buildings maintenance for the case study is corrective maintenance strategy. The FAD results showed that preventive maintenance strategy is the least suitable maintenance strategy for the case study. The ARAS results showed that predictive maintenance strategy is the most suitable strategy for the case study, while corrective maintenance strategy is the least suitable strategy (Fig. 6). In terms of weighted additive method, the most suitable strategy is S1, and S3 is the most suitable strategy in terms of weighted product method (Table 25). Based on the aggregated criteria and the solution methods, it can be inferred that the most suitable order for the case study maintenance strategies is S2 > S1 > S3 > S4.
Table 11 Comparative importance of the sustainability criteria in linguistic terms. Criteria
D1
D2
D3
x1 x2 x3 x4
– M M M
– H H H
– H M H
Table 12 Aggregated fuzzy number and crisp values for the sustainability criteria comparative importance in linguistic terms. Criteria
Fuzzy numbers
Crisp values
x1 x2 x3 x4
– 0.433, 0.633, 0.833 0.367, 0.567, 0.767 0.433, 0.633, 0.833
– 0.633 0.567 0.633
Table 13 SWARA results for the sustainability criteria. Criteria
Si
ki
qi
wi
x1 x2 x3 x4
0.633 0.567 0.633
1.000 1.633 1.567 1.633
1.000 0.612 0.391 0.239
0.446 0.273 0.174 0.107
information on the effectiveness of using a particular maintenance strategy for public buildings maintenance in the case study area. The information that was obtained is in linguistic form (Table 14). Based on the information in Table 14, the aggregated values of the fuzzy numbers for the effectiveness of the maintenance strategies that were considered were determined (Table 15). The aggregated fuzzy numbers for the sub-criteria were used to determine the system areas of the maintenance strategies effectiveness for public building maintenance (Table 16). The common areas of the maintenance strategies were determined using the information in Tables 7 and 15. By considering Equation (12), the information contents of the subcriteria for the maintenance strategies were determined (Table 17), while Equation (14) was used to compute the weighted information content (Table 18). In terms of the total weighted information contents, the FAD results Table 14 System range in linguistic terms.
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
S1 D1 E E H H E H H E E E H H E H E H
D2 H H H H E E E E E E E E E H H E
D3 M M M M M M M M M M M M M M M M
S2 D1 E E E H H H H E H E H H E E H H
D2 H H H H E E E E E E H E H H H E
D3 H H M M M M M M M M M M M M M M
S3 D1 E E E E E E E E E H H H H H H H
D2 H H E E E H H H H H M H M H H M
D3 M M M M M M M M M M M M M M M M
S4 D1 E H H E H E H E E E E H H H E H
D2 H E E E E E H H M H M H M M M H
D3 S S S I I M M M M M M M M M M M
4.2. Research contributions This study re-looks maintenance selection as it relates to public buildings from a new perspective. Until now, the principal perspectives have been the risk-rooted and life-cycle lens [119]. Specifically, the following contributions are made in the research. First, this study offers a framework to examine the issue of maintenance selection in public buildings. On every occasion that the issue of maintenance of public buildings is debated, there is trend to encourage critical reflection on building risks and assume a narrow view towards maintenance discussions. This is a widespread experience in debates on maintenance of public buildings. While this study was prepared, there was an attempt to modify this insight on maintenance of public buildings. The issue of multi-criteria decision making is revealed, tested and confirmed as a 12
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 15 Aggregated fuzzy numbers for the maintenance strategies. S1 x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
S2
0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
0.700, 0.700, 0.633, 0.633, 0.767, 0.700, 0.700, 0.767, 0.767, 0.767, 0.700, 0.700, 0.767, 0.633, 0.700, 0.700,
0.800, 0.800, 0.733, 0.733, 0.867, 0.800, 0.800, 0.867, 0.867, 0.867, 0.800, 0.800, 0.867, 0.733, 0.800, 0.800,
1.000 1.000 0.900 0.900 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.900 1.000 1.000
S3
0.600, 0.600, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
0.767, 0.767, 0.700, 0.633, 0.700, 0.700, 0.700, 0.767, 0.700, 0.767, 0.633, 0.700, 0.700, 0.700, 0.633, 0.700,
0.867, 0.867, 0.800, 0.733, 0.800, 0.800, 0.800, 0.867, 0.800, 0.867, 0.733, 0.800, 0.800, 0.800, 0.733, 0.800,
1.000 1.000 1.000 0.900 1.000 1.000 1.000 1.000 1.000 1.000 0.900 1.000 1.000 1.000 0.900 1.000
Table 16 System and common areas of the maintenance strategies. System area Sub-criteria x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
S1 0.805 0.805 0.723 0.723 0.873 0.805 0.805 0.873 0.873 0.873 0.805 0.805 0.873 0.723 0.805 0.805
S2 0.807 0.807 0.719 0.663 0.719 0.719 0.719 0.745 0.719 0.745 0.663 0.719 0.719 0.719 0.663 0.719
S4 0.631 0.631 0.631 0.567 1.390 0.745 0.550 0.719 0.694 0.719 0.694 0.663 0.637 0.637 0.694 0.663
S1 0.090 0.090 0.104 0.104 0.220 0.250 0.090 0.220 0.220 0.220 0.090 0.250 0.220 0.289 0.250 0.250
S2 0.014 0.014 0.090 0.104 0.250 0.250 0.090 0.220 0.250 0.220 0.104 0.250 0.250 0.250 0.289 0.250
0.567, 0.567, 0.767, 0.767, 0.767, 0.700, 0.700, 0.700, 0.700, 0.633, 0.567, 0.633, 0.567, 0.633, 0.633, 0.567,
0.667, 0.667, 0.867, 0.867, 0.867, 0.800, 0.800, 0.800, 0.800, 0.733, 0.667, 0.733, 0.667, 0.733, 0.733, 0.667,
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.900 0.900 0.900 0.900 0.900 0.900 0.900
0.200, 0.200, 0.200, 0.000, 0.600, 0.400, 0.200, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
0.633, 0.633, 0.633, 0.633, 0.567, 0.767, 0.500, 0.700, 0.633, 0.700, 0.633, 0.633, 0.567, 0.567, 0.633, 0.633,
0.733, 0.733, 0.733, 0.733, 0.667, 0.867, 0.600, 0.800, 0.733, 0.800, 0.733, 0.733, 0.667, 0.667, 0.733, 0.733,
1.000 1.000 1.000 1.000 1.000 1.000 0.900 1.000 1.000 1.000 1.000 0.900 0.900 0.900 1.000 0.900
Table 18 Weighted sub-criteria information index for the maintenance strategies.
Common area S3 0.325 0.325 0.659 0.659 0.659 0.690 0.690 0.690 0.690 0.697 0.737 0.697 0.737 0.697 0.697 0.737
0.200, 0.200, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400, 0.400,
S4
S3 0.220 0.220 0.079 0.079 0.220 0.250 0.090 0.250 0.250 0.289 0.123 0.289 0.341 0.289 0.289 0.341
S4 0.197 0.197 0.197 0.294 0.269 0.220 0.250 0.250 0.289 0.250 0.104 0.289 0.341 0.341 0.289 0.289
Sub-criteria
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
1.384 0.847 0.501 0.319 0.866 0.469 0.563 0.216 0.919 0.522 0.531 0.180 0.895 0.366 0.285 0.175
2.579 1.578 0.537 0.305 0.665 0.424 0.534 0.191 0.706 0.462 0.449 0.163 0.687 0.421 0.203 0.159
0.244 0.149 0.546 0.348 0.688 0.407 0.523 0.160 0.679 0.334 0.435 0.136 0.502 0.351 0.215 0.116
0.734 0.449 0.300 0.108 0.624 0.488 0.202 0.166 0.585 0.401 0.460 0.128 0.408 0.249 0.214 0.125
Table 19 Total weighted information contents and ranking order of the strategies.
Table 17 Sub-criteria information index for the maintenance strategies. Sub-criteria
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
3.161 3.161 2.799 2.799 1.985 1.687 3.161 1.985 1.985 1.985 3.161 1.687 1.985 1.325 1.687 1.687
5.888 5.888 2.998 2.673 1.524 1.524 2.998 1.756 1.524 1.756 2.673 1.524 1.524 1.524 1.199 1.524
0.558 0.558 3.053 3.053 1.579 1.466 2.940 1.466 1.466 1.271 2.587 1.271 1.113 1.271 1.271 1.113
1.675 1.675 1.675 0.946 2.367 1.756 1.138 1.524 1.264 1.524 2.738 1.199 0.904 0.904 1.264 1.199
Criteria x1 x2 x3 x4
Weighted information content S1 S2 S3 3.051 4.999 1.287 2.114 1.814 1.778 2.152 1.780 1.584 1.721 1.470 1.184
S4 1.591 1.480 1.574 0.996
Ranking order S3 > S4 > S2 > S1 S4 > S3 > S2 > S1 S4 > S3 > S2 > S1 S4 > S3 > S2 > S1
Table 20 Normalised decision matrix for ARAS method.
unique contribution to the selection of maintenance strategy for public buildings. In the authors’ opinion, the new approach of multi-criteria decision making offers a novel reference point to debate the issue of maintenance selection. Second, it offers a useful basis for public buildings to build up maintenance strategies converging on sustainability issued through the introduction of sustainability criteria in determining the most suitable maintenance strategy for public buildings. A major challenge facing 13
Sub-criterion
S0
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 X42 X43 X44
0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200
0.226 0.226 0.194 0.200 0.188 0.203 0.214 0.217 0.220 0.216 0.206 0.207 0.220 0.191 0.209 0.205
0.226 0.226 0.193 0.184 0.155 0.182 0.191 0.185 0.181 0.185 0.170 0.185 0.181 0.190 0.172 0.183
0.091 0.091 0.177 0.182 0.142 0.174 0.183 0.171 0.174 0.173 0.189 0.179 0.186 0.185 0.181 0.188
0.177 0.177 0.169 0.157 0.300 0.188 0.146 0.179 0.175 0.178 0.178 0.171 0.161 0.169 0.180 0.169
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Table 21 Weighted normalised matrix for ARAS method. Sub-criterion
S0
S1
S2
S3
S4
x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 x41 x42 x43 x44
0.088 0.088 0.088 0.088 0.087 0.087 0.087 0.087 0.093 0.093 0.093 0.093 0.055 0.055 0.055 0.055
0.099 0.060 0.035 0.023 0.082 0.057 0.038 0.024 0.102 0.057 0.035 0.022 0.099 0.053 0.035 0.021
0.099 0.061 0.034 0.021 0.068 0.050 0.034 0.020 0.084 0.049 0.029 0.020 0.082 0.053 0.029 0.019
0.040 0.024 0.032 0.021 0.062 0.048 0.033 0.019 0.080 0.045 0.032 0.019 0.084 0.051 0.031 0.020
0.077 0.047 0.030 0.018 0.131 0.052 0.026 0.019 0.081 0.047 0.030 0.018 0.072 0.047 0.030 0.018
Table 22 The maintenance strategies ranking order results for the ARAS method. Criteria
Ranking order
x1 x2 x3 x4
S3 > S4 > S2 > S1 S3 > S2 > S1 > S4 S3 > S4 > S2 > S1 S4 > S2 > S3 > S1
Table 23 Final outputs of the selection methods for the different maintenance strategies. Variable
S1
S2
S3
S4
Ranking order
Information content Utility degree
9.038 0.835
10.063 1.111
5.833 0.749
5.641 0.743
S1 > S3 > S1 > S2 S2 > S1 > S3 > S4
Table 24 Normalised decision matrix of the final outputs of the selection methods for the different maintenance strategies.
public buildings nowadays is sustainability. This concern is somewhat complex and crucial to the continued existence of buildings and their environment. It is understood that public buildings contribute carbondioxide emission as well as greenhouse effects that intimidate humans, their health and natural resources. Concern about sustainable public building is as well linked to buildings that are energy efficient, environmentally-healthy, considerate for natural surroundings and easy for office accommodation. So, in this research, an important contribution is to take a new position in sustainability of buildings with respect to the selection of maintenance strategy that attempts to reduce the harmful environmental effects of public buildings through improving efficiency and control of material usage, energy and spaces for development. Thus, in the viewpoint of the authors, selecting an appropriate maintenance strategy for public buildings would require a rigorous treatment of sustainability criteria. Third, it offers a structure to build up the principles of FAD to select maintenance strategy for public buildings. A most significant contribution of this study is that it brought on the need to capture the uncertainty elements in maintenance. Thus, a paradigm shift in maintenance evaluation and strategic analysis of schedules is the incorporation of fuzzy axiomatic design that has enhanced features over fuzzy sets alone. The issue that a substantial number of researchers endorse the application of fuzzy mathematics of maintenance analysis is in itself a signal to the promising nature of fuzzy axiomatic design in public building maintenance. Fourth, the unique adoption of SWARA method to determine the relative importance of sub-criteria of sustainability criterion and the use of WASPAS method as a decision tool in an outstanding manner to combine information and utility degree are important contributions in the public building maintenance literature.
Variable
S1
S2
S3
S4
Information content Utility degree
0.198 0.243
0.178 0.323
0.307 0.218
0.317 0.216
Table 25 Analysis of the maintenance strategies using WASPAS. S1
S2
S3
S4
Ranking order
Qi1
0.481
0.392
0.153
0.103
0.491
0.756
0.943
0.822
S1 > S2 > S3 > S4
Qi2 Qi
0.726
0.770
0.624
0.514
S3 > S4 > S2 > S1 S2 > S1 > S3 > S4
4.3. Research implications The research tackles the call for building maintenance investigations from the quantitative perceptions in the extant literature [119]. At first, the research employs the fuzzy axiomatic design to capture the uncertainty elements in building maintenance schedules. In addition, occasionally, quantitative analysis using multi-objective optimisation results in an infeasible option that is challenging to manage. These issues were not previously discussed in the building maintenance strategy literature. In fact, rather than focusing on these dimensions of problems in the literature, the risk analysis of building maintenance and how they impact on the life-cycle of the buildings have been the major focus of previous research [119]. In addition, previous literature has relegated sustainability in building maintenance strategy choice to the
Fig. 5. Utility degrees of the maintenance strategies. 14
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Fig. 6. Ranking of the maintenance strategies using different methods.
maintenance strategies ranking were different when considering the combined sustainability criteria values. In terms of economic sustainability criterion, both methods identified corrective maintenance strategy as the most suitable strategy for public buildings maintenance. Furthermore, the combination of the FAD and ARAS methods final outputs using WASPAS identified corrective maintenance strategy as the most suitable strategy for public buildings maintenance, while preventive maintenance strategy as the least suitable strategy for public building in the case study area.
background. It is now clear that the emergence of a sustainable-focused model and a study on building maintenance strategic choice with fuzzy axiomatic design at its core of analysis add new knowledge and insight and richness to building maintenance selection in some ways. By considering sustainability, the research highlights how the choice of strategy could be affected by social and environmental criteria for the most informed green decisions. The current research also shows that the most acceptable maintenance strategy selection is guide by considering every details of imprecision and certainties that could come from both the employees and the employers and even the government in the society. These issues could have significant implications for selection as well as research in maintenance.
5.2. Limitations of the study and future directions By considering the nature of this research, the outcomes are most related and useful for building managers in public buildings such as hospitals, research institutions, universities and poly-techniques. Nonetheless, the study may be considered restricted in light of methods and the interactions among criteria. To avoid this problem, one must select the criteria and related sub-criteria to be completely independent (which is almost infeasible in real-life). Thus, the Choquest method can be investigated to consider these interactions among criteria in future research. Considering this research, the outcomes are largely relevant to building managers in public institutions, particularly, agencies of governments responsible for building monitoring in Nigeria. Though this is consistent with the objective of this research, it as well restricts the relevance of the research outcomes to options in perception of building maintenance. It is likely that some sustainability criteria could have escaped our interest. A case in point is research institutes that are engineering focused, producing prototype factories as their research outcomes. Prototypes and incubation factories are small manufacturing plants with different set of criteria on sustainability from those applied in this research. So, additional probing of the sustainability issue would prove a rewarding research endeavour, especially considering the paucity of studies in this domain of knowledge at present. Apart from the building maintenance manager obtaining advantages from the proposed structure, the scheme of the maintenance policy presented can be further adjusted to usage as a check programme for director generals of government agencies, from which building manager's performance attributes could be checked. The obtained data could as well serve as audit evidence for expenditures during the annual organizational audit programmes. The gathered field-observation functional data may as well be important to examine the association among the structure.
5. Conclusions, limitations and future studies 5.1. Conclusions This research advances a multi-criteria maintenance scheme, which permits building maintenance managers to be informed about the various factors associated with building maintenance policy decision with particular emphasis on the scenario surrounding public building maintenance. An approach is adopted to capture information about sustainability criteria while carefully spreading considerations to social, environmental, economic and technical aspects. Equipped by means of the advanced knowledge, the building maintenance managers can function in the choice of the most appropriate maintenance strategy and be informed of the right combination of factors that result in the optimal decision making for public facilities. The complete design of the presented framework from the perspective of field-observed functional data, a questionnaire is built up and administer in a system of public facilities located in Ota, Ogun state, Nigeria. Furthermore, the on-site data experimentation of the field-observed data was carried out to establish and validate the structural framework presented. The nature of the structural design of the presented representation would permit building maintenance managers to adjust the parameters of the system in such a manner to favours the effectiveness in the working of the system. This study extends the applicability of FAD principles to public buildings maintenance strategy selection using economic, environmental, social and technical sustainability criteria. Furthermore, the extension of SWARA for determining the relative importance of public building sustainability sub-criteria was also reported in this study. The applicability of the proposed framework that combined FAD and SWARA was verified using information obtained from a developing country. Information from building experts in an organisation that is incharge of public buildings monitoring was used during the implementation of the proposed framework. The results obtained were compared with ARAS method results. It was observed that the FAD and ARAS methods results for the
Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 15
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
Appendix A. Supplementary data [24]
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jobe.2019.100753.
[25]
References
[26]
[1] E.E. Chidi, M. Shamsudeen, F.T. Oladipupo, A.J. Owolabi, An assessment of maintenance culture on public building in Nigeria (A case study of Osun State), IOSR J. Mech. Civ. Eng. 14 (5) (2017) 53–57. [2] O.O. Ugwu, C.C. Okafor, C.U. Nwoji, Assessment of building maintenance in Nigerian university: a case study of University of Nigeria, Nsukka, Niger. J. Technol. 37 (1) (2018) 44–52 https://doi.org/10.4314/njt.v37i1.6. [3] H. Krstic, S. Marenjak, Analysis of buildings operation and maintenance costs, Gradevinar 64 (4) (2012) 293–303. [4] J.-R. Kim, Y.-H. Jung, J.-H. Son, A study on reliability analysis model of the repair and replacement cycle of a building which utilizes Monte Carlo simulation, J. Korea Inst. Build. Constr. 10 (2) (2010) 41–50 https://doi.org/10.5345/JKIC. 2010.10.2.041. [5] C. de la Cruz-lovera, A.-J., Perea-Morewo, J.-L. de la Cruz-Fernandez, J.A. AlvarezBermejo, F. Manzano-Agugliaro, Worldwide research on energy efficiency and sustainability in public buildings, Sustain. Times 9 (2017) 1–20 https://doi.org/ 10.3390/su9081294. [6] H. Son, C. Kim, Evolutionary many-objective optimization for retrofit planning in public buildings: a comparative study, J. Clean. Prod. 190 (2018) 403–410 https://doi.org/10.1016/j.jclepro.2018.04.102. [7] O.A. Adenuga, Maintenance management practices in public hospital built environment: Nigerian case study, J. Sustain. Dev. Afr. 14 (1) (2010) 185–201. [8] M. Doumpos, J.R. Figueira, A multicriteria outranking approach for modelling corporate credit ratings: an application of the Electre Tri-nC method, Omega 82 (2019) 166–180 https://doi.org/10.1016/j.omega.2018.01.003. [9] M. Doumpos, Y. Marinakis, M. Marinaki, C. Zopounidis, An evolutionary approach to construction of outranking models for multicriteria classification: the case of the ELECTRE TRI method, Eur. J. Oper. Res. 199 (2) (2009) 496–505 https://doi.org/ 10.1016/j.ejor.2008.11.035. [10] P. Haurant, P. Oberti, M. Muselli, Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: a real case study using the ELECTRE outranking framework, Energy Policy 39 (2) (2011) 676–688 https:// doi.org/10.1016/j.enpol.2010.10.040. [11] F. Lolli, E. Balugani, A. Ishizak, R. Gamberini, M.A. Butturi, S. Marinello, B. Rimini, On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application, Expert Syst. Appl. 120 (2019) 217–227 https:// doi.org/10.1016/j.eswa.2018.11.030. [12] D.E. Ighravwe, S.A. Oke, A multi-criteria multi-hierarchical framework for ranking maintenance sustainability strategies based on PROMETHEE and fuzzy entropy weighting methods, J. Build. Pathol. Rehabil. 2 (9) (2017) 1–18 https://doi.org/ 10.1007/s41024-017-0028-7. [13] G. Polatidis, D.A. Haralamboponlos, G. Munda, R. Vreeker, Selection an appropriate multi-criteria decision analysis technique for renewable energy planning, Energy Sources, Part B 1 (2006) 181–193 https://doi.org/10.1080/ 009083190881607. [14] N. Khalil, S.N. Kamaruzzaman, M.R. Baharum, Ranking the indicators of building performance and the users' risk via analytical hierarchy process (AHP): case of Malaysia, Ecol. Indicat. 71 (2016) 567–576 https://doi.org/10.1016/j.ecolind. 2016.07.032. [15] V. Kutut, E.K. Zavadskas, M. Lazauskas, Assessment of priority alternatives for preservation of historic buildings using model based on ARAS and AHP methods, Arch. Civ. Mech. Eng. 14 (2) (2014) 287–294 https://doi.org/10.1016/j.acme. 2013.10.007. [16] J.K.W. Wong, H. Li, Application of the analytic hierarchy process (AHP) in multicriteria analysis of the selection of intelligent building systems, Build. Environ. 43 (1) (2008) 108–125 https://doi.org/10.1016/j.buildenv.2006.11.019. [17] P. Shen, W. Braham, Y. Yi, E. Eaton, Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit, At. Energ. 172 (2019) 892–912 https://doi.org/10.1016/j.energy.2019. 01.164. [18] H. Golpîra, S.A. Khan, A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty, At. Energ. 170 (2019) 1113–1129 https://doi.org/10.1016/j. energy.2018.12.185. [19] S. Angilella, P. Catalfo, S. Corrente, A. Giarlotta, M. Rizzo, Robust sustainable development assessment with composite indices aggregating interacting dimensions: the hierarchical-SMAA-Choquet integral approach, Knowl. Base Syst. 158 (2018) 136–153 https://doi.org/10.1016/j.knosys.2018.05.041. [20] W. Zhang, Y. Ju, L.F.A.M. Gomes, The SMAA-TODIM approach: modeling of preferences and a robustness analysis framework, Comput. Ind. Eng. 114 (2017) 130–141 https://doi.org/10.1016/j.cie.2017.10.006. [21] S. Corrente, J.R. Figueira, S. Greco, The SMAA-PROMETHEE method, Eur. J. Oper. Res. 239 (2) (2014) 514–522 https://doi.org/10.1016/j.ejor.2014.05.026. [22] S. Pokharel, M. Chandrashekar, A multiobjective approach to rural energy policy analysis, At. Energ. 23 (1998) 325–336 https://doi.org/10.1016/S0360-5442(97) 00103-5. [23] R. Ramanathan, L.S. Ganesh, Energy resource allocation incorporating qualitative and quantitative criteria: an integrated model using goal programming and AHP,
[27]
[28] [29] [30] [31] [32] [33] [34] [35]
[36] [37]
[38] [39] [40] [41] [42]
[43] [44] [45]
[46] [47] [48] [49] [50] [51] [52]
16
Soc. Econ. Plann. Sci. 29 (1995) 197–218 https://doi.org/10.1016/00380121(95)00013-C. M.F. Khamidi, O.A. Lateef, A. Idris, Building maintenance: a path towards sustainability, Malays. Constr. Res. J. 7 (2010). X. Zhang, Markov-based optimization model for building facilities management, J. Constr. Eng. Manag. 132 (11) (2006) 1203–1212 (2006)132:11(1203), https:// doi.org/10.1061/(ASCE)0733-9364. R. Ruparathna, K. Hewage, R. Sadiq, Economic evaluation of building energy retrofits: a fuzzy based approach, Energy Build. 139 (2017) 395–406 https://doi. org/10.1016/j.enbuild.2017.01.031. Y. Fan, X. Xia, A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with root top PV system installation and maintenance, Appl. Energy 189 (2017) (2017) 327–335 https://doi.org/10.1016/j.apenergy. 2016.12.077. E. Chan, Building maintenance strategy: a sustainable refurbishment perspective, Univers. J. Manag. 2 (1) (2014) 19–25 https://doi.org/10.13189/ujm.2014. 020103. H.Y.L. Hackman, D. Scott, Development of a conceptual framework for the study of building maintenance operation processes in the context of facility management, Surv. Built. Environ. 19 (1) (2008) 81–101. B.A. Jolaoso, N.A. Musa, O.A. Oriola, Appraisal of the maintenance of public residential estates in Ogun state: case study of Ibara housing estate, Abeokuta, J. Emerg. Trends Econ. Manag. Sci. 3 (5) (2012) 509–516. M.H. Aghdaie, S.H. Zolfani, E.K. Zavadskas, Decision making in machine tool selection: an integrated approach with SWARA and COPRAS-G methods, Inzinerine Ekonomika-Eng. Econ. 24 (1) (2013) 5–17. D. Karabašević, D. Stanujkić, S. Urošević, M. Maksimović, An approach to personnel selection based on SWARA and WASPAS methods, J. Econ. Manag. Inf. (2016) 1–11 https://doi.org/10.5937/bizinfo1601001k. M. Li, Extension of axiomatic design method for fuzzy linguistic multiple criteria group decision making with incomplete weight information, Math. Probl Eng. (2012) 1–17 https://doi.org/10.1155/2012/634326. M. Velasquez, P.T. Hester, An analysis of multi-criteria decision making methods, Int. J. Oper. Res. 10 (2) (2013) 56–66. V. Campos-Guzmán, M.S. García-Cáscales, N. Espinosa, A. Urbina, Life cycle analysis with multi-criteria decision making: a review of approaches for the sustainability evaluation of renewable energy technologies, Renew. Sustain. Energy Rev. 104 (2019) 343–366 https://doi.org/10.1016/j.rser.2019.01.031. H. Alabool, A. Kamil, N. Arshad, D. Alarabiat, Cloud service evaluation methodbased multi-criteria decision-making: a systematic literature review, J. Syst. Softw. 139 (2018) 161–188 https://doi.org/10.1016/j.jss.2018.01.038. T.L. Saaty, Relative measurement and its generalization in decision making: why pairwise comparisons are central in mathematics for the measurement of intangible factors. The analytic hierarchy/network process, Rev Royal Acad Exact, Phys. Nat. Sci. Ser. A: Math. 102 (2) (2008) 251–318. K.D. Goepel, Implementation of an online software tool for the analytic hierarchy process (AHP-OS), Int. J. Anal. Hierar. Proc. 10 (3) (2018) 469–487 https://doi. org/10.13033/ijahp.v10i3.590. J.P. Brans, Lingenierie de la decision: elaboration d’instrumentsd’aide a la decision La method PROMOTHEE, Press de l’universite Laval, 1982. J.P. Brans, P. Vincke, A preference ranking organization method: the PROMETHEE method for MCDM, Manag. Sci. 31 (6) (1985) 647–656. B. Delibasic, R. Ribeiro, Special issue on multi-criteria decision making approaches, Int. J. Decis. Support Syst. Technol. 7 (1) (2015) iv–v. B. Cheetham, P. Cuddihy, K. Goebel, Applications of soft CBR at general electric, in: S.K. Pal, T.S. Dillon, D.S. Yeung (Eds.), Soft Computing in Case Based Reasoning, Springer, London, 2001, https://doi.org/10.1007/978-1-4471-06876_15. J. Daengdej, D. Lukose, R. Murison, Using statistical models and case-based reasoning in claims prediction experience from a real-world problem, Knowl. Base Syst. 12 (5–6) (1999) 239–245 https://doi.org/10.1016/S0950-7051(99)00015-5. J.R.P. Mendes, C.K. Morooka, I.R. Guilherme, Case-based reasoning in offshore well design, J. Pet. Sci. Eng. 40 (1–2) (2003) 47–60 https://doi.org/10.1016/ S0920-4105(03)00083-4. Z. Zhang, D. Chen, Y. Feng, Z. Yuan, J. Han, A strategy for enhancing the operational agility of petroleum refinery plant using case based fuzzy reasoning method, Comput. Chem. Eng. 111 (2018) 27–36 https://doi.org/10.1016/j. compchemeng.2017.12.021. K. Zhao, X. Yu, A case based reasoning approach on supplier selection in petroleum enterprises, Expert Syst. Appl. 38 (6) (2011) 6839–6847 https://doi.org/10. 1016/j.eswa.2010.12.055. Z. Shi, H. Zhou, J. Wang, Applying case-based reasoning to engine oil design, Artif. Intell. Eng. 11 (2) (1997) 167–172 https://doi.org/10.1016/S0954-1810(96) 00029-5. S.L. Si, X.Y. You, H.C. Liu, DEMATEL technique: a systematic review of the stateof-the-art literature on methodologies and applications, Math. Probl Eng. (2018) 33 3696457 https://doi.org/10.1155/2018/3696457. I.E. Onyegiri, S.A. Oke, Applying decision trial and evaluation laboratory as a decision tool for effective safety management system in aviation transport, KKU Eng. J. 43 (4) (2016) 166–171. C.W. Churchman, R.L. Ackoff, An approximate measure of value, J. Oper. Res. Soc. Am. 2 (2) (1954) 172–187 https://doi.org/10.1287/opre.2.2.172. E. Triantaphyllou, Multi-criteria decision making methods, Multi- Criteria Decision Making Methods: and Applications, a State-Of-The-Art Survey, 186, Springer Science and Business Media, 2012. C.L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke
[53] [54] [55] [56]
[57] [58] [59]
[60] [61]
[62]
[63] [64] [65] [66] [67]
[68] [69] [70] [71]
[72] [73] [74] [75] [76] [77] [78] [79] [80] [81]
Applications a State-Of-The-Art Survey vol. 186, Springer Science & Business Media, 2012. V. Podvezko, The comparative analysis of MCDA methods SAW and COPRAS, Inzinerine Ekonomika-Eng. Econ. 22 (2) (2011) 134–146. M.J. Skibniewski, L.C. Chao, Evaluation of advanced construction technology with AHP method, J. Constr. Eng. Manag. 118 (3) (1992) 577–593 (1992)118:3(577), https://doi.org/10.1061/(ASCE)0733-9364. V. Betlon, A comparison of the analytic hierarchy process and a simple multiattribute value function, Eur. J. Oper. Res. 26 (1) (1986) 7–21 https://doi.org/10. 1016/0377-2217(86)90155-4. D. Karabasevic, D. Stanujkic, S. Urosevic, M. Maksimovic, Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods, Acta Montan. Slovaca 20 (2) (2015) 116–124 https:// doi.org/10.3390/ams20020116. D. Stanujkic, B. Djordjevic, D. Karabasevic, Selection of candidates in the process of recruitment and selection of personnel based on the SWARA and ARAS methods, Quaestus Multidisc Res. J. (2015) 53–64. D. Karabasevic, E.K. Zavadskas, Z. Turskis, D. Stanujkic, The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties, Informatica 27 (1) (2016) 49–65. S.H. Zolfani, E.K. Zavadskas, Z. Turskis, Design of products with both international and local perspectives based on Yin-Yang balance theory and SWARA method, Ekonomska istraživanja-Economic Res. 26 (2) (2013) 153–166 https://doi.org/10. 1080/1331677X.2013.11517613. H.Z. Sarfaraz, M. Bahrami, Investment prioritizing in high tech industries based on SWARA-COPRAS approach, Technol. Econ. Dev. Econ. 20 (3) (2014) 534–553 https://doi.org/10.3846/20294913.2014.881435. A. Ruzgys, R. Volvaciovas, C. Ignatavicius, Z. Turskis, Integrated evaluation of external wall insulation in residential buildings using SWARA-TODIM MCDM method, J. Civ. Eng. Manag. 20 (2014) 103–110 https://doi.org/10.3846/ 13923730.2013.843585. M. Alimardani, S. Hashemkhani Zolfani, M.H. Aghdaie, J. Tamošaitiene, A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment, Technol. Econ. Dev. Econ. 19 (3) (2013) 533–548 https://doi.org/10. 3846/20294913.2013.814606. C.W.F. Yu, J.T. Kim, Building environmental assessment schemes for rating of IAQ in sustainable buildings, Indoor Built Environ. 20 (1) (2011) 5–15 https://doi.org/ 10.1177/1420326X10397780. P. Hanke, K.U. Werner, The second hand library building: sustainable thinking through recycling old building into new libraries, Int Fed. Libr. Assoc. Inst. 38 (1) (2012) 60–67. P. Yung, X. Wang, A 6D CAD Model for automatic assessment of building sustainability, Int. J. Adv. Robot. Syst. (2014) 10.5772158446. S. Bartolini, Building sustainability through greater happiness, Econ. Lab. Relat. Rev. 25 (4) (2014) 587–602. L.L. Hopkinson, J.A. Gwilliam, Driving education and sustainable development potential within professional curricula: built environment sustainability training and training development for professionals in Wales, Local Econ. 30 (3) (2015) 280–291. O. Oduyemi, M. Okoroh, Building performance modeling for sustainable building design, Int. J. Sustain. Built. Environ. 5 (2016) (2016) 461–469. M.O. Fadeyi, The role of building information modeling (BIM) in delivering the sustainable binding value, Int. J. Sustain. Built.Environ. 6 (2017) 711–722. R. Gupta, M. Kapsali, M. Gregg, Comparative building performance evaluation of a “sustainable” community centre and a public library building, Build. Serv. Eng. Technol. 38 (6) (2017) 691–710. H. Saldana-Marquez, J.M. Gomez–Soberon, S.P. Arredondo-Rea, D.C. GamezGarcia, R. Corral-Higuera, Sustainable social housing: the comparison of the Mexican funding program for housing solutions and building sustainability rating systems, Build. Environ. 133 (2018) 103–122. A.K. Shukla, K. Sudhakar, P. Baredar, R. Mamat, BIPV based sustainable building in South Asian countries, Sol. Energy 170 (2018) 1162–1170. A. Soliman, A. Mackay, A. Schmidt, B. Allan, S. Wary, Quantifying the geographic distribution of building coverage across the US for urban sustainability studies, Comput, Environ. Urban Syst. 71 (2018) 199–208. L.F. Fuentes-Cortes, A. Flones-Tlacua-huac, Integration of distributed generation technologies on sustainable buildings, Appl. Energy 224 (2018) 582–601. M. Kamali, K. Hewage, A.S. Milani, Life cycle sustainability performance assessment framework for residential modular building: aggregated sustainability indices, Build. Environ. 138 (2018) 21–41. A. Aslani, A. Bakhtiar, M.H. Akbarzadeh, Energy-efficiency technologies in the building envelope: life cycle and adaptation assessment, J. Build. Eng. 21 (2019) 55–63. S. Lidelöw, T. Örn, A. Luciani, A. Rizzo, Energy-efficiency measures for heritage buildings: a literature review, Sustain. Cities Soc. 45 (2019) 231–242. M.S.S. Danish, T. Senjyu, A. Ibrahimi, M. Ahmadi, A. Howlader, A managed framework for energy-efficient building, J. Build. Eng. 21 (2019) 120–128. F. Silvero, F. Rodrigues, S. Montelpare, E. Spacone, H. Varum, The path towards buildings energy efficiency in South American countries, Sustain. Cities Soc. 44 (2019) 646–665. F. Shadram, J. Mukkavaara, Exploring the effects of several energy efficiency measures on the embodied/operational energy trade-off: a case study of Swedish residential buildings, Energy Build. 183 (2019) 283–296. R. Moschetti, H. Brattebø, M. Sparrevik, Exploring the pathway from zero-energy to zero-emission building solutions: a case study of a Norwegian office building, Energy Build. 188–189 (2019) 84–97.
[82] M. Almeida, M. Ferreira, Ten questions concerning cost-effective energy and carbon emissions optimization in building renovation, Build. Environ. 143 (2018) 15–23. [83] A. Burlacu, G. Sosoi, R.Ș. Vizitiu, M. Bărbuță, A.A. Șerbănoiu, Energy efficient heat pipe heat exchanger for waste heat recovery in buildings, Procedia. Manuf 22 (2018) 714–721. [84] P.V. Sáez, J.S. Cruz Astorqui, M.D.R. Merino, M.D.M. Moyano, A.R. Sánchez, Estimation of construction and demolition waste in building energy efficiency retrofitting works of the vertical envelope, J. Clean. Prod. 172 (2018) 2978–2985. [85] E. Fuentes, L. Arce, J. Salom, A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis, Renew. Sustain. Energy Rev. 81 (Part 1) (2018) 1530–1547. [86] A.K. Marinoski, R.F. Rupp, E. Ghisi, Environmental benefit analysis of strategies for potable water savings in residential buildings, J. Environ. Manag. 206 (2018) 28–39. [87] H.B. Gunay, W. Shen, G. Newsham, Data analytics to improve building performance: a critical review, Autom. ConStruct. 97 (2019) 96–109. [88] L. Pham, E. Palaneeswaran, R. Stewart, Knowing maintenance vulnerabilities to enhance building resilience, Procedia Eng. 212 (2018) 1273–1278. [89] M. Cajias, F. Fuerst, S. Bienert, Tearing down the information barrier: the price impacts of energy efficiency ratings for buildings in the German rental market, Energy Res. Soc. Sci. 47 (2019) 177–191. [90] M. Hu, Does zero energy building cost more? – an empirical comparison of the construction costs for zero energy education building in United States, Sustain. Cities Soc. 45 (2019) 324–334. [91] C. Nzukam, A. Voisin, E. Levrat, D. Sauter, B. Iung, A dynamic maintenance decision approach based on maintenance action grouping for HVAC maintenance costs savings in non-residential buildings, IFAC-PapersOnLine 50 (1) (2017) 13722–13727 https://doi.org/10.1016/j.ifacol.2017.08.2551. [92] Z. Cao, G. Liu, H. Duan, F. Xi, W. Yang, Unravelling the mystery of Chinese building lifetime: a calibration and verification based on dynamic material flow analysis, Appl. Energy 238 (2019) 442–452 https://doi.org/10.1016/j.apenergy. 2019.01.106. [93] S.B. Hamdan, A. Alwisy, B. Barkokebas, A. Bouferguene, M. Al-Hussein, A multicriteria lifecycle assessment framework for evaluating building systems design, J. Build. Eng. 23 (2019) 388–402 https://doi.org/10.1016/j.jobe.2019.02.010. [94] M. Krarti, K. Dubey, N. Howarth, Energy productivity analysis framework for buildings: a case study of GCC region, At. Energ. 167 (2019) 1251–1265 https:// doi.org/10.1016/j.energy.2018.11.060. [95] H.B. Gunay, W. Shen, G. Newsham, Data analytics to improve building performance: a critical review, Autom. ConStruct. 97 (2019) 96–109 https://doi.org/10. 1016/j.autcon.2018.10.020. [96] J.E. Cross, T.O. Shelley, A.P. Mayer, Putting the green into corrections: improving energy conservation, building function, safety and occupant well-being in an American correctional facility, Energy Res. Soc. Sci. 32 (2017) 149–163 https:// doi.org/10.1016/j.erss.2017.06.020. [97] J.R. Littlewood, M. Alam, S. Goodhew, G. Davies, The ‘Safety Gap’ in buildings: perceptions of Welsh fire safety professionals, Energy Procedia 134 (2017) 787–796 https://doi.org/10.1016/j.egypro.2017.09.586. [98] C.K.H. Hon, A.P.C. Chan, M.C.H. Yam, Relationships between safety climate and safety performance of building repair, maintenance, minor alteration, and addition (RMAA) works, Saf. Sci. 65 (2014) 10–19 https://doi.org/10.1016/j.ssci. 2013.12.012. [99] R. Sarlo, P.A. Tarazaga, M.E. Kasarda, High resolution operational modal analysis on a five-story smart building under wind and human induced excitation, Eng. Struct. 176 (2018) 279–292 https://doi.org/10.1016/j.engstruct.2018.08.060. [100] M. Awada, I. Srour, A genetic algorithm based framework to model the relationship between building renovation decisions and occupants' satisfaction with indoor environmental quality, Build. Environ. 146 (2018) 247–257 https://doi.org/ 10.1016/j.buildenv.2018.10.001. [101] Y. Geng, W. Ji, Z. Wang, B. Lin, Y. Zhu, A review of operating performance in green buildings: energy use, indoor environmental quality and occupant satisfaction, Energy Build. 183 (2019) 500–514 https://doi.org/10.1016/j.enbuild. 2018.11.017. [102] C. Yang, W. Shen, Q. Chen, B. Gunay, A practical solution for HVAC prognostics: failure mode and effects analysis in building maintenance, J. Build. Eng. 15 (2018) 26–32 https://doi.org/10.1016/j.jobe.2017.10.013. [103] S. Mahmoudkelaye, K.T. Azari, M. Pourvaziri, E. Asadian, Sustainable material selection for building enclosure through ANP method, Case Stud. Constr. Mater. 9 (2018) e00200https://doi.org/10.1016/j.cscm.2018.e00200. [104] Z. Cao, G. Liu, H. Duan, F. Xi, W. Yang, Unravelling the mystery of Chinese building lifetime: a calibration and verification based on dynamic material flow analysis, Appl. Energy 238 (2019) 442–452 https://doi.org/10.1016/j.apenergy. 2019.01.106. [105] I. Puķīte, I. Geipele, Different approaches to building management and maintenance meaning explanation, Procedia Eng. 172 (2017) 905–912 https://doi.org/ 10.1016/j.proeng.2017.02.099. [106] V. Kersuliene, E.K. Zavadskas, Z. Turskis, Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA), J. Bus. Econ. Manag. 11 (2) (2010) 243–258 https://doi.org/10.3846/jbem.2010.12. [107] C. Erden, F. Yener, I. Cil, F. Cerezci, Fuzzy axiomatic design for solving the facility layout problem of a furniture company, Int J. Oper. Logist. Manag. 5 (3) (2016) 145–153. [108] A.V. Khandekar, S. Chakraborty, Selection of material handling equipment using fuzzy axiomatic design principles, Inform 26 (2) (2015) 259–282 https://doi.org/ 10.15388/Informatica.2015.48.
17
Journal of Building Engineering 24 (2019) 100753
D.E. Ighravwe and S.A. Oke [109] C. Kahraman, S. Cebi, A new multi-attribute decision making method: hierarchical fuzzy axiomatic design, Expert Syst. Appl. 36 (3) (2009) 4848–4861 https://doi. org/10.1016/j.eswa.2008.05.041. [110] E.K. Zavadskas, Z. Turskis, T. Vilutiene, Multiple criteria analysis of foundation instalment alternatives by applying additive ratio assessment (ARAS) method, Arch. Civil. Mech. Eng. 10 (3) (2010) 123–141 https://doi.org/10.1016/S16449665(12)60141-1. [111] D.E. Ighravwe, S.A. Oke, A fuzzy-grey-weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems, Int. J. Syst. Assur. Eng. Manag. 8 (2) (2016) 961–973 https://doi.org/10. 1007/s13198-016-0554-8. [112] S. Chakraborty, E.K. Zavadskas, Applications of WASPAS method in manufacturing decision making, Informatica 25 (1) (2014) 1–20. [113] P. Karande, E. Zavadskas, S. Chakraborty, A study on the ranking performance of some MCDM methods for industrial robot selection problems, Int. J. Ind. Eng. Comput. 7 (3) (2016) 399–422 https://doi.org/10.5267/j.ijiec.2016.1.001. [114] P. Ferrante, G. Pen, G. Rizzo, G. Scuccianoce, V. Vaccaro, Old or new occupants of energy rehabilitated buildings. Two different approaches for hierarchizing group
[115] [116] [117]
[118] [119]
18
of buildings, Sustain. Cities Soc. 34 (2017) (2017) 385–393 https://doi.org/10. 1016/j.scs.2017.07.008. E. Bottani, M.C. Gentilotti, M. Rinaldi, A fuzzy logic based tool for the assessment of corporate sustainability: a case study in the food machinery industry, Sustain. Times 9 (2017) (2017) 1–29 https://doi.org/10.3390/su9040583. S.A. Oke, Condition based maintenance: status and future directions, S. Afr. J. Ind. Eng. 15 (2) (2004) 27–43. D.E. Ighravwe, S.A. Oke, K.A. Adebiyi, Preventive maintenance task balancing with spare parts optimisation via big-bang big-crunch algorithm, Int. J. Syst. Assur. Eng. Manag. 8 (2) (2017) 811–822 https://doi.org/10.1007/s13198-0160529-9. E. Bottani, M.C. Gentilotti, M. Rinaldi, A fuzzy logic based tool for the assessment of corporate sustainability: a case study in the food machinery industry, Sustain. Times 9 (2017) (2017) 1–29 https://doi.org/10.3390/su9040583. M.N. Grussing, Life cycle management methodologies for buildings, J. Infrastruct. Syst. (2013), https:/doi.org//01061/(ASCE)JS.1943-555x.0000157 130404171828002.