A software prototype for material handling equipment selection for construction sites

Automation in Construction 57 (2015) 120–131

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Automation in Construction journal homepage: www.elsevier.com/locate/autcon

A software prototype for material handling equipment selection for construction sites Kanika Prasad a, Edmundas Kazimieras Zavadskas b, Shankar Chakraborty a,⁎ a b

Department of Production Engineering, Jadavpur University, Kolkata – 700 032, West Bengal, India Vilnius Gediminas Technical University, Department of Construction Technology and Management, Sauletekio al. 11, LT-10223 Vilnius, Lithuania

a r t i c l e

i n f o

Article history: Received 6 December 2014 Received in revised form 20 May 2015 Accepted 1 June 2015 Available online 14 June 2015 Keywords: Material handling equipment Software prototype Quality function deployment Sensitivity analysis

a b s t r a c t Nowadays, every industry is experiencing demographic, economic and technological shifts, which have made innovation indispensable for them. The innovation in the construction industry can be evidenced through availability of a diverse range of new material handling equipments (MHEs) in the market having varying advanced features. An MHE is a critical investment made by a construction company that may significantly affect its future performance, competitiveness and sustainability. Therefore, selecting the best MHE with the desirable characteristics from a vast array of available alternatives is one of the most onerous tasks as often being faced by the construction engineers. In this paper, a software prototype based on quality function deployment (QFD) technique is designed and developed in Visual BASIC 6.0 for selecting two most suitable bulk-type MHEs, i.e. excavator and wheel loader for specific applications at a construction site. It is integrated with QFD method to provide due importance to the spoken and unspoken needs of the customers/construction engineers. It automates the entire MHE selection process and also performs sensitivity analysis study to investigate the effect of changing criterion weight on the alternative MHEs' ranking pattern. Its potentiality and applicability in solving bulk-type MHE selection problems is demonstrated in detail. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Material handling at a construction site, where the building materials in bulk need to be transported from one place to the other, has a direct influence on transit time, resources usage and service levels. It is intrinsically associated with the flow of construction and can be time consuming, expensive, troublesome and often dangerous. Moreover, improper, inadequate or unsafe handling of materials may cause injuries and even death to the workers in many cases. Therefore, the importance of recent concepts and ongoing efforts in the development of advanced construction technologies based on automation and information sciences, material science and system engineering should be duly acknowledged [1]. Handling materials safely, smoothly and directly with proper material handling equipment (MHE) must be the primary goal of a construction company to keep the process under control at all times. It also accounts for a major percentage of the total construction cost, and thus, efficient handling of construction materials is of paramount importance in significantly reducing this cost. Having an efficient and cost-effective material handling system necessitates designing the entire system at once even though it may comprise several subsystems. Selection and configuration of MHE types are the key sub⁎ Corresponding author. Tel./fax: +91 33 2414 6153. E-mail addresses: [email protected] (K. Prasad), [email protected] (E.K. Zavadskas), [email protected] (S. Chakraborty).

http://dx.doi.org/10.1016/j.autcon.2015.06.001 0926-5805/© 2015 Elsevier B.V. All rights reserved.

systems in the design of a material handling system [2]. In a construction company, as material handling is a cost-centered activity instead of a profit-centered one, it should be minimized as much as possible with respect to time, distance, frequency and overall cost, which can only be attained by selecting the most appropriate MHE for a given handling task. Selection of a proper MHE is also critical for its longer service life and smooth running of the construction company. Poorly selected MHE can severely affect the performance and lead to substantial losses in the construction process. Thus, to mitigate such risks, MHE selection is considered as an important issue for any construction company. The MHEs are designed in such a way that they facilitate easy, cheap, fast, and safe loading and unloading of construction materials with the least human intervention. Various types of MHEs with diverse operational features are available in the market in these days. A quite large number of features of the available MHEs, like technical specifications, capital cost, operational efficiency etc. need to be evaluated while identifying the most appropriate MHE for a particular construction site. These evaluation criteria are sometimes interrelated to each other and may be both qualitative and quantitative in nature. So, in order to select the best-suited MHE for a given handling task, all the possible options need to be analyzed carefully with respect to various relevant factors, which requires a lot of time, sufficient knowledge and necessary skill in a particular domain. Although, technical brochures can effectively convey various MHE specifications and some level of technical performance data, they often do not provide true comparisons of a particular

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MHE with the other competing choices. Thus, lack of complete, structured and accurate information is a problem often faced by the construction engineers while selecting an MHE for a specific handling task. This creates a need for development of a systematic and rational approach that can automate the entire MHE selection procedure. In this paper, a software prototype integrated with quality function deployment (QFD) technique is developed in Visual BASIC 6.0 to select two bulk-type MHEs, i.e. excavator and wheel loader to be deployed at a particular construction site. This integration with QFD technique becomes necessary here to incorporate the dynamic, spoken and unspoken requirements of the construction engineers in the final MHE selection decision. This paper is organized as follows. Section 2 presents a review on the past researches. Section 3 summarizes QFD technique, while the framework for the developed software prototype is outlined in Section 4. One illustrative example and advantages of the software prototype are provided in Section 5, and in the last section, conclusions are drawn. 2. Literature review The earlier researchers already suggested various mathematical models, multi-criteria decision making (MCDM) methods and knowledge-based systems to address the issue of MHE selection for industrial applications and construction projects. Skibniewski and Chao [3] applied analytic hierarchy process (AHP) to develop a systematic approach to evaluate advanced construction technologies. The viability of the proposed approach was demonstrated while evaluating tower crane alternatives. It was claimed that the proposed approach could organize tangible and intangible factors more systematically and provide a structured solution to decision making problems for new construction technology adaption. Braglia et al. [4] developed a model integrating AHP with integer linear programming method for solving MHE selection problem for intra-cell material movement in a cellular manufacturing system. It became possible to perform selection of the ‘good’ set of MHEs for intra-cell material movement under the given budget and space constraints. It was also shown how the AHP weightings could be combined with integer linear programming to include possible resource and size limitations. Bhattacharya et al. [5] proposed a mathematical model based on AHP method to combine cost factor components with the importance weightings for MHE selection. Shapira and Goldenberg [6] developed a model based on AHP method to select equipment for various construction projects. The proposed selection model had two modules, e.g. evaluation of soft factors and overall evaluation of costs with respect to soft factors. A modification of AHP was also proposed to correspond with the nature of equipment selection while offering an efficient and convenient approach to MHE selection. Chakraborty and Banik [7] employed AHP method to develop systematic steps for selecting the most appropriate MHE under a specific handling environment to meet some organizational requirements. Conveying systems were selected as the most preferred MHE. Sensitivity analysis was also performed to identify the most critical and robust criteria in the MHE selection process. Vijayaram [8] presented a review on material handling technology and pointed out the role of expert systems in selecting appropriate handling equipment for engineering industries. The need for developing expert systems for guiding the decision makers in selecting appropriate MHEs was also emphasized. Lin et al. [9] proposed an adaptive AHP approach (A3) employing soft computing and genetic algorithm (GA) to recover the real weights of various criteria in AHP methodology. Its applicability was demonstrated using a construction management example for determining criteria weights for a bestvalue bid. It was observed to be superior to the original AHP method with respect to cost-effectiveness, timeliness and better decision quality. Shapira and Simcha [10] implemented a non-statistical quantitative approach to assess the safety factors caused by the operation of tower cranes at a construction site. Onut et al. [11] proposed a combined fuzzy analytic network process (FANP) and fuzzy technique for order

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performance by similarity to ideal solution (FTOPSIS) methodology for evaluating and selecting the most efficient MHE. It was applied to a steel construction industry where rail crane system was identified as the most efficient MHE. Fuzzy TOPSIS was employed to select an MHE alternative, whereas, FANP was applied for calculating criteria weights. Ulubeyli and Kazaz [12] applied ELimination and Choice Expressing REality (ELECTRE III) method for selecting concrete pumps for a construction site. It was observed that X-52 was the most suitable concrete pump, followed by Z-52 and Y-52, respectively. A sensitivity analysis study identified no considerable change in the ranking pattern of the pump alternatives. Tuzkaya et al. [13] evaluated the performance of alternative MHEs while applying FANP and preference ranking organization method for enrichment evaluation (PROMETHEE). The proposed methodology could deal with the vagueness embedded in the MHE selection process. A sensitivity analysis showed that the derived results would become sensitive to the changes in the considered parameters. Lashgari et al. [14] combined AHP and TOPSIS methods under fuzzy environment in order to select a proper shaft sinking method in Parvadeh Coal Mine. Raise boring machine was selected as the most appropriate shaft sinking method for the mine. Using a GA-based meta-heuristic algorithm, Poon et al. [15] developed a mathematical model for effective selection and allocation of MHE for stochastic production material demand problems in a manufacturing organization. Momani and Ahmed [16] selected the most appropriate MHE while combining Monte Carlo simulation with AHP method. The applicability of the proposed model was demonstrated through a real-world problem showing its advantages over the traditional AHP method. Lashgari et al. [17] integrated fuzzy AHP, ANP and TOPSIS methods to develop a hybrid multiattribute decision making tool for MHE selection in Gole Gohar iron mine. The fleet of cable shovel and truck was identified as the most economical loading and hauling system. It was claimed that the proposed method would also be applicable for selection of earth-moving machinery for other open-pit mining project with necessary modifications. Jiang et al. [18] adopted a fuzzy MCDM model to aid in the decision making process for choosing the best wireless technology for tracking construction materials. The results showed that Wi-Fi might be a suitable solution for optimists and neutral persons, but UWB might be the better alternative for pessimists. Yazdani-Chamzini et al. [19] applied the Takagi-Sugeno fuzzy model based on subtractive clustering method for predicting the road header performance with respect to different machine- and rock-related parameters. The performance of the developed models was evaluated in comparison with the recorded data, and the best fit model was identified based on some performance evaluation indices. Karande and Chakraborty [20] employed weighted utility additive (WUTA) method to select the most suitable MHE for a given application. Based on an illustrative example, the viability of WUTA method was tested, and it was concluded that the WUTA would be a suitable tool in solving MHE selection problems. Rossi et al. [21] developed a structured approach based on AHP methodology for selecting the best alternative for manuable material handling taking into consideration ergonomic criteria and production performance measures. Mousavi et al. [22] applied a fuzzy grey group compromise ranking method to deal with the MHE evaluation and selection problems having uncertain information. A real-time example was considered for conveyer selection in a manufacturing environment. A sensitivity analysis was also preformed to investigate the effect of strategy weight and distinguished coefficient on the derived results. Olearczyk et al. [23] proposed a methodology for crane selection and developed the related mathematical algorithms to assess the construction of multi-lift operations for the selected cranes. The developed methodology could calculate the crane rotation point and assist in modifying the boom configuration rather than relocating the unit. Yazdani-Chamzini [24] employed two fuzzy MCDM methods, i.e. FAHP and FTOPSIS to select the most suitable MHE under an uncertain environment. The effectiveness of the proposed model was validated using a real-time case study. A sensitivity analysis was also performed to study the result sensitiveness to changes

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of the criteria weights. Waris et al. [25] identified some important criteria for selection of sustainable on-site construction equipment based on the fundamental concept of sustainability and provided an assessment framework to guide the decision makers for appraising the selection process. Kildienė et al. [26] designed a multi-criteria assessment model based on AHP and permutation methods to assess the deployment and distribution potential of a new technology/product for construction sector. Mital et al. [27] developed a modeling framework to design material handling system and warehouses under uncertainty to maximize the efficiency of the system while minimizing the risk of implementation. Zhang et al. [28] proposed a dynamic optimization method for shop floor material handling based on real-time and multi-source manufacturing data to provide a new paradigm for manufacturing organizations to implement a real-time data-driven optimization module for a specific handling task. On the other hand, various knowledge-based systems for MHE selection were also developed in the near past. Park [2] developed an expert system called ‘ICMESE’ for selection and evaluation of MHEs suitable for movement and storage of materials in a manufacturing facility. It was claimed that the developed expert system could resolve the limitations of the existing expert systems for MHE selection. Haidar et al. [29] developed a decision support system (DSS) for selection of excavating and haulage equipment in open-cast mining, while utilizing a hybrid knowledge-based system and GA. It was concluded that the developed DSS could successfully select the same type of equipment to excavate the overburden as used by the contractor in the mine. Chan et al. [30] designed an AHP-based expert consultant system called ‘MHESA’ with a database for selection of MHE suitable for movement and storage of materials in a manufacturing system. Yaman [31] designed a knowledge-based system for MHE selection and re-design of equipment in a given facility layout. It would guide the designers not familiar with the MHE selection process in arriving at the best decision while overcoming the limitations of analytical approaches. Fonseca et al. [32] developed a prototype expert system to identify the most suitable conveyor from a list of 76 conveyor types based on their suitability scores, which were computed using a weighted evaluation method. A total of 25 material, move and method attributes were considered to find an effective match between various MHEs and different features of the considered conveyor types. Kulak [33] designed a DSS called ‘FUMAHES’ for MHE selection based on axiomatic design principles, while including both crisp and fuzzy information. Cho and Egbelu [34] developed a web-based system ‘DESIGNER’, which could model and automate the material handling system design process, including selection of the best-suited MHE for a specific handling task. Sawant and Mohite [35] proposed a DSS named ‘AGVSEL’ for selection of automated guided vehicles based on TOPSIS, block TOPSIS and modified TOPSIS methods. Using those approaches, AGV model T-20 was considered as the best choice, and AGV UV-600 was the least preferred choice of the decision maker for the given application. Jato-Espino et al. [36] presented a review on applications of 25 MCDM methods for making precise decisions in various construction industries. It was observed that AHP and TOPSIS methods were predominantly applied, especially in combination along with other techniques. Skibniewski [37] reviewed 136 research articles in the field of computer-based construction safety engineering management. The review of the earlier research works reveals that AHP, ANP, utility models, goal programming, outranking methods, TOPSIS etc. are some of the methods applied for solving MHE selection problems in various manufacturing as well as construction industries. But, these methods have several limitations, like a) they require huge time-consuming computations, b) they are often based on the decision maker's involvement for arriving at the best possible decision and therefore fail to capture the overall requirement of the organization, c) most of them are inept to process both cardinal and ordinal data simultaneously, d) they fail to acknowledge the voice of customers, which is the basic motivating factor for any organization to forge

ahead in this global competitive market, e) the previously developed knowledge-based systems for MHE evaluation and selection are also found to be problem specific and lack user-friendliness, f) almost all the earlier expert systems for MHE selection cannot tackle the dynamic nature of the selection problem, and g) they fail to interrelate the needs of customers/engineers with the technical specifications of MHEs to be deployed at a construction site. Thus, it can be summarized that none of the previously developed expert systems for MHE selection has attempted to include the needs of customers into the decision making process. If customers' requirements are not considered while choosing the most appropriate MHE for a given handling task, it can then be inferred that the decision maker has failed to capture a very critical and important aspect in the overall selection process. Hence, QFD technique aimed at attaining the highest level of customer satisfaction may prove to be an appropriate tool to address the above-mentioned limitations of the previously employed MCDM methods and expert systems for solving MHE selection problems. Therefore, in this paper, a software prototype based on QFD technique is designed and developed in Visual BASIC 6.0 to automate the entire MHE selection process for a construction site while incorporating customers' needs into the decision making procedure. It is also observed that various input parameters of different MCDM methods employed for solving MHE selection problems are often subjected to sources of uncertainty and vagueness due to error in measurement, absence of data and sometimes inadequate or wrong data. So, there is an ardent need to investigate the effect of those ambiguities on the final solution, which can only be addressed through a detailed sensitivity analysis study. The earlier expert systems developed for MHE selection completely missed to acknowledge any type of sensitivity analysis study in their modules. In this paper, for the first time, the developed software prototype can perform a sensitivity analysis to investigate the influence of changing criterion weight on the final ranking order of MHE alternatives. 3. QFD methodology Akao [38] defined QFD as a ‘method to transform user demands into design quality, to deploy the functions forming quality and to deploy methods for achieving the design quality into sub-systems and component parts, and ultimately to specific elements of the manufacturing process'. Thus, QFD focuses on improving customers' satisfaction by a systematic analysis of the customers' needs and competitive market pressures, with the objective to develop a better product or provide a better service. The main aim of QFD is to identify the customers' requirements and the related important technical requirements, and subsequently, inject/translate those customers' requirements into technical requirements, throughout the marketing, research and development, engineering and manufacturing stages of product development to achieve the highest product quality. It simply employs a visual connective approach which is easy to comprehend and convenient to deal in practice. The principal tool for QFD is the house of quality (HOQ) matrix, as shown in Fig. 1. Along with HOQ, several other management and planning tools, as provided in Fig. 2, are also employed to identify, comprehend and prioritize customers' requirements effectively. The major building blocks of HOQ matrix include customers' requirements—demands of the customers for concerned products or services; technical requirements—design considerations determined by the concerned construction company's designers or development team with the purpose of achieving customers' requirements; interrelationship matrix—systematic ways of identifying the degree of relationship between each pair of customers' requirement and technical requirement; technical correlation matrix (also referred as roof of the matrix)—depicts relationship between various technical requirements; planning matrix—measures the performance of a construction company with respect to its benchmarked competitive company; and prioritized technical requirements—record the priorities assigned to technical requirements and compare them with those of the benchmarked

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making process while incorporating needs of the customers in the augmented decisions. Moreover, it is already mentioned that till date, QFD methodology has not been implemented for selection of an appropriate MHE for any organization. This section describes the developmental procedures of a QFD-based software prototype intended to select the most suitable bulk-type MHE for a construction site based on some important evaluation criteria. The QFD technique is incorporated here to interrelate the dynamic requirements of the customers/engineers with the technical specifications of MHEs. It is basically aimed to utilize knowledge of the experts on the related domain when they scarce. The framework for design and development of this software prototype is exhibited in Fig. 3. It comprises of the following three basic steps:

Technical correlation matrix Primary

Planning matrix

Secondary

Interrelationship matrix

Primary

Customers’ requirements

Tertiary

Secondary

Technical requirements

Tertiary

Prioritized technical requirements

Fig. 1. HOQ matrix [38].

competitor. Though, there are six major building blocks in a HOQ matrix, its structure may vary depending upon the objective, phase and scope of the problem under study. The QFD technique can be adopted to process both qualitative and quantitative data. Its main merit over the other MCDM approaches is that it provides flexibility to the decision makers/engineers to correlate both customer needs and engineering metrics through assigning scores and weights to them, and at the same time, it defines the direction of improvement for each metric, which may be directly or inversely proportional to each other [40]. The QFD technique now becomes so popular that it has been successfully employed in diverse fields of engineering and management, like material selection [41], industrial robot selection [42], supplier selection [43–47] etc. 4. Design of a software prototype for MHE selection for construction sites In the current context, growing consumer awareness, skill shortage and changing nature of the workforce necessitate development of a systematic framework/structured approach/graphical user interface for a construction industry, which can automate the MHE selection decision

Affinity diagram

Interrelationship diagram

Tree diagram/ Process flow

Matrix data analysis

Matrix diagram Unknown

Process decision program chart Fig. 2. Management and planning tools [39].

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Known

Arrow diagram

a) accumulation of the relevant data for the considered MHE selection problem, b) implementation of QFD methodology, and c) evaluation of the feasible alternatives to select the best MHE for a specific handling task at a construction site and perform the related sensitivity analysis study.

This software prototype is mainly developed for selection of two bulk-type MHEs, i.e. excavator and wheel loader, which are commonly used at any construction site. If a trench or sub-floor needs to be dug, an excavation equipment is then required. An excavator is a heavy construction equipment consisting of a boom, stick, bucket and cab on a rotating platform known as house. They are primarily employed for digging trenches, holes and foundations, material handling, forestry work, demolition, landscaping, mining etc. On the other hand, wheel loader is a type of truck usually wheeled, sometimes on tracks, that has a front-mounted square wide bucket connected to the end of two arms to scoop up loose materials from the ground, such as dirt, sand, asphalt, demolition debris, snow, gravel, logs, raw minerals, recycled materials, rock, woodchips, etc. The loader assembly may be a removable attachment or permanently mounted on tracks or wheels. The opening window of the software prototype is shown in Fig. 4 to guide the end user/engineer while selecting the most suitable MHE for a particular construction site. The first step in the development process of this software prototype comprises of accumulating the pertinent data for the excavator and wheel loader to build the related database. For this, battery power (in A-h), cost (in relative (R) scale), distance between the tumblers (in mm), ground pressure (in kPa), rated power (in kW), height of the cab (in mm), height of the boom (in mm), maximum dumping height (in mm), maximum digging depth (in mm), minimum swing radius (in mm), operating weight (in ton), overall length (in mm), overall width (in mm) and travel speed (in km/h) are first shortlisted as the technical requirements for excavator. Similarly, breakout force (in kN), cost (in R scale), bucket capacity (in m3), ground clearance (in mm), length (in mm), width (in mm), height (in mm), maximum power (in kW), digging depth (in mm), operating height (in mm), operating weight (in ton) and travel speed (in km/h) are considered as the important criteria for evaluation of wheel loader. The procurement costs of excavator and wheel loader are expressed using a relative scale of 1–9, where the scale values of 1 and 9 represent the minimum and maximum costs, respectively, as given in Table 1. These technical requirements are positioned at the top of HOQ matrix along its columns. The detailed information about the considered technical requirements for excavator and wheel loader are accumulated from the available online brochures of different manufacturers for subsequent development of the database in MS-ACCESS. The most important customers' requirements for MHE evaluation and selection are identified as capital investment, space occupied by the equipment, ease of operation, volume of materials that can be handled, speed in handling, operational reach, power of the engine, ease of maintenance, flexibility, safety and comfort during operation, and

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Fig. 3. Flowchart of the QFD-based software prototype.

environmental friendliness through questionnaires and customers'/ engineers' feedback forms. These customers' requirements are placed along the rows of HOQ matrix. The developed software prototype provides three options, i.e. ‘Excavator’, ‘Wheel loader’ and ‘Custom’ to choose the type of interrelationship matrix between customers' requirements and technical requirements to be generated. If any of the first two options is chosen, i.e. ‘Excavator’ and ‘Wheel loader’, an already filled up interrelationship matrix with default values appears. On the other hand, if ‘Custom’ option is chosen, the end user needs to fill up an empty interrelationship matrix with the necessary information based on the instructions provided. The customers' requirements can be either beneficial (higher the better) or non-beneficial (lower the better) in nature, and they are differentiated by the corresponding improvement driver value (+ 1

for beneficial criterion and −1 for non-beneficial criterion). The relative importance of the customers' requirements are assigned with the priority values of 1–5, where 1 is the least and 5 is the most important customer's requirements. From the filled up HOQ matrix, the weight for each technical requirement is computed using the following equation:

wj ¼

n X

IDi  Pr i  correlation index

ð1Þ

i¼1

where wj is the weight for jth technical requirement, n is the number of customers' requirements, Pri is the priority assigned to ith customer requirement, IDi is the improvement driver value for ith customer requirement and correlation index is the relative importance of jth technical

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125

Fig. 4. Opening window of the software prototype for MHE selection.

requirement with respect to ith customer requirement. These weights are subsequently employed to calculate the performance scores of the feasible MHEs for a specific handling task. The end user then needs to provide ranges of values for the selected criteria (technical requirements) to derive a list of feasible MHE alternatives satisfying those preset criteria values. From this list of feasible MHE alternatives, a sub-set of MHEs is finally shortlisted for the given handling task and the corresponding decision matrix is subsequently generated. It is then normalized using a linear normalization technique and the performance score for each MHE alternative is computed applying the following expression:

PSi ¼

n X

w j  ðNormalized valueÞi j ði ¼ 1; 2; …; m; j ¼ 1; 2; …; nÞ

ð2Þ

j¼1

where (Normalized value)ij is the linearly normalized value of the performnace of ith MHE alternative with respect to jth criterion, m is the number of alternatives and n is the number of criteria. Based on these performance scores, the alternative MHEs are then ranked, and the most appropriate MHE for the given handling task is finally identified along with its detailed technical specifications and an actual photograph.

Sensitivity analysis is an important statistical tool that assesses whether the final solutions derived from the software prototype are robust, consistent and likely to be generalized. It is quite helpful in understanding the strength of conclusions drawn in a decision making process, while examining the influence of protocol design error, inadvertent bias, deviation from the assumptions underlying statistical model and any unexpected input error on final results. A sensitivity analysis study on input data shows here how the rankings of MHE alternatives vary with the changing criterion weight. It also determines the range of the most important criterion weight over which the ranking pattern of MHE alternatives as derived by this software prototype remains stable. Thus, a non-proportional additive single dimensional weight sensitivity analysis [48] is performed here. In this approach, the most important criterion with the highest priority weight is identified first. Its priority weight is then increased and decreased in steps, while equally adjusting the remaining criteria weights. This approach is based on the assumption that all the criteria weights must add up to one. Here, the weight of the most important criterion can be reduced up to 0 and increased up to ŵ'j. The normalized weight for jth criterion (technical requirement) can be obtained applying the following expression: n X ^ j ¼ jw j j= w jw j j

Table 1 Scale indicating costs for excavator and wheel loader.

ð3Þ

j¼1

Scale

Interpretation

Excavator's cost (in USD)

Wheel loader's cost (in USD)

1 2 3 4 5 6 7 8 9

Lowest Very very low Very low Low Medium High Very high Very very high Highest

10 000–20 000 20 001–45 000 45 001–70 000 70 001–100 000 100 001–1200 00 120 001–150 000 150 001–200 000 200 001–250 000 250 001–300 000

5000–15 000 15 001–30 000 30 001–45 000 45 001–60 000 60 001–75 000 75 001–100 000 100 001–120 000 120 001–150 000 150 001–200 000

The value of ŵ'j is then determined as follows:   ^ 0j ¼ w ^ jmax þ ðn–1Þ  w ^ jmin w

ð4Þ

where ŵjmax and ŵjmin are the maximum and minimum normalized criterion weights, respectively. If the criterion weight is increased beyond ŵ'j, the weight of one of the remaining criteria then becomes negative.

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Fig. 5. HOQ matrix for excavator selection problem.

New sets of criteria weights are thus obtained to study the effect of changing criterion weight on the ranking performance of the software prototype. Both local and global weight stability intervals can be obtained in this sensitivity analysis study. The local weight stability interval indicates the range of criterion weight over which the position of the top-ranked MHE alternative remains unaltered. On the other hand, the global weight stability interval identifies the range of criterion weight over which the overall rank order of all the MHE alternatives remains unchanged. 5. Illustrative example In order to illustrate and validate the applicability of the developed software prototype, the following MHE selection problem for a construction site is formulated and solved, along with the related sensitivity analysis study. 5.1. Selection of an excavator The aim of this MHE selection problem is to identify the most appropriate excavator from a feasible set of alternatives belonging to the

similar category. The finally shortlisted MHEs should possess some desirable characteristics, like highly powerful engine, enhanced capability to handle large volume of materials and relatively low capital investment cost. For solving this problem employing the developed software prototype, ‘Excavator’ option is first chosen from ‘Type of material handling equipment’ module and ‘Next’ functional key is pressed to start the selection process. The above-mentioned characteristics of an excavator can be associated with its various important technical specifications, such as battery power, cost, operating weight and rated power. Hence, the construction engineer shortlists four criteria from the dropdown menu of the available technical requirements, as shown in Fig. 5 for evaluating the excavator alternatives. The functional key ‘HOQ matrix' is then pressed to generate the default HOQ matrix for excavator selection. For working at a construction site, an excavator should have low procurement cost, higher battery power and rated power, and ability to handle larger volume of materials. Amongst the considered customers' requirements, capital investment and space occupied by the MHE are non-beneficial attributes as indicated by their negative improvement driver values. Here, highest priority value of 4 is assigned to two attributes, i.e. capital investment and volume that can be handled. This indicates that capital investment made in an excavator and volume of

Fig. 6. Pre-selection module for excavator selection problem.

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materials it can safely handle are of prime importance to the construction engineers. Pressing of ‘Weight’ functional key then calculates the priority weights of the shortlisted technical requirements which are subsequently utilized for computation of the performance scores of the candidate excavator alternatives and opens up the ‘Pre-selection module’ in a new window, as exhibited in Fig. 6. A negative priority weight for cost indicates to always have its lower values. In the pre-selection module, the range of value for each technical requirement is specified and the functional key ‘Feasible alternatives’ is pressed to retrieve a list of feasible excavators satisfying all the set criteria values. A final set of six MHEs to be evaluated is then chosen from the list of feasible alternatives for ultimate selection of the most appropriate excavator for the given handling task at a construction site. In Fig. 7, the corresponding decision matrix for this excavator selection problem is automatically generated from the database on pressing of ‘Decision matrix’ functional key. Here, model ZX470LCH-3 emerges out as the most suited excavator, followed by model ZX350LCH-3G. Model ZX200LC-3G is identified as the least preferred choice. To graphically display the performance scores of the considered alternatives in the form of a bar chart and retrieve all the technical details of the best alternative (model ZX470LCH-3) along with its real-time photograph, the ‘Display’ functional key needs to be pressed. The detailed output from the software prototype for this MHE selection problem is exhibited in Fig. 7. In this selection problem, operating weight, rated power and battery power have higher priority weights than cost. It explains that handling a large volume of materials while consuming less power is of maximum importance to the construction engineers. A close view on the technical specifications of model ZX470LCH-3 reveals that it successfully satisfies all the desired technical requirements, i.e. higher operating weight, rated power and battery power as compared to its competitors. Although, the procurement cost of model ZX470LCH-3 is slightly on the higher side (USD

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100 001–120 000), but it is outperformed by the upper values of other criteria having higher priorities. This justifies selection of model ZX470LCH-3 as the most appropriate excavator for the given handling task. At the end, ‘Sensitivity analysis’ functional key is pressed to derive the local and global criterion weight stability intervals. For the sensitivity analysis study to observe the change in ranking pattern of the six alternative excavators with respect to varying criterion weight, the relative importance (normalized weight) of the considered four criteria are determined as 0.2123, 0.0559, 0.4972 and 0.2346, respectively, by employing Eq. (3). Hence, operating weight is identified as the most important criterion having the maximum relative importance. Now, its weight is varied within the feasible range of 0–0.6649 while adjusting the other criteria weights in such a way that all the criteria weights must add up to one. The weight of operating weight criterion cannot be increased beyond 0.6649, because the weight of cost criterion then becomes negative. These criteria weight variations are shown in Fig. 8. The ranking orders of the six excavators at varying weights of operating weight criterion are derived on pressing the ‘Graph’ functional key. Fig. 8 reveals that for the entire criterion weight range of 0 ≤ ŵ3 ≤ 0.6649 (ŵ3 is the normalized weight of operating weight criterion), model ZX470LCH-3 remains as the best excavator. It signifies that for this excavator selection problem, the local weight stability interval lies between 0 and 0.6649. It is also observed that the original ranking order of the alternative excavators remains unaltered when the weight of operating weight criterion varies between 0.1 and 0.6649. Therefore, it becomes the global weight stability interval for this selection problem. It can thus be inferred that the QFD-based software prototype as developed for solving the excavator selection problem offers a wide range of local and global weight stability intervals over which the selection decision of the most appropriate excavator for the given handling task remains unaltered. The most robust ranking pattern of MHE alternatives is thus obtained within the global weight stability interval.

Fig. 7. Decision matrix for excavator selection problem.

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Fig. 8. Sensitivity analysis for excavator selection problem.

5.2. Advantages of the developed software prototype This software prototype automates and eases out the entire MHE selection procedure, while eliminating rigorous calculations involved, thereby reducing the time taken to arrive at the best course of action. The construction engineers also need not to have any in-depth technical knowledge regarding the details and applications of the available MHEs. As it is integrated with QFD methodology, it provides due importance to the needs of customers/construction engineers and can also be augmented under a group decision making environment seeking opinions from different individuals. It is also quite user-friendly as it provides the end user with two options, i.e. default and custom for developing the corresponding interrelationship matrix. The default interrelationship matrices for MHE selection problems drastically minimize the time and effort of the construction engineers to achieve the most accurate decision. On the other hand, the end user is also given the freedom to customize the interrelationship matrix according to the specific

requirements of MHE application. It can effectively be employed for solving other types of MHE selection problems, showing its flexibility and universal applicability. Its novelty and scientific value is exhibited by incorporating a possibility to perform the related sensitivity analysis study to determine the weight stability interval over which the selection decision remains stable and robust. Moreover, it can be periodically upgraded in order to resolve the dynamic nature of the decision making problem. It performs satisfactorily in the presence of exceptional inputs and does not crash down in stressful conditions, which displays robustness of the same. 6. Conclusions The ability to handle construction materials safely is vital for proper functioning of any construction company that can only be attained while employing appropriate MHEs. Availability of a large number of MHEs with varying technical specifications and application areas

Fig. 9. HOQ matrix for wheel loader selection problem.

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Fig. 10. Pre-selection module for wheel loader selection problem.

makes the selection process more complicated and time consuming. Therefore, in this paper, a software prototype based on QFD technique is developed to automate the entire decision making process for selection of two bulk-type MHEs, i.e. excavator and wheel loader for specific handling tasks at a construction site. A software prototype in Visual BASIC 6.0 with an exhaustive database is designed to remove the huge mathematical calculations involved, while reducing the time taken in the entire MHE selection process. It can evaluate any number of MHE alternatives with respect to any number of selection criteria. The construction engineers now need not to have any in-depth technical knowledge regarding the details of the available MHEs. Since it is

integrated with QFD methodology, it can be applied under a group decision making environment. Moreover, the related sensitivity analysis study determines the local and global weight stability intervals over which the position of the top-ranked MHE alternative and the overall ranking pattern of all the candidate alternatives, respectively, remain stable. It also assures that the most appropriate MHE is selected accurately with respect to the given problem description. It performs successfully in presence of any erroneous input without getting crashed. The end user can also easily switch from one module to another in accordance with the dynamic requirements of the selection problem, which proves the flexibility of this software prototype. It presents a

Fig. 11. Decision matrix for wheel loader selection problem.

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Fig. 12. Sensitivity analysis for wheel loader selection problem.

structured approach for solving MHE selection problems for any construction site in order to satisfy the organizational objectives while fully exploiting the performance potential of the selected MHE. While creating the database for this software prototype, it is noticed that each and every organization does not display all the intricacies of technical specifications and commercial information on public domain. Therefore, the future scope of this paper may involve in including all the detailed technical specifications for excavator and wheel loader from every organization into the database to make the developed software prototype more efficient and powerful. Its database may be upgraded from time to time according to the changing handling environment to make it more dynamic. It can be applicable for evaluation and selection of other types of MHEs as used at different construction sites, while creating a new module and database within the same system. The capability, reach and usability of this prototype software may further be enhanced while making it entirely web-based to become accessible to its end users through an internet network. Appendix A. Selection of a wheel loader In this problem, the selected wheel loader should be capable of transporting a large volume of materials in shorter time. It must offer greater flexibility and the required capital investment should also be comparatively low. Considering the above-mentioned requirements of a wheel loader, bucket capacity, cost, digging depth, operating weight and travel speed are chosen as the most significant evaluation criteria from the drop-down menu of the available technical requirements, as shown in Fig. 9. The end user then provides the range of value for each criterion according to the requirements of the specific handling task in the preselection module of Fig. 10. In the next step, a final set of seven wheel loaders is shortlisted for subsequent evaluation. Model ZW250e is identified as the most appropriate choice for the given handling task, while model ZW40d emerges out as the least suitable wheel loader. The performance scores of the seven wheel loader alternatives, and the detailed technical specifications and an actual photograph of the finally selected wheel loader are exhibited in Fig. 11. Superiority of wheel loader model ZW250e over the other competing alternatives can be justified while reviewing its technical details. Although its procurement cost is very high (120 001–150000 USD), the highest values for the remaining three technical parameters favor its selection as the most preferred choice for the specified application.

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