A multiple criteria decision support on-line system for construction

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Engineering Applications of Artificial Intelligence 20 (2007) 163–175 www.elsevier.com/locate/engappai

A multiple criteria decision support on-line system for construction A. Kaklauskasa,, E.K. Zavadskasb, V. Trinkunasa a

Department of Construction Economics and Property Management, Vilnius Gediminas Technical University, Sauletekio av. 11, Vilnius, LT-10223, Lithuania b Principal Vice-Rector of Vilnius Gediminas Technical University, Sauletekio av. 11, Vilnius, LT-10223, Lithuania Received 14 September 2005; received in revised form 28 June 2006; accepted 28 June 2006 Available online 12 September 2006

Abstract Technological innovations through changes in the availability of information technology inclusive information systems, neural networks, decision support and expert systems, e-commerce that have been provided by a variety of new services were developed by the construction sector. Most of all construction on-line systems seek to find how to make the most economic decisions. Construction alternatives have to be evaluated not only from the economic position, but also take into consideration qualitative, technical, technological and other characteristics. Based on an analysis of the construction on-line systems the authors of paper developed multiple criteria decision support on-line system for construction. r 2006 Elsevier Ltd. All rights reserved. Keywords: Construction on-line systems; New multiple criteria analysis methods; Construction alternatives; Decision support on-line system

1. Introduction Thousands of various purpose construction Web sites (Architecture and Design, Building Process, CAD Software, Codes and Permits, Contractors and Suppliers, Investment Services, Mortgages Services, Development, Marketing, Consulting, Insurance and Inspection Services, etc.) can be found on the Internet. Different types of Webbased information systems, neural networks, expert and decision support systems, e-commerce are possible to be found on the above types of construction Web sites. However, most of all these construction on-line systems for various calculations use only economic information. The objects of this research are construction decisions comprising of the whole the project of a building’s life cycle as well as its separate parts e.g. constructive, technological, administrative, management, economical and investment etc. For instance, it might be used for the analysis of separate constructive decisions (of walls, roof, windows), analysis of interested parties (competitors, suppliers, contractors, etc.), determination of efficient loans, analysis and selection of rational refurbishment versions (e.g. roof, Corresponding author. Tel.: +370 5 2745234; fax: +370 5 2745235.

E-mail address: [email protected] (A. Kaklauskas). 0952-1976/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.engappai.2006.06.009

walls, windows, etc.), multiple criteria analysis and the determination of market value of a real estate property (e.g. residential houses, commercial, office, warehousing, manufacturing and agricultural buildings, etc.), analysis and selection of a rational market and the determination of efficient investment versions, etc. This paper is structured as follows: following this introduction, Section 2 outlines application of information systems, neural networks, expert and decision support systems, e-commerce at national, organization and project levels. In Section 3 we describe the possibilities to increase construction on-line system’s efficiency by the application of a multiple criteria on-line system decision support for construction. Methods developed by the authors that are used in the system’s model-base are introduced in Section 4. The practical possibilities of the system are presented in Section 5 and there is a case study in Section 6. Finally, some concluding remarks are provided in Section 7. 2. Application of information systems, neural networks, expert and decision support systems, e-commerce at national, organization and project levels Construction information technology (IT) can be used on the national, organization and project levels.

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On the national level IT may be used to disseminate the information on laws, norms, standards, technical issues, the results of research and experiments, contract offers, foreign experience, etc. IT enables one to find and analyze the required data quickly and from various perspectives, as well as allowing effective communication between interested parties (i.e. videoconferences, e-mail, etc.) for making important decisions at a distance. IT at country or European levels have been analyzed and written about in a variety of research literature. For example, Hassan and McCaffer (2002), McCaffer and Hassan (2000) report on findings from a project funded by the European Commission entitled European large scale engineering (LSE) wide integration support effort (eLSEwise) and present business and Information and Communication Technology (ICT) trends in the European LSE construction industry. As an example, they performed investigation into clients’ views and perceptions of the European Large Scale Engineering ICT environment and showed that the most important systems currently for client organizations are finance and accounting, project planning and human resources. Also, there is an anticipated increase in the importance of project planning, quality assurance (QA) systems and document control, e.g. material procurement, computer aided decision (CAD) systems and communications systems during the next 10 years (Hassan and McCaffer, 2002). Liao et al. (2002) demonstrated how the electronic Taiwan government procurement system functions and reengineers its internal procurement processes, which in turn benefits both government bodies and venders. ProcureZone is (ITC Software, 2004) a ‘‘One-Stop Procurement Resource and Hub’’ that simplifies the purchasing process for buyers and suppliers throughout the construction industry. On the organizational level IT is used to get and process the data on financing and investment possibilities (videotext) as well as on real estate (GIS—Geographical Information System), government-provided services (expert systems), producers and their products (electronic catalogues), etc. For example, organizations using telecommunication networks and IT get the possibility to search for most suitable suppliers, contractors and use market offers for the best advantage. IT also provides the opportunities for learning and qualification improvement (by distance learning, the availability of electronic library). For example, a recent European survey (Hassan and McCaffer, 2002) has highlighted the need for electronic sharing of information between large scale engineering (LSE) clients’ information systems and those of funding bodies in the areas of finance and accounting; consultants in the areas of modelling and calculations; project managers in the areas of project planning and QA systems and document control; contractors in areas of CAD drawings, materials procurement, project planning, QA systems and documents monitoring, and communication systems; and suppliers in the area of material procurement.

On the project level the use of information and artificial intelligence systems (i.e. expert, decision-support systems, etc.) allows all the interested parties, (i.e. designers, economists, architects, builders, facilities management personel) to solve the problems concerning the life time of a building (including brief, design, construction, maintenance, facilities management and demolishing stages). For this purpose databases are being created embracing the data and knowledge obtained from the previously made similar projects as well as the experience of experts and the data contained in various norms, standards and other sources of information. Cheung et al. (2004) describe the development of a webbased construction project performance monitoring system (PPMS) that aims at assisting project managers in exercising construction project monitoring. With the aid of a panel of project management specialists, the following project’s performance measure categories that are identified for inclusion in the PPMS: People, Cost, Time, Quality, Safety and Health, Environment, Client Satisfaction, and Communication. Alshawi and Ingirige (2003) briefly explain the main features of the currently available web-based software, which comes under the umbrella of web-enabled project management tools: tender stage (the main functions of this type of software are to advertise and distribute tender documents, select successful tenders and award contracts), design and construction stage (project manager’s monitor and manage the exchange of documents between members of the project team so that the overall deadlines of the project are met), Trading/e-commerce (purchasing of materials is a lengthy and complex process, which requires the identification of considerable resources and potential suppliers as well as the evaluation of quotes that are normally received in different formats). Husin and Rafi (2003) review current available Internetbased computer-aided design tools and explore the possible utilization of these in architectural practices. Expert systems today generally serve to relieve a ‘human’ professional of some difficult but clearly formulated tasks. Expert systems are most frequently applied at project level. More seldom they are applied at organization level, and much more rarely at construction industry level. According to Mohan (1990), the next construction areas are covered by expert systems: project planning, scheduling, and control, project management, construction methods, equipment management, legal issues, human resource management, concrete mixing and placement and temporary-facilities layout. Further, particular fields are given in which expert systems help to solve different problems: e.g. automated schedule updating, layout of temporary construction facilities, safety analysis, predicting time and cost of construction during initial design, cost estimating from preliminary design, masonry construction, differing site conditions analysis, strategic planning of construction alternatives, construction risk identification, vertical construction schedules and failure diagnosis, etc. Web-based

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training tools on construction topics (Cheung et al., 2004) are highly illustrated and utilize graphical menus as well as expert system modules. These modules (construction, evacuation plans and procedures, ergonomics, respiratory protection, safety and health management, scaffolding, steel erection, wood products, woodworking, etc.) enable the user to answer questions, and to receive reliable advice on how OSHA (Occupational Safety and Health Administration) regulations apply to a work site. Expert Advisors (asbestos, confined space, fire safety, hazard awareness, lead in construction, etc.) are based solely on expert systems (Nobe et al., 1999). Decision support systems are rarely applied at organization level and are hardly applied in a construction branch. The most samples of application are traced at project level. For example, the hybrid computerized decision support system for construction quality assessment (Emerging Construction Technologies, 2004) that automate the assessment process by acquiring digital images of the areas to be assessed and analyze the images to identify and measure defects. By means of using advanced technologies such as digital cameras, optical scanners, gyroscopic technology, machine learning, pattern recognition, and image processing, the hybrid computerized decision support system for construction quality assessment (Husin and Rafi, 2003) can produce objective, quantitative, and reliable results of assessment, and can reduce the time needed to interpret the results. Neural network technology has been successfully applied to a wide range of real-world construction applications, such as (Mohan, 1990): cost management, quality control, signal processing, credit rating, sales forecasting, modelling, quality control, portfolio management, targeted marketing and education, finance, etc. Many e-commerce systems for construction material procurement have emerged in the past few years. These systems have become the e-trading marketplaces for manufacturers, suppliers, agents and purchasers for buying and selling construction materials. Owners of these e-commerce systems vary from manufacturers, suppliers, agent companies, or even application service providers (Kong et al., 2004). According to Kong et al. (2004), construction material information in current e-commerce systems is isolated without interaction with one other and purchasers usually cannot find all the required information (material information, supplier information, manufacturer information, buyer information, agent information, and market information such as the amount of sales of different materials, buying patterns, buyers’ comments on materials and services, etc.) and material from one system or another. Kong et al. (2004) expected that by enabling information sharing between different parties in the construction material procurement process one can facilitate improved information communication and coordination, have better strategic planning and decision making, and rapidly and flexibly supply chain management.

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On analysing Web-based systems (information systems, neural networks, expert and decision support systems, e-commerce) applied world wide from decision support aspect, it is possible to notice in construction that they supply full-scale information needful for decision support. However, most of all neural networks, decision support and expert systems are seeking to find how to make the most economic construction decisions, and most of all these decisions are intended only for economic objectives. These propositions reflect the fact that qualitative criteria are rather disregarded in automated evaluations of intelligent systems created for the construction branch in the world currently. Quite a number of decision making methods (including all multiple criteria decision making methods) may make integrated analyses of many indicators of different dimensions. However, for various reasons they are not made by many computer systems developed in the construction branch. Several main reasons behind this fact are provided below: construction is a very conservative branch of economy and it is difficult to introduce innovations (e.g. intelligent technologies); quantitative factors (price, construction duration) are considered more important in construction than qualitative factors (sound insulation, heat conduction, environment pollution), because it is considered that construction quality must be granted by quality management systems; decision support methods are rather rarely applied in construction; it is difficult to develop databases and knowledge bases with integrated quantitative and qualitative information and knowledge, etc. Construction alternatives under evaluation have to be evaluated not only from the economic position, but take into consideration qualitative, technical, technological, comfort and other factors as well. Construction alternative solutions allow for a more rational and realistic assessment of economic, technical, technological conditions and traditions and for more satisfaction of different customer requirements. Therefore, application of multiple criteria analysis methods and multiple criteria decision support systems can increase the efficiency of construction process. 3. Increase of efficiency of construction on-line systems Based on the analysis of above Web-based systems and in order to determine the most efficient versions of construction alternatives a multiple criteria decision support on-line system for construction (OLSC) consisting of a database, database management system, model-base, model-base management system and user interface was developed by the authors of this paper. Database management system is designed to support query and analytical modelling by integrating different types of data. The model-base management system is intended for incorporation of analytical models in OLSC. It permits large numbers of models to be made accessible very quickly. The model-base management system also contains information about how steps are sequenced to

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execute a given algorithm. Besides such OLSC components as the database, the database management system, the model-base and the model-base management system a very important role is played by the user interface. The user interface not only determines successful user’s work but also joins all aforementioned components and determines successful operation of the whole system largely.

factors include an account of the economic, aesthetic, technical, technological, comfort, comfort and other factors. The model-base of a decision support system should include models that enable a decision-maker to do a comprehensive analysis of the available variants from a database and to make a proper choice. The following models of a model-base aim at performing the functions of:

3.1. Database

 

The presentation of information needed for decisionmaking in the OLSC may be in a conceptual form (digital/ numerical, textual, graphical, diagrams, graphs and drawing, etc), photographic, sound, video) and quantitative forms. Therefore, the presentation of quantitative information involves criteria systems and subsystems, units of measurement, values and initial weights that fully define the provided variants. Conceptual information means a conceptual description of the alternative solutions and the criteria and ways of determining their values and weights, etc. In this way, the OLSC enables the decision-maker to receive varied conceptual and quantitative information on construction alternatives from a database and a modelbase allowing him/her to analyze the above factors and to make an efficient solution. The analysis of database structures in decision support systems according to the type of problem solved reveals their various utilities. OLSC has a relational database structure when the information is stored in the form of tables. These tables contain quantitative and conceptual information. Each table is given a name and is saved in the computer’s external memory as a separate file. Logically linked parts of the table form a relational model. To design the structure of a database and perform its completion, storage, editing, navigation, searching and browsing, etc. a database management system was used in the OLSC. The interested parties have their specific needs and financial situation. Therefore, each time when using the OLSC the parties can make corrections of the database according to their aims and their financial situation. For example, a certain client considers the sound insulation of the external walls to be more important than their appearance while another client is of the opposite opinion. The client striving to express his/her attitude towards these issues numerically may ascribe various significance values to them that eventually will affect the general estimation of a refurbishment alternative. Though this assessment may seem biased and even subjective, the solution finally made can meet the client’s requirements, aims and affordability exactly. 3.2. Model-base The efficiency of a construction variant is often determined by taking into account many factors. These

  

A model for the establishment of the criteria weights. A model for multiple criteria analysis and for setting the priorities. A model for the determination of an alternative’s utility degree. A model for the determination of an alternative’s market value. The model for presentation of recommendations.

According to the user’s needs, various models may be provided by a model management system. When a certain model (i.e. search for construction alternatives) is used, the results obtained become the initial data for some other models (i.e. a model for multiple criteria analysis and setting the priorities). The results of the latter, in turn, can be taken as the initial data for some other models (i.e. determination of utility degree of market, suppliers, contractors, renovation of walls, windows and roof, etc.) (Kaklauskas, 1999). The management system of the model-base allows user to select desired additional models related to the existing models provided by a system administrator (Kaklauskas and Zavadskas, 2002). 4. Methods used in the system model-base Since the analysis of construction alternatives is usually performed by taking into account economic, quality, technical, technological, comfort and other factors, a model-base should include models which will enable a decision maker to carry out a comprehensive analysis of the variants available in database and make a proper choice. The multi-criteria analysis methods are best suited for the achievement of this goal. Various authors analyze different possibilities to apply multi-criteria analysis methods. Zavadskas et al. (2004a) have analysed possibilities to apply Electre III method in the evaluation of the effectiveness of investments to commercial objects. The authors note that during the evaluation of the effectiveness of investments to commercial objects, the total effect of various criteria must be evaluated: the amount and trends in the construction of commercial objects, legal issues, possible construction solutions. Possibilities to apply various game theory methods while making decisions in the construction sector were analysed by Zavadskas et al. (2002). The authors have developed software that enables calculations applying simple min–max principle, extended min–max principle, Wald’s

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rule, Savage criteria, Hurwicz’s rule, Laplace’s rule, Bayes’s rule and Hodges–Lehmann’s rule. This software provides an investment to the construction or renovation of a residential house in Nida as an example. But for selecting construction materials is not always the best choice. For this purpose authors selected multiple criteria analysis methods developed by the authors (Zavadskas et al., 1995, 1998). Those methods are used by the OLSC in the analysis of the construction alternatives and are as follows:

    

their significance and values. The weights of quantitative criteria can be coordinated if the values of the quantitative criteria are expressed through an equivalent monetary unit (Stages 1–4). Having performed a strict mutual coordination of the quantitative criteria weights, the same coordination is done with the weights of the qualitative criteria (Stages 5–7). Stage 1: The determination of the sum of values for every quantitative criteria according to Si ¼

Method of complex determination of the weight of the criteria taking into account their quantitative and qualitative characteristics. Method of multiple criteria complex proportional assessment (COPRAS) of the alternatives. Method of determining the utility degree and market value of alternatives based on the complex analysis of all their benefits and drawbacks. The method for presentation of recommendations. The methods used for framing the model-base OLSC are described further on, in this paper.

Different authors applied these methods for the achievement of various goals. Kvederyte (2000) has used this method when had analysed the efficiency of a life cycle of an individual house and has proposed a model complex analysis. Banaitis (2000) recommends applying these methods in a model of rational housing. Kaklauskas et al. (2005) already used COPRAS method for multivariant design and multiple criteria analysis of building refurbishment. COPRAS method was used in preparation of housing credit access model for Lithuania (Zavadskas et al., 2004b). 4.1. Grouped decision making matrix of construction alternative’s multiple criteria analysis The results of the comparative analysis of construction alternatives are presented here as a grouped decision making matrix where columns contain n alternatives, while all quantitative and conceptual information pertaining to them are found in Table 1. Any alternative that has a criteria value worse than the required level was rejected. In case analysed by the authors, the Grouped Decision Making Matrix is designed for analysis of quantitative and conceptual information. Therefore, it is suitable only for database management system in this instance. 4.2. Determination of the criteria’s weights Taking into account their quantitative and qualitative characteristics, we developed a new method for the complex determination of the weight of the criteria. This method allows one to calculate and coordinate the weights of the quantitative and qualitative criteria according to

167

n X

xij ;

i ¼ 1; t;

j ¼ 1; n,

(1)

j¼1

where xij is the value of the i criteria in the j alternative of a solution; t is the number of quantitative criteria; and n is the number of the alternatives compared. Stage 2: The total monetary expression of every quantitative criteria describing the investigated alternative is obtained by applying: Pi ¼ S i pi ;

i ¼ 1; t,

(2)

where pi is the initial weight of the i criteria. pi should be measured in such a way as, having been multiplied by a quantitative criteria value, an equivalent monetary expression can be obtained. According to the quantitative criteria’s effect on the efficiency of the alternative’s life cycle, the quantitative criteria can be divided into: 1. Short-term factors, affecting the alternative only for a certain period of time; 2. Long-term factors, affecting the alternative throughout its life cycle. The initial weights of long-term criteria, such as resources needed for the maintenance and environmental protection depends on the alternative’s repayment time and on the evaluation, in financial terms, of a criteria’s unit of measure and is pi ¼ ef i ,

(3)

where e is repayment time of an alternative; and fi is monetary evaluation of a measure unit of the i criteria. The initial weight of a single criteria comprising of, for example, the cost of an alternative, is equal in financial terms to the criteria’s unit of measure and is pi ¼ f i .

(4)

The meaning of the initial weight of a quantitative criteria consists of multiplying the initial weight by the value of a quantitative criteria and its monetary expression is calculated over the whole period of the alternative’s repayment (equivalent to former natural expression). Stage 3: The overall quantitative criteria magnitude’s sum expressed in financial terms is determined by V¼

t X i¼1

Pi ;

i ¼ 1; t.

(5)

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Table 1 Grouped decision making matrix of construction alternative’s multiple criteria analysis Quantitative information relevant to construction alternatives Criteria describing the construction alternatives

a

Weights

Measuring units

X1 z1 q1 m1 X2 z2 q2 m2 y y y y Xi zi qi mi y y y y Xt zt qt mt Qualitative criteria Xt+1 zt+1 qt+1 mt+1 Xt+2 zt+2 qt+2 mt+2 y y y y Xs zs qs ms y y y y Xm zm qm mm Conceptual information relevant to alternatives (i.e. text, drawings, graphics, video tapes) Cz Cq Cm Cf Quantitative criteria

a

Comparable construction alternatives a1

a2

y

aj

y

an

x11 x21 y xi1 y xt1 xt+1 xt+2 y xs1 y xm1

x12 x22 y xi2 y xt2 xt+1 xt+2 y xs2 y xm2

y y y y y y y y y y y y

x1j x2j y xij y xtj xt+1 xt+2 y xsj y xmj

y y y y y y y y y y y y

x1n x2n y xin y xtn xt+1 xt+2 y xsn y xmn

y

Cj

y

Cn

C1

1 1

C2

2 2

j j

n n

The sign zi (7) indicates that a greater/lesser criteria value corresponds to a greater significance for interested parties.

Stage 4: The quantitative criteria weights describing the alternative, which can be expressed in financial terms, are determined as follows: qi ¼

Pi ; V

i ¼ 1; t.

(6)

When the above method is applied in the calculation of weights, the total sum of weights of the quantitative criteria is always equal to 1: t X

qi ¼ 1.

(7)

i¼1

Stage 5: In order to achieve full coordination between the weights of quantitative and qualitative criteria, a comparative standard of value (E) is set. E is equal to the sum of any selected weights of quantitative criteria. One of the main requirements for this comparative standard value is that according to the utility, E should be easily comparable to all the qualitative criteria. The weights of all the qualitative criteria are determined by the comparison of their utility with the standard value. E is determined according to the following equation: E¼

g X

qz ,

(8)

z¼1

where g is the number of quantitative criteria and is included into the compared standard; qz is the weight of z quantitative criteria and is included into the compared standard. Stage 6: The initial weight vi of qualitative criteria is determined by using expert methods that compare their relative significance to the significance E of the selected

compared standard. Relative weights of qualitative criteria should be expressed in percentages. Stage 7: The weight of the i qualitative criteria is determined as follows: ni E ; i ¼ t þ 1; . . . ; m. (9) 100 The above method allows for the determination of weights of the criteria that are maximally interrelated and depend on qualitative and quantitative characteristics of all criteria. Therefore, equivalence can be drawn between the notes of qualitative aspects and the costs of the quantitative aspects, after the establishment of the weight of each criteria. qi ¼

4.3. Method of multiple criteria complex proportional assessment (COPRAS) The method of complex proportional assessment (Zavadskas et al., 1995, 1999, 2001) assumes direct and proportional dependence of the significance and utility degree of the investigated versions in a system of criteria adequately describing the alternatives and of values and weights of the criteria. A decision maker by using the experts’ methods determines the system of criteria and calculates the values and initial weights of the qualitative criteria. The determination of significance, priority and utility degree of alternatives is carried out in five stages. Stage 1: The weighted normalized decision-making matrix D is formed at this stage. The purpose here is to receive dimensionless weighted values from comparative indexes. When the dimensionless values of the indexes are known then all criteria can be compared.

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The following equation is used for this purpose: xij q d ij ¼ Pn i ; i ¼ 1; m; j ¼ 1; n, j¼1 xij

(10)

where xij is the value of the i criteria in the j alternative; m is the number of criteria; n is the number of the alternatives compared; and qi is weight of i criteria. The sum of dimensionless weighted index values dij of each criteria xi is always equal to the weight qi: qi ¼

n X

d ij ;

i ¼ 1; m;

j ¼ 1; n.

(11)

j¼1

In other words, the value of weight qi of the investigated criteria is proportionally distributed among all alternative versions aj according to their value xij. Stage 2: The sums of weighted normalized indexes describing the jth version are calculated. The versions are described by minimizing indexes Sj and maximizing indexes S+j. The lower the value of the minimizing indexes such as the price of an alternative, the better the attainment of goals. Further, the greater the value of maximizing indexes such as quality, the better attainment of goals. Sums are calculated according to S þj ¼ S j ¼

m X i¼1 m X

d þij ; d ij ;

i ¼ 1; m;

j ¼ 1; n.

ð12Þ

The greater the value S+j then there is more satisfaction of the interested parties. The lower the value Sj the better the attainment of goals of interested parties. S+j and Sj express the degree of goals attained by the interested parties in each alternative. In any case the sums of ‘pluses’ S+j and ‘minuses’ Sj of alternatives are always respectively equal to the sums of weights of maximizing and minimizing criteria: Sþ ¼

n X

S þj ¼

j¼1

S ¼

n X j¼1

of interested parties will be satisfied to a smaller extent than in the case of the best alternative. Relative significance Qj of each alternative aj is found according to P S min nj¼1 Sj P ; j ¼ 1; n. (14) Qj ¼ Sþj þ Sj nj¼1 ðS  min =S j Þ It is assumed that people can measure values of various alternatives, in terms of the so-called utility. Each alternative has its consumer or other interested party’s utility. In the proposed method, the utility of alternatives is measured quantitatively. The degree of the alternative’s utility is directly associated with the quantitative and conceptual information related to the alternative. If one alternative is characterized by the highest quality level and price indices, while other alternatives show better maintenance characteristics, having obtained the same significance values as a result of multiple criteria evaluation, then this means that their utility degree is also equal. With the increase/decrease of the significance of an analyzed alternative, it was found that, its degree of utility also increases/decreases. The degree of alternative utility is determined by comparing the analysed alternatives with the most efficient alternative. All the values of the utility degree related to the analyzed alternatives will range from 0% to 100%. Stage 5: Utility degree Nj of alternative aj is calculated as N j ¼ ðQj : Qmax Þ 100%,

i¼1

m X n X

d þij ,

i¼1 j¼1

S j ¼

m X n X

d ij ;

i ¼ 1; m;

j ¼ 1; n.

ð13Þ

i¼1 j¼1

In this way, the calculations may be additionally checked. Stage 3: The significance of comparative alternatives is determined on the basis of describing positive alternatives S+j and negative alternatives Sj characteristics. Stage 4: Determination of alternative priorities. The greater Qj the higher is the priority of the alternative. Significance Qj of alternative aj indicates the satisfaction degree of demands and goals pursued by the interested parties. In this case, the significance Qmax of the most rational alternative will always be the highest. The significance of all remaining alternatives is lower compared to the most efficient alternative. Total demands and goals

169

(15)

where Qj and Qmax are the significance of alternatives obtained from Eq. (14). In order to find what price will make an alternative of that which is being valuated, competitive on the market, a method for determining the market value of alternatives based on the complex analysis of all their benefits and drawbacks was suggested. According to this method the alternatives market value of an alternative that is being estimated are directly proportional to the system of the criteria that adequately describes them and the values and weights of these criteria. This method and its practical application have been described in several publications (Zavadskas et al., 2001). Following the performed analysis of different multiple criteria decision making methods (TOPSIS, SAW, etc.) is it possible to make a conclusion that these methods do not show in what percent one alternative is better than another one. The suggested methods solve this problem. It is a task of the degree of utility. The degree of utility Nj of the alternative aj indicates the level of satisfying the needs of the parties interested in the project. The more goals are achieved and the more important they are, the higher is the degree of the project utility. For example, the significance of the difference between the utility degree of first alternative (N 1 ¼ 100%) and the fifth alternative (N 5 ¼ 87:75%) shows that first alternative is more useful that fifth alternative by 12.25% (see Table 4). Having

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170

Table 2 Alternative’s multiple criteria analysis results Criteria under evaluation

X1 X2 y Xi y Xm The sums of weighted normalized The sums of weighted normalized Significance of the alternative Alternative’s priorities Alternative’s utility degree (%) a

Measuring units

a

m1 z1 m2 z2 y y mi zi y y mm zm maximizing indices of the alternative minimizing indices of the alternative

q1 q2 y qi y qm

1

2

y

j

y

n

d11 d21 y di1 y dm1 S+1 S–1 Q1 Pr1 N1

d12 d22 y di2 y dm2 S+2 S–2 Q2 Pr2 N2

y y y y y y y y y y y

d1j d2j y dij y dmj S+j S–j Qj Prj Nj

y y y y y y y y y y y

d1n d2n y din y dmn S+n S–n Qn Prn Nn

The sign zi (7) indicates that a greater/lesser criteria value satisfies interested parties.

calculated by what percent one alternative is better that another one, the developed new methods allow solving a lot of other problems. For example, it may be used as a basis for determining real estate market value (Table 2). The application of a Multiple Criteria Decision Support On-Line System for Construction (OLSC) allows one to determine the strengths and weaknesses of the alternatives. Calculations were made to find out by what degree one version is better than another and the reasons disclosed why it is so. Landmarks have been set for an increase in the efficiency of construction versions. All this was done argumentatively, based on indexes that were under investigation, on their values and weights and on conceptual information. This saved the users’ time considerably by allowing them to increase both the efficiency and quality of construction alternatives analysis. The method for the presentation of recommendations, offered by the authors, is used for the analysis of alternatives and for the preparation of recommendations. This has been described in several publications by Zavadskas and Kaklauskas (1996, 2000). 5. Practical possibilities of the system





At present, the developed OLSC allows for the performance of the following functions:



Comparable alternatives (matrix D)

Weights

Search of construction products and services: A consumer may perform a search for alternatives from catalogues of different suppliers and producers (services providers). This is possible since the forms of data submitted are standardized into specific levels. Such standardization creates conditions that can be used by special intelligent agents who perform a search of the required construction products (services) from various catalogues, and gather information about the products (services). All suppliers of construction materials can easily provide the required data in a certain standard form of the created OLSC. Such data in a standard form facilitate



search and selection of necessary information (see Fig. 1). However, one or several regions can limit such a search. Finding alternatives and making comparative tables: Consumers specify requirements and constraints and the OLSC queries the information of specific construction products (services) from a number of online vendors (services providers) and returns a price-list and other characteristics that best meets the consumer’s desire. The OLSC performs the tedious, time-consuming, and repetitive tasks of searching databases, retrieving and filtering information, and delivering the information back to the user. Results of a search of specific construction products (services) are submitted in tables, which may include direct links to a Web page of a supplier or producer (services provider). Also, by submission, such a display of the multiple criteria comparisons can become more effectively supported. The results of the search of a concrete construction product (service) are often provided in one table where one can sometimes find direct links to the Web page of the supplier or manufacturer (services provider). Evaluation stages of alternatives: (i.e. multiple criteria analysis of alternatives and selection of most efficient ones). While going through the purchasing decision process a customer should examine a large number of alternatives, each of which is surrounded by a considerable amount of information (for example, information about windows would comprise the following characteristics: price, discounts given, thermal insulation, sound insulation, rate of harm to human health of the product, aesthetic, weight, physical and moral longevity and period of guarantee). Following on from the gathered information, the priority and utility degree of alternatives is then calculated. This helps consumers to decide what product best fits their requirements. The after-purchase evaluation stage: A consumer evaluates the usefulness of the product in the after-purchase evaluation stage.

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Fig. 1. Standard form for providing data about construction materials.



Other typical construction tasks solved by users are: J analysis of interested parties (competitors, suppliers, contractors, etc.), J determination of efficient loans, J analysis and selection of rational refurbishment versions (e.g. roof, walls, windows, etc.), J multiple criteria analysis and determination of market value of a real estate (e.g. residential houses, commercial, office, warehousing, manufacturing and agricultural buildings, etc.), J analysis and selection of a rational market, J determination of efficient investment versions, etc.

6. Case study Residents of a multi-apartment dwelling wanted to change the windows of their apartments. All windows of the apartments are of the same dimensions. A consumer can perform a search for alternatives from different suppliers databases. This is possible since the forms of data submissions are standardized at a specific level. Such standardization creates the conditions for use by special intelligent agents performing a search for the required construction material in various databases, and for gathering information about them. Consumers specify requirements and constraints (see Fig. 2) and the OLSC system queries the information of a specific window from a number of online suppliers. Results of the search for specific windows are submitted in tables, which may include direct links to a Web page of suppliers. By the submission of such a display, the multiple criteria comparisons can become more effectively supported. A presentation of information in different types of windows in Web pages can be in conceptual (digital, textual, graphical, photographic, video) form. Conceptual information is needed to make a more complete and accurate valuation of the window alternatives that are being considered. Conceptual description of the first alternative of window is presented in Fig. 3. Residents are interested not only in the price of windows, but in their quality as well. Since the dwelling is near a

noisy crossroad, sound insulation of a window became an urgent priority. There is a tendency for regular increases of the price for fuel, and winters are cold in Lithuania and therefore all consumers give greater importance to the thermal insulation of windows. A guarantee period of windows is also important, as are other factors. Five suppliers (Hronas, Doleta, Roda, Alseka, Staliu gaminiai) were found after making a search for the required alternative windows and it was found that they offered nine alternative windows (see Fig. 4). Decision-making matrix formed in the second stage, shows found alternatives. Based on this decision-making matrix it was possible to define the most efficient variant. Each criteria goes together with its measurement unit and weight. The magnitude of weight indicates how many times one criteria is more significant than the other, in a multiple criteria evaluation of window refurbishment (Trinkunas, 2001). The calculations revealed that the key factors that have affected the efficiency of window refurbishment are: cost (weight q1 ¼ 1:0), heat conductivity (q2 ¼ 0:54), sound insulation (q3 ¼ 0:21), guarantee period (q6 ¼ 0:12) and more. The quantitative information presentation involves criteria systems and subsystems, units of measurement, values and the initial weight, which fully define the provided variants. Quantitative information of windows is submitted in the form of a grouped decision making matrix, where the rows mean n windows under valuation, and columns include quantitative information. In this way, the system enables the decision maker to receive varied conceptual and quantitative information on windows from a database and a model-base, allowing him/ her to analyze the above factors and decide on an efficient solution. Since the analysis of windows is usually performed by taking into account economic, quality, technical, technological, legal, social and other factors, a model-base includes models which enable a decision maker to carry out a comprehensive analysis of the available variants and so make a proper choice. In order to demonstrate parallel adjustment of the theoretical knowledge presented in Section 4 with results of computer computations, detailed explanations of the

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Fig. 2. Consumers specify requirements and constraints and the OLSC system queries the information of a specific window from a number of online suppliers.

Fig. 3. Conceptual description of the first alternative of window.

obtained results will be provided. Thus, the data provided in Table 3 are similar to those provided in Fig. 4; however, the former are clearer, because information not used in calculations directly is discarded.

From Table 3 we see that each criteria unites with its measurement unit and weight. The magnitude of weight indicates how many times one criteria is more significant than another criteria. The key factors which affected the

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Fig. 4. Five suppliers were found after making a search of the required alternative windows.

Table 3 Initial data for windows alternatives multiple criteria analysis Criteria under evaluation

1. 2. 3. 4. 5. 6. 7. 8. 9.

Price of the window Heat conductivity Sound insulation Light permeability Light reflection Guarantee period Delivery period Imprest Installation price a

a

  + + + +   

Weights

1.00 0.90 0.50 0.40 0.20 0.30 0.30 0.30 1.00

Measuring units

Lt W/m2 K dB % % Years Days % %

Comparable windows alternatives 1

2

3

4

5

6

7

8

9

420 2.8 28 0.89 0.8 5 7 50 20

661 2.8 28 0.89 0.8 5 7 50 20

686 2.8 28 0.89 0.8 5 7 50 20

637 2.8 28 0.89 0.8 5 7 50 20

963 2.8 28 0.89 0.8 5 7 50 20

989 2.8 28 0.89 0.8 5 7 50 20

800 1.1 32 0.77 0.12 3 25 30 20

1150 1.1 32 0.77 0.12 3 25 30 20

1520 1.1 32 0.77 0.12 3 25 30 20

The sign zi (7) indicates that a greater/lesser criteria value corresponds to a greater significance for interested parties.

efficiency of windows (see Table 3): price of the window (q1 ¼ 1:00); installation price (q9 ¼ 1:00); heat conductivity (q2 ¼ 0:90); sound insulation (q3 ¼ 0:50); and light permeability (q4 ¼ 0:40). The results of a multiple criteria evaluation of nine windows alternatives are presented in Table 4. Table 4 shows that the first version is the best in the utility degree equalling 100%. The seventh version was second according to priority and its utility degree was equal to 98.56%. While going through the purchasing decision process, a customer should examine a large number of alternatives, each of which is surrounded by a considerable amount of information. Following on from the gathered information, the multiple criteria analysis of windows is then carried out (see Fig. 5).

In order to make a reasoned decision, thorough and based calculations are required. As in many other cases, computer technology is especially useful here: it helps to obtain calculations provided in Table 4 very quickly (see Fig. 5). 7. Testing the system In order to test the usefulness of the system, final semester master degree students from the Construction Economics and Business program at Vilnius Gediminas Technical University developed database of construction materials. These students work as managers in various construction materials suppliers companies in Vilnius. They have placed information about construction

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Table 4 Results of windows alternatives multiple criteria analysis Criteria under evaluation

*

Weights

Measuring units

Price of the window  1.00 Lt Heat conductivity  0.90 W/m2 K Sound insulation + 0.50 dB Light permeability + 0.40 % Light reflection + 0.20 % Guarantee period + 0.30 Years Delivery period  0.30 Days Imprest  0.30 % Installation price  1.00 % Sums of weighted normalized maximizing indices of variant S+j Sums of weighted normalized minimizing indices of variant S–j Significance of variant Qj Priority of variant Project’s utility degree Nj (%)

Comparable windows alternatives 1

2

3

4

5

6

7

8

9

0.054 0.125 0.053 0.047 0.031 0.038 0.018 0.038 0.111 0.169 0.347 0.5908 1 100

0.084 0.125 0.053 0.047 0.031 0.038 0.018 0.038 0.111 0.169 0.377 0.5555 4 94.01

0.088 0.125 0.053 0.047 0.031 0.038 0.018 0.038 0.111 0.169 0.381 0.5521 5 93.44

0.081 0.125 0.053 0.047 0.031 0.038 0.018 0.038 0.111 0.169 0.374 0.5587 3 94.56

0.123 0.125 0.053 0.047 0.031 0.038 0.018 0.038 0.111 0.169 0.416 0.5185 7 87.75

0.126 0.125 0.053 0.047 0.031 0.038 0.018 0.038 0.111 0.169 0.419 0.5156 8 87.27

0.102 0.049 0.061 0.040 0.005 0.023 0.064 0.023 0.111 0.129 0.350 0.5823 2 98.56

0.147 0.049 0.061 0.040 0.005 0.023 0.064 0.023 0.111 0.129 0.394 0.5336 6 90.31

0.194 0.049 0.061 0.040 0.005 0.023 0.064 0.023 0.111 0.129 0.442 0.4928 9 83.41

Fig. 5. Multiple criteria analysis of window alternatives and the selection of the most efficient ones.

materials that they were selling into the database. This system has been tested by 12 students for areas that could be improved, e.g. process, interface, navigation, search for alternatives from different suppliers’ databases, multiple criteria evaluations. A testing of multiple criteria decision support on-line system for construction (OLSC) was also performed by a designed questionnaire that included ten organizations from construction materials suppliers in Vilnius. The letter was attached to the questionnaire was as follows ‘‘We would like you to draw on your experience and expertise to help us to test whether the OLSC can also meet your needs as a user. Please read through the

following questions circling your response’’. A more complete study is underway to study the satisfaction of users and the current construction materials suppliers do in order to survive. If not, then what are the issues that prevent people from using such approach. A first functional OLSC prototype was developed in 2001, using existing information in order to start testing the main characteristics of the System together with prospective user groups in order to determine. Construction materials suppliers performed tests to ensure the desired results are achieved. When additional or improved modules were ready, they were included/exchanged in the OLSC.

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8. Conclusions The analysis of information, neural networks, expert and decision support systems, e-commerce used in construction, which were developed by practicians and users from various countries, assisted the authors to create their own multiple criteria decision support on-line system for construction (OLSC). The OLSC developed by the authors differs from other new multiple criteria analysis methods (method of complex determination of the weight of the criteria; method of multiple criteria complex proportional evaluation of the alternatives; method of determining the utility degree and market value of alternatives; the method for presentation of recommendations). A database of construction alternatives was developed and provides a comprehensive assessment of alternative versions from economic, technical, technological, qualitative and other perspectives. Based on the above database, the developed Multiple Criteria Decision Support On-Line System for Construction enables the user to analyze alternatives quantitatively (i.e. a system and subsystems of criteria, units of measure, values and weights) and conceptually (i.e. the text, formula, schemes, graphs, diagrams and videotapes). References Alshawi, M., Ingirige, B., 2003. Web-enabled Project Management: an Emerging Paradigm in Construction. Automation in Construction 12 (4), 349–364. Banaitis, A., 2000. Model of Rational Housing in Lithuania. Statyba (Civil Engineering) 6 (6), 451–456. Cheung, S., Suen, H.C.H., Cheung, K.K.W., 2004. PPMS: a Web-based Construction Project Performance Monitoring System. Automation in Construction 13 (3), 361–376. Emerging Construction Technologies, 2004. Hybrid Computerized Decision Support System for Infrastructure Assessment. /http://www. new-technologies.org/ECT/Other/imageprocess.htmS Hassan, T.M., McCaffer, R., 2002. Vision of the Large Scale Engineering Construction Industry in Europe. Automation in Construction 11 (4), 421–437. Husin, R., Rafi, A., 2003. The impact of Internet-enabled computer-aided design in the construction industry. Automation in Construction 12 (5), 509–513. ITC Software, 2004. Example of a Product Developed for Customers. /http://www.it-careernet.com/itc/procurezone.htmS Kaklauskas, A., 1999. Multiple Criteria Decision Support of Building Life Cycle. Research report presented for habilitation. Technika, Vilnius. Kaklauskas, A., Zavadskas, E., 2002. Web-Based Decision Support. Technika, Vilnius.

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