State of art surveys of overviews on MCDM/MADM methods.
Zavadskas, Edmundas Kazimieras ; Turskis, Zenonas ; Kildiene, Simona 等
Introduction
Humans make decisions all the time. Decision-making is a very
complex and difficult task. During the past decades, operations research
(OR) has come a long way as a field that supports scientific management.
OR mainly deals with model building and algorithmic optimization
procedures that facilitate the analysis of complex real-world problems
(Zopounidis, Pardalos 2010). Since von Neumann and Morgenstern (1947)
and Savage (1954), this has become the dominant paradigm in decision
analysis and decision support in the presence of multiple evaluation
dimensions. Traditional OR techniques fit the same purpose: they
maximise or minimize a utility function in the presence of constraints.
Multi-Criteria Decision Making (MCDM) can be said to be both old and new
part of OR, depending on one's frame of reference (Koksalan et al.
2011). MCDM has been one of the fastest growing problem areas in many
disciplines. The central problem is how to evaluate a set of
alternatives in terms of a number of criteria (Triantaphyllou 2010).
Many modern researchers have considered MCDM problems. MCDM refers to
making decisions in the presence of multiple, usually conflicting,
criteria. The past decades have seen a dramatic increase on all main
areas of MCDM:
--Formal models (algorithms, procedures and selection paradigms);
--Evaluation theories (assumptions about values or preferences and
structured representations of values or preferences);
--Assessment methodologies (elicitation, estimation and scaling of
individuals' preferences, utilities and subjective probabilities in
MCDM situations) (Fishburn 1978).
There is no unique and well-defined methodology that one could
follow step-by-step from the beginning to the end of a decision aiding
process. When dealing with objects that can only be described and
compared using several characteristics, aggregation is a major issue: it
aims at operating a synthesis of the, usually contradictory, features of
the objects, in view of achieving a goal such as choosing among the
objects, rank ordering them, sorting them into categories and so on
(Bouyssou et al. 2006).
MCDM methods cover a wide range of quite distinct approaches. MCDM
methods can be broadly classified into two categories: discrete MCDM or
discrete MADM (Multi-attribute Decision Making) and continuous MODM
(Multi-Objective Decision Making) methods (Fig. 1). A dictionary
definition of a "criterion" is "a means or standard of
judging" by which one particular choice or course of action could
be judged to be more desirable than another (h). Each problem has
multiple, usually conflicting objectives/criteria. Each
objective/criterion has a different unit of measurement. MCDM can be
perceived as a process of evaluating real-world situations based on
various qualitative/quantitative criteria in certain/uncertain/ risky
environments in order to find a suitable course of
action/choice/strategy/policy among several available options (Raju,
Kumar 2013).
[FIGURE 1 OMITTED]
MODM methods are associated with problems where alternatives are
non-predetermined and the aim of the problem under consideration is to
design the best/optimal alternative by considering a set of well-defined
design constraints, a set of quantifiable objectives. Thus, MODM methods
deal with the design process and the number of alternatives is infinite
(continuous). It is a constant challenge for designers to select the
best materials and constructions to satisfy complex design problems
(Jahan, Edwards 2013).
MADM art is interrelated with art of the Rational Choice Theory. It
assumes that people are motivated by money and by the possibility of
making a profit, and this has allowed constructing formal and often
predictive models of human behaviour. They act rationally within
specific given constraints and based on the information that they have
about the conditions under which they are acting. Human actions involve
both rational and non-rational elements (Scott 2000). Rational choice
theories maintain that individuals must anticipate the outcomes of
alternative courses of action and calculate that which will be best for
them. As it is not possible for individuals to achieve everything they
want, they must also make choices in relation to both their goals and
means for attaining these goals. Rational individuals choose the
alternative that is likely to give them the greatest satisfaction.
Although the expected utility model has many possible founders, Von
Neumann and Morgenstern (1947) are usually credited for the first
axiomatic foundation of expected utility measurement. Today, the
expected utility model is widely used as the normative cornerstone of
decision analysis (Keeney, Raiffa 1976).
1. Main ideas of overview
Zavadskas and Turskis (2011) published a review of MCDM methods.
This study looks at long known and relatively recently published
methods. Liou and Tzeng (2012) published an article, which was intended
to review the multi-criteria techniques that Zavadskas and Turskis
(2011) did not mention in their article. Lately, Liou (2013) summarized
the Tzeng's research work. Therefore, this gave rise to an idea to
investigate the existing situation with reviews on MCDM/MADM methods.
Discrete MCDM/MADM methods deal with discrete and predetermined
alternatives, which are described by a determined discrete criteria set.
The main task is:
--Rational selection among limited number of alternatives;
--Assessment and ranking of limited number of alternatives.
Recently, hundreds of publications have been published to provide
information about MCDM methods, their development and application in
different fields. This article provides an overview of the publication,
which provides an overview of MCDM methods. The research is based on Web
of Science database, which is a part of Thomson Reuters Web of
Knowledge. The 1970s was an important decade for many seminal works.
Foundations of modern MCDM were developed in 1950s and 1960s.
Development of MCDM researches accelerated during the 80s and early 90s,
and seems to have continued its exponential growth (Koksalan et al.
2011). Fig. 2 provides information about this from databases of
reference for review of MCDM methods and their application.
The overview provides systematically classified information on MCDM
reviews. They are grouped as shown in Fig. 3:
--Books on MCDM methods (Table 2);
--Articles on multi-criteria methods in scientific journals (Table
3);
--Articles on different MCDM approaches (Table 4);
--Comparative analysis of several MCDM methods (Table 5);
--MCDM review related with individual activity topics (Table 6).
[FIGURE 2 OMITTED]
[FIGURE 3 OMITTED]
2. Main results
Table 1 provides information about the scope of the MCDM methods
(Table 1). The total of 71 articles were found on the topic "MCDM
review papers" in ISI Web of Science database (December 2013).
However, there were only some articles that belonged to few different
fields of the research area (Table 1).
The book by Koksalan et al. (2011) provides a brief history of the
development of MCDM methods. It briefly describes the development of the
area from ancient to modern times. Keeney and Raiffa (1976) formulated
the basics of Decision with Multiple Objectives. Hwang and Masud (1979)
provided review on development of MODM methods and applications in a
relatively short period of time. Later, Hwang and Yoon (1981) reviewed
the MADM methods (SAW, TOPSIS, ELECTRE, LINMAP).
Saaty (1980) published a detailed study on the analytic hierarchy
process (AHP). Later, Saaty (1996) published a study on the further
development of the Analytic Network Process (ANP) method. Zeleny (1982)
published a book, which deals with the problem of compromise theory.
Hwang and Lin (1987) published a study for Group Decision Making under
multi-criteria. Roy (1996) summarized the information on ELECTRE group
methods. Seminal studies have been prepared by Belton and Stewart
(2002), Gal et al. (2009), Miettinen (2009). Brauers (2004) published a
study on the basis of which MOORA and MULTIMOORA methods were developed.
A great job was done by Figueira et al. (2005), Ehrgott et al. (2010),
editing wide studies in which well-known scientists in this area
published individual studies on different classes of MCDM methods.
Valuable studies were published by Triantaphyllou (2000, 2010).
Hanne (2009) and Kaliszewski (2010) published a detailed study on soft
computing intelligent strategies for Meta MCDM. Apparently, this is only
a part of all existing noteworthy studies.
A number of books have been published, which contain detailed
information about the MCDM approaches in separate specific areas of
research. As an outcome of the development and growing application of
MCDM methods, many specific subfields have emerged. Next, some of them
are named: Ballestero and Romero (2009) analysed MCDM and its
application to economic problems; Peldschus and Zavadskas (1997)
analysed an application of discrete matrix games theory in construction
and management; Chen and Li (2006) investigated applications of MCDM
techniques in environmental management of construction; Zavadskas et al.
(1994) applied MCDM in project construction; Venkata (2007) demonstrated
how the graph theory and matrix approach as well as fuzzy MADM methods
can be effectively used for decision-making in various situations of the
manufacturing environment; Koo (2009) presented a study on the
development of sustainability assessment model.
Table 3 provides information on reviews of general methods of MCDM.
Only some have been listed. A seminal study by Bragge et al. (2012) has
been recently published, which was carried out based on bibliometric
study of MCDM methods prevalence. A large-scale survey was conducted by
Toloie-Eshlaghy and Homayonfar (2011). It gives an overview of about 800
links. Zavadskas and Turskis (2011) provided an overview on MCDM methods
based on the traditional classification (Hwang, Yoon 1981).
A number of publications can be found in regional journals, such as
Alias et al. (2008), and several publications, which provide an overview
on the theory of fuzzy MCDM applications, such as Chu, Lin (2009),
El-Wahed (2008) and the analysis and classification of multi-criteria
systems.
Significantly more review articles on separate MCDM methods (Table
4) have been published. Kaplinski and Tamosaitiene (2010) reviewed
applications of discrete matrix game theory, Behzadian et al.
(2010)--PROMETHEE method, and Behzadian et al. (2012)--TOPSIS method.
Zopounidis and Doumpos (2002) conducted a review on a multi-criteria
classification and sorting. Adler et al. (2002) reviewed the DEA
method's applications. Shih (2008) conducted a review on TOPSIS
group methods. Ishizaka, Labib (2011) and Ho (2008) publications were
devoted to the analysis of AHP method. Jadhav and Rajendra Sonar (2009)
overviewed MCDM software packages. Balezentis and Balezentis (2013)
published a review article on MULTIMOORA method.
A significantly greater number of publications have been devoted to
comparative analysis of separate MCDM methods (Table 5). Opricovic and
Tzeng (2004) conducted a benchmarking on TOPSIS and VIKOR methods.
Simanaviciene and Ustinovicius (2012) provided a benchmarking on TOPSIS,
SAW and COPRAS methods. Podvezko (2011) conducted a comparative study of
SAW and COPRAS methods and Podviezko (2012) provided a comparative study
on SAW, PROMETHEE, TOPSIS, and COPRAS methods. Albinana and Vila (2012)
conducted benchmarking on VIKOR, ELECTRE, COPRAS, and EVAMIX methods. A
benchmarking MOORA, AHP, TOPSIS, VIKOR, ELECTRE and PROMETHEE methods
has been provided by Chakraborty (2011). Interesting and valuable work
in the field of MCDM benchmarking has been performed by Kou et al.
(2012), Peng et al. (2011), Balezentis et al. (2012) and Stanujkic et
al. (2012). Antucheviciene et al. (2011, 2012) carried out investigation
on fuzzy MCDM methods (TOPSIS, VIKOR and COPRAS) and provided a
comparative analysis. A large number of valuable works has been
conducted, which enables us to evaluate the positive and negative
characteristics of different MCDM methods and their ability to help
solving real practical problems in different areas.
Reviews on topics of individual activities can be identified into a
separate group of reviews on MCDM methods. Ehrgott et al. (2004),
Xidonas and Psarras (2009) applied the MCDM methods to portfolio
optimization and management; Jahan et al. (2010)--material choosing and
screening; Diaz-Balteiro and Romero (2008)--forestry-related decisions;
Ananda and Herath (2009)--forest management and planning. Greening and
Bernow (2004) applied the MCDM methods to design of Coordinated Energy
and Environmental Policy; Moffett and Sarkar (2006)--design of
conservation area networks. Kaplinski and Tupenaite (2011) reviewed the
latest MCDM applications in modern construction economics. Wang et al.
(2009) and Yazdani-Chamzini et al. (2013) reviewed MCDM applications for
energy systems assessment. Kaplinski and Peldschus (2011) and
Tamosaitiene and Kaplinski (2013) reviewed the applications of MCDM
methods in the social sciences; Zavadskas et al. (2008) reviewed the
applications of MCDM methods in the area of bridge and road
construction; and Huang et al. (2011)--MCDM methods in the social
sciences.
Conclusions
The paper presents synopsis of numerous publications, which
describe the use of traditional MCDM methods and some of the relatively
recently developed methods. However, this review does not cover recent
methods that have not yet been reviewed in articles or books. However,
it is worth noting that publications reviewed in this article at least
allow for a partial representation of the structure of those MCDM
methods, which are gaining wider use.
Recently, development of hybrid and modular methods is becoming
increasingly important. They are based on previously developed
well-known methods, such as TOPSIS, SAW, DEA, AHP, ANP, VIKOR, DEMATEL,
DEA, PROMETHEE, ELECTRE and their modification, by applying fuzzy and
grey number theory. Relatively recently developed MCDM methods, such as
COPRAS, ARAS, MOORA, MULTIMOORA, SWARA and WASPAS are rapidly developed
and applied to solve real life problems. In order to help researchers
and practitioners interested in hybrid MCDM techniques and applications
of hybrid MCDM methods, it is necessary to publish reviews on these
issues in future.
Caption: Fig. 1. Broad classification of MCDM methods
Caption: Fig. 2. Number of publications on topic: review papers on
MCDM methods (based on ISI Web of Science database)
Caption: Fig. 3. Five-step pyramid of MCDM reviews
doi:10.3846/20294913.2014.892037
References
Adler, N.; Friedman, L.; Sinuany-Stern, Z. 2002. Review of ranking
methods in the data envelopment analysis context, The European Journal
of Operational Research 140(2): 249-265.
http://dx.doi.org/10.1016/S0377-2217(02)00068-1
Albinana, J. C.; Vila, C. 2012. A framework for concurrent material
and process selection during conceptual product design stages, Materials
and Design 41(10): 433-446.
http://dx.doi.org/10.1016/j.matdes.2012.05.016
Alias, M. A.; Hashim, S. Z. M.; Samsudin, S. 2008. Multi-criteria
decision making and it applications: literature review, Jurnal Teknologi
Maklumat 20(2): 129-152.
Ananda, J.; Herath, G. 2009. A critical review of multi-criteria
decision making methods with special reference to forest management and
planning, Ecological Economics 68(10): 2535-2548.
http://dx.doi.org/10.1016/j.ecolecon.2009.05.010
Antucheviciene, J.; Zakarevicius, A.; Zavadskas, E. K. 2011.
Measuring congruence of ranking results applying particular MCDM
methods, Informatica 22(3): 319-338.
Antucheviciene, J.; Zavadskas, E. K.; Zakarevicius, A. 2012.
Ranking redevelopment decisions of derelict buildings and analysis of
ranking results, Economic Computation and Economic Cybernetics Studies
and Research 46(2): 37-62.
Aruldoss, M.; Lakshmi, T. M.; Venkatesan, V. P. 2013. a survey on
multi criteria decision making methods and its applications, American
Journal of Information Systems 1(1): 31-43.
Balezentis, A.; Balezentis, T.; Misiunas, A. 2012. An integrated
assessment of Lithuanian economic sectors based on financial ratios and
fuzzy MCDM methods, Technological and Economic Development of Economy
18(1): 34-53. http://dx.doi.org/10.3846/20294913.2012.656151
Balezentis, T.; Balezentis, A. 2013. A survey on development and
applications of the multi criteria decision-making method MULTIMOORA,
Journal of Multi-Criteria Decision Analysis (in Press).
Ballestero, E.; Romero, C. 2010. Multiple criteria decision making
and its applications to economic problems. Berlin: Springer. 168 p.
Behzadian, M.; Kazemzadeh, R. B.; Albadvi, A.; Aghdasi, M. 2010.
PROMETHEE: a comprehensive literature review on methodologies and
applications, European Journal of Operational Research 200(1): 198-215.
http://dx.doi.org/10.1016/j.ejor.2009.01.021
Behzadian, M.; Otaghsara, S. K.; Yazdani, M.; Ignatius, J. 2012. A
state-of the-art survey of TOPSIS applications, Expert Systems with
Applications 39(17): 13051-13069.
http://dx.doi.org/10.1016/j.eswa.2012.05.056
Belton, V; Stewart, T. J. 2002. Multiple criteria decision
analysis: an integrated approach. Dordrecht: Kluwer Academic Publishers.
372 p. http://dx.doi.org/10.1007/978-1-4615-1495-4
Bouyssou, D.; Marchant, T.; Pirlot, M.; Tsoukias, A.; Vincke, P.
2006. Evaluation and decision models with multiple criteria: stepping
stones for the analyst. New York: Springer. 459 p.
Bragge, J.; Korhonen, P.; Wallenius, H.; Wallenius, J. 2012.
Scholarly communities of research in multiple criteria decision making:
a bibliometric research profiling study, International Journal of
Information Technology & Decision Making 11(2): 401-426.
http://dx.doi.org/10.1142/S0219622012400081
Brauers, W. K. 2004. Optimization methods for a stakeholder
society, a revolution in economic thinking by multi-objective
optimization, series: nonconvex optimization and its applications. Vol.
73. Boston-Dordrecht-London: Kluwer Academic Publishers and Springer.
342 p. http://dx.doi.org/10.1007/978-1-4419-9178-2
Chakraborty, S. 2011. Applications of the MOORA method for decision
making in manufacturing environment, The International Journal of
Advanced Manufacturing Technology 54(9-12): 1155-1166.
http://dx.doi.org/10.1007/s00170-010-2972-0
Chen, Z.; Li, H. 2006. Environmental management in construction. A
quantitative approach. London and New York: Taylor & Francis. 232 p.
Chu, T.-C.; Lin, Y. 2009. An extension to fuzzy MCDM, Computers
& Mathematics with Applications 57(3): 445-454.
http://dx.doi.org/10.1016/j.camwa.2008.10.076
Cook, W. D.; Seiford, L. M. 2009. Data envelopment analysis
(DEA)--thirty years on, European Journal of Operational Research 192(1):
1-17. http://dx.doi.org/10.1016/j.ejor.2008.01.032
Diaz-Balteiro, L.; Romero, C. 2008. Making forestry decisions with
multiple criteria: a review and an assessment, Forest Ecology and
Management 255(8-9): 3222-3241.
http://dx.doi.org/10.1016/j.foreco.2008.01.038
Doumpos, M.; Grigoroudis, E. 2013. Multicriteria decision aid and
artificial intelligence: links, theory and applications. Chichester:
Wiley-Blackwell. 368 p.
Ehrgott, M.; Figueira, R. J.; Greco, S. 2010. Trends in multiple
criteria decision analysis. New York, Dordrecht, Heidelberg, London:
Springer. 412 p. http://dx.doi.org/10.1007/978-1-4419-5904-1
Ehrgott, M.; Klamroth, K.; Schwehmc, C. 2004. An MCDM approach to
portfolio optimization, European Journal of Operational Research 155(3):
752-770. http://dx.doi.org/10.1016/S0377-2217(02)00881-0
El-Wahed, W. F. A. 2008. Intelligent fuzzy multi-criteria decision
making: review and analysis, Fuzzy Multi-Criteria Decision Making
Springer Optimization and its Applications 16: 19-50.
Figueira, J.; Greco, S.; Ehrgott, M. (Eds.). 2005. Multiple
criteria decision analysis: state of the art survey. Boston: Springer.
1048 p.
Fishburn, P. C. 1978. A survey of multiattibute/multicriterion
evaluation theories, in S. Zionts (Ed.). Multicriteria problem solving.
Berlin: Springer Verlag, 181-224.
Gal, T.; Stewart, T. J.; Hanne, T. 2009. Multicriteria decision
making advances in MCDM models, algorithms, theory and applications.
Berlin: Springer. 564 p.
Greening, L. A.; Bernow, S. 2004. Design of coordinated energy and
environmental policies: use of multi-criteria decision-making, Energy
Policy 32(6): 721-735. http://dx.doi.org/10.1016/j.enpol.2003.08.017
Hanne, T. 2009. Intelligent strategies for meta multiple criteria
decision making. Berlin: Springer. 216 p.
Ho, W. 2008. Integrated analytic hierarchy process and its
applications--a literature review, European Journal of Operational
Research 186(1): 211-228. http://dx.doi.org/10.1016/j.ejor.2007.01.004
Ho, W.; Xu, X.; Dey, P. K. 2010. Multi-criteria decision making
approaches for supplier evaluation and selection: a literature review,
European Journal of Operational Research 202(1): 16-24.
http://dx.doi.org/10.1016/j.ejor.2009.05.009
Huang, I. B.; Keisler, J.; Linkov, I. 2011. Multi-criteria decision
analysis in environmental sciences: ten years of applications and
trends, Science of the Total Environment 409(19): 3578-3594.
http://dx.doi.org/10.1016/j.scitotenv.2011.06.022
Hwang, C. L.; Masud, A. S. M. 1979. Multiple objective decision
making methods and applications, a state of the art survey, Lecture
Notes in Economics and Mathematical Systems. Vol. 164. Berlin:
Springer-Verlag. 357 p. http://dx.doi.org/10.1007/978-3-642-45511-7
Hwang, C. L.; Yoon, K. 1981. Multiple attributes decision making
methods and applications. Berlin, Heidelberg: Springer. 269 p.
http://dx.doi.org/10.1007/978-3-642-48318-9
Hwang, C. L.; Lin, M. J. 1987. Group decision making under multiple
criteria: methods and applications. Berlin: Springer-Verlag. 405 p.
http://dx.doi.org/10.1007/978-3-642-61580-1
Ishizaka, A.; Labib, A. 2011. Review of the main developments in
the analytic hierarchy process, Expert Systems with Applications 38(11):
14336-14345.
Ishizaka, A.; Nemery, P. 2013. Multi-criteria decision analysis:
methods and software. Chichester: John Wiley & Sons. 296 p.
Jadhav, A. S.; Rajendra Sonar, R. M. 2009. Evaluating and selecting
software packages: a review, Information and Software Technology 51(3):
555-563. http://dx.doi.org/10.1016/j.infsof.2008.09.003
Jahan, A.; Ismail, M. Y.; Sapuan, S. M.; Mustaph, F. 2010. Material
screening and choosing methods--a review, Materials & Design 31(2):
696-705. http://dx.doi.org/10.1016/j.matdes.2009.08.013
Jahan, A.; Edwards, K. L. 2013. VIKOR method for material selection
problems with interval numbers and target-based criteria, Materials
& Design 47: 759-765.
Kabir, G.; Sadiq, R.; Tesfamariam, S. 2013. A review of
multi-criteria decision-making methods for infrastructure management,
Structure and Infrastructure Engineering: Maintenance, Management,
Life-Cycle Design and Performance. London: Taylor and Francis. 36 p.
http://dx.doi.org/10.1080/15732479.2013.795978
Kahraman, C. 2008. Fuzzy multi-criteria decision making. Theory and
applications with recent developments. Turkey: Springer,
Science+Business Media, LLC. 591 p.
Kaliszewski, I. 2010. Soft computing for complex multiple criteria
decision making. Berlin: Springer. 172 p.
Kaplinski, O.; Peldschus, F. 2011. The problems of quantitative
evaluation of socio-economic systems' development: review,
Inzinerine Ekonomika--Engineering Economics 22(4): 345-355.
Kaplinski, O.; Tamosaitiene, J. 2010. Game theory applications in
construction engineering and management, Technological and Economic
Development of Economy 16(2): 348-363.
http://dx.doi.org/10.3846/tede.2010.22
Kaplinski, O.; Tupenaite, L. 2011. Review of the multiple criteria
decision making methods, intelligent and biometric systems applied in
modern construction economics, Transformations in Business &
Economics 10(1): 166-181.
Keeney, R. L.; Raiffa, H. 1976. Decision with multiple objectives:
preferences and value trade-offs. New York: John Wiley & Sons. 569
p.
Koksalan, M. M.; Wallenius, J.; Zionts, S. 2011. Multiple criteria
decision making: from early history to the 21st century. Singapore;
Hackensack, NJ: World Scientific. 200 p.
Koo, D. H. 2009. Development of sustainability assessment model.
Development of sustainability assessment model for underground
infrastructure. VDM Verlag Dr. Muller. 184 p.
Kou, G.; Lu, Y.; Peng, Y.; Shi, Y. 2012. Evaluation of
classification algorithms using MCDM and rank correlation, International
Journal of Information Technology & Decision Making 11(1): 197-225.
http://dx.doi.org/10.1142/S0219622012500095
Larichev, O. I.; Olson, D. L. 2001. Multiple criteria analysis in
strategic siting problems. Norwell: Kluwer Academic Publishers. 217 p.
Liou, J. J. H. 2013. New concepts and trends of MCDM for
tomorrow--in honor of Professor Gwo-Hshiung Tzeng on the occasion of his
70th birthday, Technological and Economic Development of Economy 19(2):
367-375. http://dx.doi.org/10.3846/20294913.2013.811037
Liou, J. J. H.; Tzeng, G.-H. 2012. Comments on "Multiple
criteria decision making (MCDM) methods in economics: an overview",
Technological and Economic Development of Economy 18(4): 672-695.
http://dx.doi.org/10.3846/20294913.2012.753489
Manouselis, N.; Costopoulou, C. 2007. Analysis and classification
of multi-criteria recommender systems, World Wide Web 10(4): 415-441.
http://dx.doi.org/10.1007/s11280-007-0019-8
Melo, M. T.; Nickel, S.; Saldanha-da-Gama, F. 2009. Facility
location and supply chain management--a review, European Journal of
Operational Research 169(2): 401-412.
http://dx.doi.org/10.1016/j.ejor.2008.05.007
Miettinen, K. 2009. Nonlinear multiobjective optimization. Berlin:
Springer. 320 p.
Moffett, A.; Sarkar, S. 2006. Incorporating multiple criteria into
the design of conservation area networks: a minireview with
recommendations, Diversity and Distributions 12(2): 125-137.
http://dx.doi.org/10.1111/j.1366-9516.2005.00202.x
Opricovic, S.; Tzeng, G. H. 2004.Compromise solution by MCDM
methods: a comparative analysis of VIKOR and TOPSIS, European Journal of
Operation Research 156(2): 445-455.
http://dx.doi.org/10.1016/S0377-2217(03)00020-1
Pedrycz, W.; Ekel, P.; Parreiras, R. 2010. Fuzzy multicriteria
decision-making: models, methods and applications. Chichester: John
Wiley & Sons Ltd. 360 p. http://dx.doi.org/10.1002/9780470974032
Peldschus, F.; Zavadskas, E. 1997. Matrix games in building
technology and management. Vilnius: Technika. 134 p. (in Lithuanian).
Peng, Y.; Kou, G.; Wang, G.; Shi, Y. 2011. FAMCDM: a fusion
approach of MCDM methods to rank multiclass classification algorithms,
Omega 39(6): 677-689. http://dx.doi.org/10.1016/j.omega.2011.01.009
Podviezko, A. 2012. Augmenting multicriteria decision aid methods
by graphical and analytical reporting tools, Lecture in Business
Information Processing 106: 236-251.
http://dx.doi.org/10.1007/978-3-642-29231-6_19
Podvezko, V. 2011. The comparative analysis of MCDA methods SAW and
COPRAS, Inzinerine Ekonomika --Engineering Economics 2(22): 134-146.
Raju, K. S.; Kumar, D. M. 2013. Multicriterion analysis in
engineering and management. Kindle edition. PHI Learning Private
Limited. 343 p.
Roy, B. 1996. Multicriteria methodology for decision aiding.
Dortrecht: Kluwer Academic Publishers. 293 p.
http://dx.doi.org/10.1007/978-1-4757-2500-1
Saaty, T. L. 1980. The analytic hierarchy process. New York:
McGraw-Hill. 287 p.
Saaty, T. L. 1996. Decision making with dependence and feedback:
the analytic network process. Pittsburgh: RWS Publications. 370 p.
Scott, J. 2000. Rational choice theory, in Browning, G. (Ed.). From
understanding contemporary society: theories of the present. London:
SAGE Publications Ltd, 126-139.
http://dx.doi.org/10.4135/9781446218310.n9
Savage, L. J. 1954. The foundations of statistics. Second revised
edition. New York: Wiley. 112 p.
Shih, H.-S. 2008. Incremental analysis for MCDM with an application
to group TOPSIS, European Journal of Operational Research 186(2):
720-734. http://dx.doi.org/10.1016/j.ejor.2007.02.012
Simanaviciene, R.; Ustinovicius, L. 2012. A new approach to
assessing the biases of decisions based on multiple attribute decision
making methods, Elektronics and Electrical Engineering--Elektronika ir
Elektrotechnika 1(117): 29-32.
Stanujkic, D.; Magdalinovic, N.; Jovanovic, R.; Stojanovic, S.
2012. An objective multi-criteria approach to optimization using MOORA
method and interval grey numbers, Technological and Economic Development
of Economy 18(2): 331-363.
http://dx.doi.org/10.3846/20294913.2012.676996
Tamosaitiene, J.; Kaplinski, O. 2013. Strategic Environmental
Assessment (SEA) of socio-economic systems: a systematic review,
Technological and Economic Development of Economy 19(4): 661-674.
http://dx.doi.org/10.3846/20294913.2013.862882
Toloie-Eshlaghy, A.; Homayonfar, M. 2011. MCDM methodologies and
applications: a literature review from 1999 to 2009, Research Journal of
International Studies (21): 86-137.
Triantaphyllou, E. 2000. Multi-criteria decision making methods: a
comparative study. Dordrecht: Kluwer Academic Publishers. 289 p.
http://dx.doi.org/10.1007/978-1-4757-3157-6
Triantaphyllou, E. 2010. Multi-criteria decision making methods: a
comparative study. Dordrecht: Springer. 288 p.
Tzeng, G.-H.; Huang, J.-J. 2011. Multiple attribute decision
making. New York: CRC Press. 349 p.
Venkata, R. R. 2007. Decision making in the manufacturing
environment: using graph theory and fuzzy multiple attribute decision
making methods. London, Heidelberg, New York, Dordrecht: Springer. 391
p.
Von Neumann, J.; Morgenstern, O. 1947. Theory of games and economic
behavior. 2nd ed. Princeton: Princeton University Press. 641 p.
Wang, J.-J.; Jing, Y.-Y.; Zhang, C.-F.; Zhao, J.-H. 2009. Review on
multi-criteria decision analysis aid in sustainable energy
decision-making, Renewable and Sustainable Energy Reviews 13(9):
2263-2278. http://dx.doi.org/10.1016/j.rser.2009.06.021
Xidonas, P.; Psarras, J. 2009. Equity portfolio management within
the MCDM frame: a literature review, International Journal of Banking,
Accounting and Finance 1(3): 285-309.
Yazdani-Chamzini, A.; Fouladgar, M. M.; Zavadskas, E. K.; Moini, S.
H. H. 2013. Selecting the optimal renewable energy using multi criteria
decision making, Journal of Business Economics and Management 14(5):
957-978. http://dx.doi.org/10.3846/16111699.2013.766257
Zavadskas, E. K.; Liias, R.; Turskis, Z. 2008. Multi-attribute
decision-making methods for assessment of quality in bridges and road
construction: state-of-the-art surveys, The Baltic Journal of Road and
Bridge Engineering 3(3): 152-160.
http://dx.doi.org/10.3846/1822-427X.2008.3.152-160
Zavadskas, E.; Peldschus, F.; Kaklauskas, A. 1994. Multiple
criteria evaluation of projects in construction. Vilnius: Technika. 226
p.
Zavadskas, E. K.; Turskis, Z. 2011. Multiple criteria decision
making (MCDM) methods in economics: an overview, Technological and
Economic Development of Economy 17(2): 397-427.
http://dx.doi.org/10.3846/20294913.2011.593291
Zeleny, M. 1982. Multiple criteria decision making. New York:
McGraw-Hill. 563 p.
Zopounidis, C.; Doumpos, M. 2002. Multicriteria classification and
sorting methods: a literature review, European Journal of Operational
Research 138(2): 229-246.
http://dx.doi.org/10.1016/S0377-2217(01)00243-0
Zopounidis, C.; Pardalos, P. M. (Eds.). 2010. Handbook of
multicriteria analysis. Berlin, Heidelberg: Springer-Verlag. 455 p.
http://dx.doi.org/10.1007/978-3-540-92828-7
Received 31 December 2012; accepted 31 January 2014
Edmundas Kazimieras ZAVADSKAS, Zenonas TURSKIS, Simona KILDIENE
Department of Construction Technology and Management, Faculty of
Civil Engineering, Vilnius Gediminas Technical University, Sauletekio
al. 11, 10223 Vilnius, Lithuania
Corresponding author Simona Kildiene
E-mail: simona.kildiene@vgtu.lt
Edmundas Kazimieras ZAVADSKAS. PhD, DSc, h.c.multi. Prof., the Head
of the Department of Construction Technology and Management of Vilnius
Gediminas Technical University, Lithuania. Senior Research Fellow at the
Research Institute of Smart Building Technologies. PhD in Building
Structures (1973). Dr Sc. (1987) in Building Technology and Management.
A member of Lithuanian and several foreign Academies of Sciences.
Doctore Honoris Causa from Poznan, Saint Petersburg and Kiev
universities. The Honorary International Chair Professor in the National
Taipei University of Technology. A member of international
organizations; a member of steering and programme committees at many
international conferences; a member of the editorial boards of several
research journals; the author and co-author of more than 400 papers and
a number of monographs in Lithuanian, English, German and Russian.
Editor-in-chief of journals Technological and Economic Development of
Economy and Journal of Civil Engineering and Management. Research
interests: building technology and management, decision-making theory,
automation in design and decision support systems.
Zenonas TURSKIS. PhD from VISI (Vilnius Engineering Construction
Institute, former name of Vilnius Gediminas Technical University). He
works at the Construction Department of Vilnius Gediminas Technical
University. He has more than 100 publications in journals such as
International Journal of Information Technology & Decision Making,
Economic Research, Journal of Economic Computation and Economic
Cybernetics Studies and Research (ECECSR) and more. His research
interests include automated programming, technological decision
multicriteria evaluation in construction and investment areas.
Simona KILDIENE. PhD student at the Department of Construction
Technology and Management of Vilnius Gediminas Technical University,
Vilnius, Lithuania. Master of Science (construction engineering), VGTU,
2010. Bachelor of Science (construction management), VGTU, 2008.
Research interests include construction economics, construction
management, multiple criteria analysis and decision-making theories.
Table. 1. Number of publications by research area MCDM review papers
(from database ISI Web of Science)
Number of
MCDM review papers publications
Energy fuels 18
Operations research management science 17
Management 12
Environmental sciences, Ecology 10
Economics 5
Environmental sciences 5
Computer science artificial intelligence 3
Engineering Electrical Electronic 3
Biodiversity Conservation 2
Computer science information systems 2
Ecology 2
Environmental studies 2
Geography 2
Geography physical 2
Agronomy 1
Business 1
Chemistry Physical 1
Computer Science interdisciplinary application 1
Computer Science Software Engineering 1
Computer Science Theory Methods 1
Construction Building Technology 1
Engineering Civil 1
Engineering Industrial 1
Engineering Manufacturing 1
Forestry 1
Forestry 1
Health Care Sciences Service 1
Information Science Library Science 1
Mathematics Applied 1
Metallurgy Metallurgical Engineering 1
Mining Mineral Processing 1
Obstetrics Gynaecology 1
Planning Development 1
Public Environmental Occupational Health 1
Statistics Probability 1
Urban Studies 1
Water Resources 1
Table 2. Books on MCDM methods
Reference Considered problem
Keeney, Raiffa 1976 Decision with MODM
Hwang, Masud 1979 MODM methods
Saaty 1980 The analytic hierarchy process
Hwang, Yoon 1981 MADM
Zeleny 1982 MCDM
Hwang, Lin 1987 Group MCDM
Zavadskas et al. 1994 Multi-criteria evaluation of projects
in construction
Roy 1996 Multicriteria methodology for decision
aiding
Saaty 1996 Decision making with dependence and
feedback
Peldschus, Zavadskas 1997 Matrix games in building technology and
management
Triantaphyllou 2000 MCDM methods
Belton, Stewart 2002 Multiple criteria decision analysis
Figueira et al. (Eds.) 2005 Multiple criteria decision analysis
Bouyssou et al. 2006 Evaluation and decision models with
multiple criteria: stepping stones for
the analyst
Chen, Li 2006 Environmental management in
construction
Kahraman 2008 Fuzzy MCDM
Gal et al. 2009 Multi-criteria decision making advances
in MCDM models, algorithms, theory and
applications
Hanne 2009 Intelligent strategies for meta MCDM
Koo 2009 Development of sustainability
assessment model
Miettinen 2009 Nonlinear multi objective optimization
Ballestero, Romero 2010 MCDM and its applications to economic
problems
Ehrgott et al. 2010 Trends in multiple criteria decision
analysis
Kaliszewski 2010 Soft computing for complex multiple
criteria decision making
Pedrycz et al. 2010 Decision-making in system project,
planning, operation, and control:
motivation, objectives, and basic
concepts offuzzy MCDM
Triantaphyllou 2010 MCDM methods: a comparative study
Zopounidis, Pardalos 2010 Multi-criteria analysis
Koksalan et al. 2011 MCDM
Tzeng, Huang 2011 MADM
Doumpos, Grigoroudis 2013 Recent advances in intelligent decision
making and presentation of hybrid
models and algorithms for preference
modelling and optimisation problems
Ishizaka, Nemery 2013 Multi-criteria decision analysis
Larichev, Olson 2001 Multiple criteria analysis in strategic
siting problems
Table 3. General reviews on MCDM in articles of scientific journals
Reference Considered problem
Manouselis, Costopoulou 2007 Analysis and classification of
multi-criteria recommender systems
El-Wahed 2008 Intelligent fuzzy MCDM
Chu, Lin 2009 An extension to fuzzy MCDM
Zavadskas, Turskis 2011 Multiple criteria decision making
(MCDM) methods in economics
Bragge et al. 2012 Scholarly communities of research in
multiple criteria decision making: a
bibliometric research profiling study
Liou, Tzeng 2012 Multiple criteria decision making
(MCDM) methods in economics
Liou 2013 New concepts and trends of MCDM
Aruldoss et al. 2013 A survey on multi criteria decision
making methods and its applications
Table 4. Overview on application of different MCDM methods
Reference Considered problem
Adler et al. 2002 Data Envelopment Analysis (DEA)
Zopounidis, Doumpos 2002 Multicriteria classification and
sorting methods
Ho 2008 Integrated analytic hierarchy process
Shih 2008 MCDM with an application to group
TOPSIS
Jadhav, Rajendra Sonar 2009 Software packages
Cook, Seiford 2009 Data envelopment analysis (DEA)
Kaplinski, Tamosaitiene 2010 Game theory application
Behzadian et al. 2010 PROMETHEE
Ishizaka, Labib 2011 Analytic hierarchy process
Behzadian et al. 2012 A state-of the-art survey of TOPSIS
applications
Balezentis, T., Applications of the multi criteria
Balezentis, A. 2013 decision making method MULTIMOORA
Table 5. Comparative analysis of MCDM methods
Reference Considered problem
Kou et al. 2012 Evaluation of classification algorithms
using MCDM and rank correlation
Peng et al. 2011 FAMCDM
Podvezko 2011 Comparative analysis SAW and COPRAS
Antucheviciene et al. 2012 Comparative analysis of FTOPSIS, FVIKOR
and COPRAS-F
Antucheviciene et al. 2011 Measuring congruence of ranking results
applying particular MCDM methods
Opricovic, Tzeng 2004 TOPSIS and VIKOR
Simanaviciene, TOPSIS, SAW, COPRAS
Ustinovicius 2012
Chakraborty 2011 MOORA, AHP, TOPSIS, VIKOR, ELECTRE,
PROMETHEE
Balezentis et al. 2012 VIKOR, TOPSIS, ARAS
Albinana, Vila 2012 VIKOR, ELECTRE, COPRAS, EVAMIX
Stanujkic et al. 2012 Comparative analysis of some prominent
MCDM methods
Table 6. MCDM review on topics of individual activities
Reference Considered problem
Greening, Bernow 2004 Design of coordinated energy and
environmental policies
Melo et al. 2009 Facility location and supply chain
management
Moffett, Sarkar 2006 Design of conservation area networks
Ananda, Herath 2009 Forest management and planning
Diaz-Balteiro, Romero 2008 Forestry decisions
Ehrgott et al. 2004 Portfolio optimization
Ho et al. 2010 Supplier evaluation and selection
Jahan et al. 2010 Material screening and choosing
Xidonas, Psarras 2009 Equity portfolio management
Kaplinski, Tupenaite 2011 Modern construction economics
Wang et al. 2009 Sustainable energy
Kaplinski, Peldschus 2011 Social science
Huang et al. 2011 Environmental sciences
Zavadskas et al. 2008 Quality in bridges and road
construction
Yazdani-Chamzini et al. 2013 Selecting the optimal renewable energy
Tamosaitiene, Kaplinski 2013 Application of MCDM methods in social
sciences
Kabir et al. 2013 A review of multi-criteria
decision-making methods for
infrastructure management
