Abstract
The multi-attribute group decision making (MAGDM) problem has received considerable attention in the decision analysis field and fruitful achievements have been reported in the literature. This paper focuses on the MAGDM in which the subjective absolute judgement on alternatives with respect to evaluating attributes are represented by fuzzy numbers. This paper employs the consensus degree to measure the agreement level of a MAGDM solution and develops a new measure degree-departure degree to evaluate how far the decision makers from their initial decision preferences. Based on these two conflicting measure degrees, the decision process of MAGDM is modelled as a multi-objective optimization problem. A decision support model (DSM) for MAGDM is proposed. The proposed DSM, incorporating five implementing phases, aims at obtaining acceptable decision solution(s) by solving the multi-objective optimization problem and conducting an interactive procedure with decision makers. In case study, this paper takes the alternative selection problem about hydroelectric project to illustrate the phases and procedure of the proposed DSM.
Article PDF
Avoid common mistakes on your manuscript.
References
Utpal Bose, Anne M. Davey and David L Olson, “Multi-attribute utility methods in group decision making Past application and potential for inclusion in GDSS,” Omega, 25, 691–706 (1997).
V. S. Lai, Bo K. Wong and W. Cheung, “Group decision making in a multiple criteria environment: A case using AHP in software selection,” European Journal of Operational Research, 137, 134–144 (2002).
C. Kahraman, D. Ruan and I. Doğan, “Fuzzy group decision-making for facility location selection,” Information Sciences, 157, 135–153 (2003).
C. E. Bozdağ, C. Kahraman and D. Ruan, “Fuzzy group decision making for selection among comput-er integrated manufacturing systems,” Computers in Industry, 51, 13–29 (2003).
A. K. Choudhury, R. Shankar and M. K. Tiwari, “Consensus-based intelligent group decision-making model for the selection of advanced technology,” Decision Support Systems, 42, 1776–1799 (2006).
J. Lu, G.Q. Zhang, D. Ruan, Intelligent multi-criteria fuzzy group decision-making for situation assessments, Soft Comput 12, 289–299 (2008).
T. L. Saaty, Rank from comparisons and from ratings in the analytic hierarchy/network processes, European Journal of Operational Research 168, 557–570 (2006).
C.-H. Yeh, Y.-H. Chang, Modeling subjective evaluation for fuzzy group multicriteria decision making, European Journal of Operational Research 194, 464–473 (2009).
Zeshui Xu, Multiple-attribute group decision making with different formats of preference information on attributes, IEEE Transaction on System-s,Man,and Cybernetics-Part B:Cybernetics 37, 1500–1511 (2007).
S.Saint and J.R.Lawson, “Rules for reaching cosensus: A modern approach to decision making,” CA:Jossey-Bass, San Francisco, 1994.
J. Kacprzyk, M. Fedrizzi and H. Nurmi, Group decision making and consensus under fuzzy preferences and fuzzy majority, Fuzzy Set and Systems 49, 21–31 (1992).
E. Ephrati and J. S. Rosenschein, Deriving consensus in multiagent systems, Atificial Intelligence 87, 21–74 (1996).
F. Herrera, E. Herrera-Viedam and J. L. Verdegay, A model of consensus in group decision making under linguistic assessments, Fuzzy Sets and Systems 79, 73–87 (1996).
F. Herrera, E. Herrera-Viedam and J. L. Verdegay, A rational consensus model in group decision making under linguistic assessment, Fuzzy Sets and Systems 88, 31–49 (1997).
M. Fedrizzi,M. Fedrizzi and R. A. Marques Pereira, Soft consensus and network dynamics in group decision making, International Journal of Intelligent Systems 14, 63–77 (1999).
E. Herrera-Viedma, Modeling the retrieval process for an information retrieval system using an ordinal fuzzy linguistic approach, Journal of the American Society for Information Science and Technology 52, 460–475 (2001).
E. Herrera-Viedam and L. Mart´ınez and F.Chiclana, A consensus support system model for group decision-making problems with multigranular linguistic preference relations, IEEE Transactions on Fuzzy Systems 13, 644–658 (2005).
P. Eklund, A. Rusinowska and H. de Swart, Consensus reaching in committees, European Journal of Operational Research 178, 185–193 (2007).
F.Mata,L.Mart´ınez and E.Herrera-Viedam, An adaptive consensus support model for group decision-making problems in a multigranular fuzzy linguistic context, IEEE Transactions on Fuzzy Systems 17, 279–290 (2009).
G. Bordogna, M. Fedrizzi and G. Pasi, A linguistic modeling of consensus in group decision making based on OWA operators, IEEE Transactions on Systems,Man,Cybernetics-Part A 27, 126–132 (1997).
D. Ben-Arieh and Z. F. Chen, Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations, IEEE Transactions on Systems,Man,Cybernetics-Part A 36, 558–568 (2006).
D. Ben-Arieh and T. Easton, Multi-criteria group consensus under linear cost opinion elasticity, Decision Support Systems 43, 713–721 (2007).
Zeshui Xu, An automatic approach to reaching consensus in multiple attribute group decision making, Computers & Industrial Engineering 56, 1369–1374 (2009).
S. H. Kim and C. H. Han, An interactive procedure for multi-attribute group decision making with incomplete information, Computers & Operations Research 26 (1999), 755–772.
Y. Y. Haimes, “Risk Modeling, Assessment, and Management,” 2nd edition, Wiley-Interscience, New York, 2004.
The Telegraph, “Amazon facing ‘real-life Avatar’ says James Cameron”, 2010.
C.-H. Yeh, R. J. Willis and H.Deng et al, Task oriented weighting in multi-criteria analysis, European Journal of Operation Research 119, 130–146 (1999).
H.-J. Zimmermann, “Fuzzy set theory and its applications,” 3rd edition, Kluwer Academic Publishers, Boston, 1999.
A. I. Olcer and A. Y. Odabasi, A new fuzzy multiple attribute group decision making methodology and its application to propulsion/manoeuvring system selection problem, European Journal of Operation Research 166, 93–114 (2005).
L. A. Zadeh, Fuzzy sets, Information and Control 8, 38–353 (1965).
T. L. Saaty, “The analytic hierarchy process,” McGraw-Hill, New York, 1980.
N. Bryson, Group decision-makig and the analytic hierarchy process:Exploring the consensus-relevant information content, Computers and Operation Research 23, 27–35 (1996).
R. Zwich, E. Carlstein and D. V. Budescu, Measures of similarity among fuzzy concepts:A comparative analysis, International Journal of Approximate Reasoning 1, 221–242 (1987).
G. R. Klir and B. Yuan, “Fuzzy sets and fuzzy logic theory and applications,” Prentice-Hall, Upper Saddle River, NJ.
C.-T. Chen, Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems 114, 1–9 (2000).
K. Deb, “Multi-Objective Optimization Using Evolutionary Algorithms,” John Wiley & Sons, New York, 2001.
K. Deb, A. Pratap, S. Agarwal, et al., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 2, 182–197 (2002).
C. L. Hwang and K. Yoon “Multiple attribute decision making: Methods and application,” Springer-Verlag, New York, 1981.
H. Deng, C.-H. Yeh and R. J. Willis, Inter-company comparison using modified TOPSIS with objective weights, Computers & Operations Research 10, 645–653 (2000).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
**ong, J., Chen, Y., Yang, K. et al. A decision support model for multi-attribute group decision making using a multi-objective optimization approach. Int J Comput Intell Syst 6, 337–353 (2013). https://doi.org/10.1080/18756891.2013.769781
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1080/18756891.2013.769781