Interval-Valued Hesitant Fuzzy Method based on Group Decision Analysis for Estimating Weights of Decision Makers

Document Type : Research Paper

Authors

School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

In this paper, a new soft computing group decision method based on the concept of compromise ratio is introduced for determining decision makers (DMs)' weights through the group decision process under uncertainty. In this method, preferences and judgments of the DMs or experts are expressed by linguistic terms for rating the industrial alternatives among selected criteria as well as the relative significance of each criterion. The DMs’ opinions are demonstrated by a decision matrix in interval-valued hesitant fuzzy sets (IVHFSs). In addition, the interval-valued hesitant fuzzy positive and negative ideal solutions are defined by the matrix, respectively. Then, the hesitant fuzzy average and worst group scores of the DMs’ decision matrix from matrices of interval-valued hesitant fuzzy positive and negative ideal solutions are described based on n-dimensional interval-valued hesitant fuzzy Euclidean distance measure. Further, a novel collective index is introduced based on the IVHFS to determine the weight of each DM or expert in the group decision process. Finally, an application example in industrial selection problems is presented about the best site selection for building a new factory to explain the computation process of the proposed soft computing group decision method in detail.

Keywords

Main Subjects


Atanassov, Krassimir T 1986. Intuitionistic fuzzy sets. Fuzzy sets and Systems 20: 87-96.
 
Bodily, Samuel E 1979. Note-A Delegation Process for Combining Individual Utility Functions. Management Science 25: 1035-1041.
 
Brock, Horace W 1980. The problem of “utility weights” in group preference aggregation. Operations Research 28: 176-187.
 
Chatterjee, Kajal and Samarjit Kar 2013. A hybrid MCDM approach for selection of financial institution in supply chain risk management. In A hybrid MCDM approach for selection of financial institution in supply chain risk management, Fuzzy Systems (FUZZ), 2013 IEEE International Conference on, 1-7: IEEE.
 
Chen, Chen-Tung 2000. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems 114: 1-9.
 
Chen, Na, Zeshui Xu and Meimei Xia 2013. Interval-valued hesitant preference relations and their applications to group decision making. Knowledge-Based Systems 37: 528-540.
 
Chen, Shyi-Ming 2001. Fuzzy group decision making for evaluating the rate of aggregative risk in software development. Fuzzy sets and systems 118: 75-88.
 
Chu, T-C and M-T Lai 2005. Selecting distribution centre location using an improved fuzzy MCDM approach. The International Journal of Advanced Manufacturing Technology 26: 293-299.
 
Demirel, Tufan, Nihan Çetin Demirel and Cengiz Kahraman 2010. Multi-criteria warehouse location selection using Choquet integral. Expert Systems with Applications 37: 3943-3952.
Doria, Serena 2012. Characterization of a coherent upper conditional prevision as the Choquet integral with respect to its associated Hausdorff outer measure. Annals of Operations Research 195: 33-48.
 
French Jr, John RP 1956. A formal theory of social power. Psychological review 63: 181.
 
Gitinavard, H, SM Mousavi and B Vahdani 2015. Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems. Soft Computing: 1-19.
 
Greco, Salvatore, Benedetto Matarazzo and Silvio Giove 2011. The Choquet integral with respect to a level dependent capacity. Fuzzy sets and Systems 175: 1-35.
 
J.-M. Martel, S. Ben Khélifa 2000. Deux propositions d’aide multicritère à la décision de groupe. in: Ben Abdelaziz, Haouari et Mellouli (Eds.), Optimisation et Décision, Centre de Publication Universitaire, Tunis pp. 213–228.
 
Jabeur, Khaled and Jean-Marc Martel 2002. Quantification de l'importance relative des membres d'un groupe en vue de determiner un preordre collectif. Infor-Information Systems and Operational Research 40: 18-176.
 
Jahanshahloo, Gholam Reza, F Hosseinzadeh Lotfi and AR Davoodi 2009. Extension of TOPSIS for decision-making problems with interval data: Interval efficiency. Mathematical and Computer Modelling 49: 1137-1142.
 
Kahraman, Cengiz, Da Ruan and Ibrahim Doǧan 2003. Fuzzy group decision-making for facility location selection. Information Sciences 157: 135-153.
 
Kangas, Jyrki, Annika Kangas, Pekka Leskinen and Jouni Pykäläinen 2001. MCDM methods in strategic planning of forestry on state‐owned lands in Finland: applications and experiences. Journal of Multi‐Criteria Decision Analysis 10: 257-271.
 
Keeney, Ralph L 1976. A group preference axiomatization with cardinal utility. Management Science 23: 140-145.
 
Keeney, Ralph L and Craig W Kirkwood 1975. Group decision making using cardinal social welfare functions. Management Science 22: 430-437.
 
Kim, Soung Hie and Byeong Seok Ahn 1999. Interactive group decision making procedure under incomplete information. European Journal of Operational Research 116: 498-507.
 
Kung, Chaang-Yung and Kun-Li Wen 2007. Applying grey relational analysis and grey decision-making to evaluate the relationship between company attributes and its financial performance—a case study of venture capital enterprises in Taiwan. Decision Support Systems 43: 842-852.
 
Liu, Chin-Hung and Hsin-Hung Wu 2008. A fuzzy group decision-making approach in quality function deployment. Quality and Quantity 42: 527-540.
 
Melese, Francois 2009. The Economic Evaluation of Alternatives (EEoA): Rethinking the Application of Cost-effectiveness Analysis, Multi-criteria Decision-making (MCDM) and the Analysis of Alternatives (AoA) in Defense Procurement. In The Economic Evaluation of Alternatives (EEoA): Rethinking the Application of Cost-effectiveness Analysis, Multi-criteria Decision-making (MCDM) and the Analysis of Alternatives (AoA) in Defense Procurement: DTIC Document.
 
Mirkin, Boris G 1979. Group choice. Washington, DC.
Miyamoto, Sadaaki 2000. Multisets and fuzzy multisets. In Multisets and fuzzy multisets, Soft Computing and Human-Centered Machines, 9-33: Springer.
 
Patil, Sachin K and Ravi Kant 2014. A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Systems with Applications 41: 679-693.
 
Saghafian, Soroush and S Reza Hejazi 2005. Multi-criteria group decision making using a modified fuzzy TOPSIS procedure. In Multi-criteria group decision making using a modified fuzzy TOPSIS procedure, Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on, 215-221: IEEE.
 
Tavakkoli-Moghaddam, Reza, Hossein Gitinavard, Seyed Meysam Mousavi and Ali Siadat 2015. An interval-valued hesitant fuzzy TOPSIS method to determine the criteria weights. In An interval-valued hesitant fuzzy TOPSIS method to determine the criteria weights, Outlooks and Insights on Group Decision and Negotiation, 157-169: Springer.
 
Theil, Henri 1963. On the symmetry approach to the committee decision problem. Management Science 9: 380-393.
 
Torra, Vicenç 2010. Hesitant fuzzy sets. International Journal of Intelligent Systems 25: 529-539.
 
Torra, Vicenç and Yasuo Narukawa 2009. On hesitant fuzzy sets and decision. In On hesitant fuzzy sets and decision, Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on, 1378-1382: IEEE.
 
Turksen, I Burhan 1986. Interval valued fuzzy sets based on normal forms. Fuzzy sets and Systems 20: 191-210.
 
Van den Honert, RC 2001. Decisional power in group decision making: a note on the allocation of group members' weights in the multiplicative AHP and SMART. Group Decision and Negotiation 10: 275-286.
 
Vis, Barbara, Jaap Woldendorp and Hans Keman 2013. Examining variation in economic performance using fuzzy-sets. Quality & Quantity 47: 1-19.
 
Wang, Yu-Jie 2014. A fuzzy multi-criteria decision-making model by associating technique for order preference by similarity to ideal solution with relative preference relation. Information Sciences 268: 169-184.
 
Xia, Meimei, Zeshui Xu and Na Chen 2013. Some hesitant fuzzy aggregation operators with their application in group decision making. Group Decision and Negotiation 22: 259-279.
 
Xu, Yejun, Lei Chen, Rosa M Rodríguez, Francisco Herrera and Huimin Wang 2016. Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making. Knowledge-Based Systems 99: 71-78.
 
Xu, Zeshui 2008. Group decision making based on multiple types of linguistic preference relations. Information Sciences 178: 452-467.
 
Xu, Zeshui and Xiaolu Zhang 2013. Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowledge-Based Systems 52: 53-64.
Yang, XH, DX She, ZF Yang, QH Tang and JQ Li 2009. Chaotic Bayesian method based on multiple criteria decision making (MCDM) for forecasting nonlinear hydrological time series. International Journal of Nonlinear Sciences and Numerical Simulation 10: 1595-1610.
 
Yue, Zhongliang 2011. An extended TOPSIS for determining weights of decision makers with interval numbers. Knowledge-Based Systems 24: 146-153.
 
Zadeh, Lotfi A 1975. The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences 8: 199-249.
 
Zadeh, Lotfi A 1965. Fuzzy sets. Information and control 8: 338-353.
 
Zeleny, Milan and James L Cochrane 1982. Multiple criteria decision making: McGraw-Hill New York.
 
Zhang, Zhiming 2013. Hesitant fuzzy power aggregation operators and their application to multiple attribute group decision making. Information Sciences 234: 150-181.
 
Zhou, M, Q Chen and YL Cai 2012. Optimizing the industrial structure of a watershed in association with economic-environmental consideration: an inexact fuzzy multi-objective programming model. Journal of Cleaner Production 47: 116–131.