Unconventional emergencies usually have the characteristics of complexity, dynamic, and unpredictability, which greatly enhances the difficulty of emergency decision-making. Aiming at the multi-stage large group emergency decision-making problem featuring unknown stage weight and preference information expressed as interval numbers, we propose a new decision-making method. First, we present a similarity measurement formula for interval numbers. Each stage’s preference information is clustered using this similarity. To minimize the conflict of preferences, we derived two relative entropy optimization models to calculate the aggregation and stage weights. Next, we rank the alternatives based on the comprehensive group preference information. Finally, we present an illustrative example to verify the validity and practicability of this approach, and discuss several advantages of this method for managing emergency decision-making problems.
Multiple stage Conflict Large group Group decision-making
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The authors would like to thank the editors and anonymous reviewers for their insightful comments and suggestions. This paper was supported by grants from the Natural Science Foundation of China (71171202, 71210003, 71431006), the Mobile E-business Collaborative Innovation Center of Hunan Province, and the Key Laboratory of Hunan Province for Mobile Business Intelligence.
Compliance with ethical standards
Conflict of interest
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be constructed as influencing the position presented in, or the review of the manuscript entitled “A Multi-stage Conflict Style Large Group Emergency Decision-making Method”.
Alonso S, Pérez IJ, Cabrerizo FJ (2013) A linguistic consensus model for web 2.0 communities. Appl Soft Comput 13(1):149–157CrossRefGoogle Scholar
Baležentis T, Zeng SZ (2013) Group multi-criteria decision making based upon interval-valued fuzzy numbers: an extension of the MULTIMOORA method. Expert Syst Appl 40(2):543–550CrossRefGoogle Scholar
Chai JY, Liu JNK, Xu ZS (2013) A rule-based group decision model for warehouse evaluation under interval-valued Intuitionistic fuzzy environments. Expert Syst Appl 40(6):1959–1970CrossRefGoogle Scholar
Chen XH, Liu YF (2010) Expert weights determination method and realization algorithm based on interval numbers group decision matrices. Syst Eng Electron 32(10):2128–2131zbMATHGoogle Scholar
Facchinetti G, Ricci RG, Muzzioli S (1998) Note on raking fuzzy triangular numbers. Int J Intell Syst 13(7):613–622CrossRefGoogle Scholar
Fishburn PC, Gehrlein WV (1976) Borda’s rule, positional voting and condorcet’s simple majority principle. Public Choice 28(1):79–88CrossRefGoogle Scholar
Goh CH, Tung YC, Cheng CH (1996) A revised weighted sum decision model for robot selection. Comput Ind Eng 30(2):193–199CrossRefGoogle Scholar
Kuo MS, Liang GS (2012) A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Appl Soft Comput 12(1):476–485CrossRefGoogle Scholar
Liu XY, Ju YB, Wang AH (2012) A multiple attribute group decision making method with its application to emergency alternative assessment. J Converg Inf Technol 7(2):75–82Google Scholar
Mao JJ, Yao DB, Wang CC (2013) A novel cross-entropy and entropy measures of IFSs and their applications. Knowl Based Syst 48:37–45CrossRefGoogle Scholar
Meysam Mousavi S, Behnam V, Tavakkoli-Moghaddam R et al (2013) A multi-stage decision-making process for multiple attributes analysis under an interval-valued fuzzy environment. Int J Adv Manuf Technol 64(9):1263–1273CrossRefGoogle Scholar
Mousavi SM, Makui A, Raissi S (2012) A multi-criteria decision-making approach with interval numbers for evaluating project risk responses. Int J Eng 25(5):121–129CrossRefGoogle Scholar
Perez IJ, Cabrerizo FJ, Herrera-Viedma E (2010) A mobile decision support system for dynamic group decision-making problems. IEEE Trans Syst Man Cybern Part A Syst Hum 40(6):1244–1256Google Scholar
Shim KC, Fontane DG, Labadie JW (2002) Spatial decision support system for integrated river basin flood control. J Water Resour Plan Manag 128(3):190–201CrossRefGoogle Scholar
Song GX, Yang H (2000) Research on group behavioral decision making. Acad Explor 57(3):48–49Google Scholar
Su ZX, Chen MY, Xia PP et al (2011) An interactive method for dynamic intuitionistic fuzzy multi-attribute group decision making. Expert Syst Appl 38(12):15286–15295CrossRefGoogle Scholar
Wei GW, Zhao XF, Lin R (2012) Generalized triangular fuzzy correlated averaging operator and their application to multiple attribute decision making. Appl Math Model 36(7):2975–2982MathSciNetCrossRefzbMATHGoogle Scholar
Xia MM, Xu ZS (2012) Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment. Inf Fusion 13(1):31–47MathSciNetCrossRefGoogle Scholar
Xu ZS (2001) Algorithm for priority of fuzzy complementary judgement matrix. J Syst Eng 16(4):311–314Google Scholar
Xu ZS (2007) Multiple attribute group decision making with different formats of preference information on attributes. IEEE Trans Syst Man Cybern Part B 37(6):1500–1511MathSciNetCrossRefGoogle Scholar
Xu ZS, Cai XQ (2013) On consensus of group decision making with interval utility values and interval preference orderings. Group Decis Negot 22(6):997–1019CrossRefGoogle Scholar
Xu XH, Chen XH (2011) A conflict measure model and corresponding conflict coordination mechanism in large group decisions. J Syst Sci Inf 9(1):1–18Google Scholar
Xu JP, Wu ZB, Zhang Y (2014) A consensus based method for multi-criteria group decision making under uncertain linguistic setting. Group Decis Negot 23(1):127–148CrossRefGoogle Scholar