A Dynamic Reference Point Method for Emergency Response Under Hesitant Probabilistic Fuzzy Environment
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According to the characteristics of emergency decision-making in crisis management, this paper proposes a dynamic decision-making method using the hesitant probabilistic fuzzy set to deal with the inadequate information, uncertainty and dynamic trends. This method is suitable for emergency decision-making as it provides supports for the dynamic and evolutionary characteristics of emergency responses and the uncertain probability about external environment is also considered. In order to make a continuous adjustment with the development of situations, we give a definition of the expectation level, based on which the dynamic reference point method is proposed to obtain the optimal emergency response plan under the hesitant probabilistic fuzzy environment. We also analyze the probability of different situations that may occur in the process of emergency decision-making and provide an algorithm for solving this problem. Finally, a practical case of hazardous goods leakage pollution accident is given to illustrate our method, and then, the optimal decision alternative chain is obtained.
KeywordsEmergency decision-making Hesitant probabilistic fuzzy variable (HPFV) Hesitant probabilistic fuzzy number (HPFN) Expectation level Dynamic reference point (DRP) method
The authors thank the anonymous reviewers for their helpful comments and suggestions, which have led to an improved version of this paper. The work was supported by National Natural Science Foundation of China (Nos. 71571123, 71501135, 71532007), the Scientific Research Found of Sichuan Provincial Education Department (Nos. 16ZB0343, DSWL16-12) and the Young scholars high level academic team construction project at Sichuan University (skgt201501).
- 13.Zhao, J.D., Jin, T., Shen, H.Z.: A case-based evolutionary group decision support method for emergency response. In: Intelligence and Security Informatics, pp. 94–104. (2007)Google Scholar
- 23.Cai, M., Li, Q., Lang, G.: Shadowed sets of dynamic fuzzy sets. Granul. Comput. 1–10 (2016)Google Scholar
- 26.Syau, Y.R., Skowron, A., Lin, E.B.: Inclusion degree with variable-precision model in analyzing inconsistent decision tables. Granul. Comput. 1–8 (2016)Google Scholar
- 27.Wang, C., Fu, X., Meng, S., He, Y.: SPIFGIA operators and their applications to decision making. Granul. Comput. 1–10 (2016)Google Scholar
- 28.Sanchez, M.A., Castro, J.R., Castillo, O., Mendoza1, O., Rodriguez-Diaz, A., Melin, P.: Fuzzy higher type information granules from an uncertainty measurement. Granul. Comput. 1–9 (2016)Google Scholar
- 47.Xu, Z.S., Zhou, W.: Consensus building with a group of decision makers under the hesitant probabilistic fuzzy environment. Fuzzy Optim. Decis. Mak. 1–23 (2016)Google Scholar
- 53.Cheng, T.J., Wu, F.P., Li, J.B.: Risk decision model for emergency response based on cumulative prospective theory with incomplete information. Syst. Eng. 04, 70–75 (2014). (in Chinese) Google Scholar