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A Dynamic Reference Point Method for Emergency Response Under Hesitant Probabilistic Fuzzy Environment

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Abstract

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.

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References

  1. Yang, L., Fan, Z.P., Zhang, Y.: Risk decision analysis in emergency response: a method based on cumulative prospect theory. Comput. Oper. Res. 42, 75–82 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  2. Yang, L., Fan, Z.P., Yuan, Y., Li, H.Y.: A FTA-based method for risk decision-making in emergency response. Comput. Oper. Res. 42, 49–57 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  3. Hämäläinen, R.P., Lindstedt, M.R., Sinkko, K.: Multiattribute risk analysis in nuclear emergency management. Risk Anal. 20(4), 455–468 (2000)

    Article  Google Scholar 

  4. Shim, K.C., Fontane, D.G., Labadie, J.W.: Spatial decision support system for integrated river basin flood control. J. Water Resour. Plan. Manag. 128(3), 190–201 (2002)

    Article  Google Scholar 

  5. Levy, J.K., Taji, K.: Group decision support for hazards planning and emergency management: a Group Analytic Network Process (GANP) approach. Math. Comput. Model. 46(7), 906–917 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Fu, G.: A fuzzy optimization method for multicriteria decision making: an application to reservoir flood control operation. Expert Syst. Appl. 34(1), 145–149 (2008)

    Article  Google Scholar 

  7. Zhang, G.Q., Ma, J., Lu, J.: Emergency management evaluation by a fuzzy multi-criteria group decision support system. Stoch. Environ. Res. Risk Assess. 23(4), 517–527 (2009)

    Article  MathSciNet  Google Scholar 

  8. Zhou, Q., Huang, W.L., Zhang, Y.: Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Saf. Sci. 49(2), 243–252 (2011)

    Article  Google Scholar 

  9. Yan, F., Chen, L.S., Zhang, F., Zhao, Y.F.: Application of improved FAHP for nuclear reactor accident emergency decision. Appl. Mech. Mater. 389, 136–142 (2013)

    Article  Google Scholar 

  10. Zhang, Q.S., Xie, B.L., Zhang, X.M.: Uncertain internet public opinion emergency decision system based on case reasoning and grey relational analysis. Open Cybern. Syst. J. 8, 274–282 (2014)

    Article  Google Scholar 

  11. Sun, B.Z., Ma, W.M., Chen, X.T.: Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision making. Expert Syst. 32(4), 507–521 (2015)

    Article  Google Scholar 

  12. Qu, J.H., Meng, X.L., You, H.: Multi-stage ranking of emergency technology alternatives for water source pollution accidents using a fuzzy group decision making tool. J. Hazard. Mater. 310, 68–81 (2016)

    Article  Google Scholar 

  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)

  14. Pauwels, N., Van De Walle, B., Hardeman, F., Soudan, K.: The implications of irreversibility in emergency response decisions. Theory Decis. 49(1), 25–51 (2000)

    Article  MATH  Google Scholar 

  15. Yang, J.J., Xu, C.H.: Emergency decision engineering model based on sequential games. Syst. Eng. Proced. 5, 276–282 (2012)

    Article  MathSciNet  Google Scholar 

  16. Ge, L., Mourits, M.C., Kristensen, A.R., Huirne, R.B.: A modelling approach to support dynamic decision-making in the control of FMD epidemics. Prev. Vet. Med. 95(3), 167–174 (2010)

    Article  Google Scholar 

  17. Georgiadou, P.S., Papazoglou, I.A., Kiranoudis, C.T., Markatos, N.C.: Modeling emergency evacuation for major hazard industrial sites. Reliab. Eng. Syst. Saf. 92(10), 1388–1402 (2007)

    Article  Google Scholar 

  18. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  19. Dubois, D., Prade, H.: Bridging gaps between several forms of granular computing. Granul. Comput. 1(2), 115–126 (2016)

    Article  Google Scholar 

  20. Pérez, I.J., Cabrerizo, F.J., Herrera-Viedma, E.: A mobile decision support system for dynamic group decision making problems. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 40(6), 1244–1256 (2010)

    Article  Google Scholar 

  21. Yao, Y.: A triarchic theory of granular computing. Granul. Comput. 1(2), 145–157 (2016)

    Article  Google Scholar 

  22. Pedrycz, W., Rocha, A.F.: Fuzzy-set based models of neurons and knowledge-based networks. IEEE Trans. Fuzzy Syst. 1(4), 254–266 (1993)

    Article  Google Scholar 

  23. Cai, M., Li, Q., Lang, G.: Shadowed sets of dynamic fuzzy sets. Granul. Comput. 1–10 (2016)

  24. Wilke, G., Portmann, E.: Granular computing as a basis of human–data interaction: a cognitive cities use case. Granul. Comput. 1(3), 181–197 (2016)

    Article  Google Scholar 

  25. Zhou, X.: Membership grade mining of mutually inverse fuzzy implication propositions. Granul. Comput. 2(1), 55–62 (2017)

    Article  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)

  27. Wang, C., Fu, X., Meng, S., He, Y.: SPIFGIA operators and their applications to decision making. Granul. Comput. 1–10 (2016)

  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)

  29. Turksen, I.B.: Interval valued fuzzy sets based on normal forms. Fuzzy Sets Syst. 20(2), 191–210 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  30. Xu, Z.S., Gou, X.J.: An overview of interval-valued intuitionistic fuzzy information aggregations and applications. Granul. Comput. 2, 1–27 (2017)

    Article  Google Scholar 

  31. Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications, pp. 1–12. Academic Press, New York (1980)

    MATH  Google Scholar 

  32. Mendel, J.M.: A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul. Comput. 1(1), 59–69 (2016)

    Article  Google Scholar 

  33. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986)

    Article  MATH  Google Scholar 

  34. Das, S., Kar, S., Pal, T.: Robust decision making using intuitionistic fuzzy numbers. Granul. Comput. 2(1), 41–54 (2017)

    Article  Google Scholar 

  35. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)

    MATH  Google Scholar 

  36. Zhang, Z.M., Wang, C., Tian, D.Z., Li, K.: Induced generalized hesitant fuzzy operators and their application to multiple attribute group decision making. Comput. Ind. Eng. 67, 116–138 (2014)

    Article  Google Scholar 

  37. Zhang, Z.M.: Hesitant fuzzy power aggregation operators and their application to multiple attribute group decision making. Inf. Sci. 234, 150–181 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  38. Liao, H.C., Xu, Z.S., Zeng, X.J.: Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inf. Sci. 271, 125–142 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  39. Xu, Z.S., Wang, H.: Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul. Comput. 1(1), 21–35 (2016)

    Article  Google Scholar 

  40. Busemeyer, J.R., Pleskac, T.J.: Theoretical tools for understanding and aiding dynamic decision making. J. Math. Psychol. 53(3), 126–138 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  41. Saaty, T.L.: Time dependent decision-making; dynamic priorities in the AHP/ANP: generalizing from points to functions and from real to complex variables. Math. Comput. Model. 46(7), 860–891 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  42. Li, L., Lai, K.K.: Fuzzy dynamic programming approach to hybrid multiobjective multistage decision-making problems. Fuzzy Sets Syst. 117(1), 13–25 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  43. Chen, S.Y., Fu, G.T.: Combining fuzzy iteration model with dynamic programming to solve multiobjective multistage decision making problems. Fuzzy Sets Syst. 152(3), 499–512 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  44. Nielsen, T.D., Jaffray, J.Y.: Dynamic decision making without expected utility: an operational approach. Eur. J. Oper. Res. 169(1), 226–246 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  45. Bulinskaya, E.V.: Some aspects of decision making under uncertainty. J. Stat. Plan. Inference 137(8), 2613–2632 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  46. Xu, Z.S., Yager, R.R.: Dynamic intuitionistic fuzzy multi-attribute decision making. Int. J. Approx. Reason. 48(1), 246–262 (2008)

    Article  MATH  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)

  48. Xu, Z.S., Xia, M.M.: Distance and similarity measures for hesitant fuzzy sets. Inf. Sci. 181(11), 2128–2138 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  49. Diamond, P., Kloeden, P.E.: Metric Spaces of Fuzzy Sets: Theory and Applications. World Scientific, Singapore (1994)

    Book  MATH  Google Scholar 

  50. Kacprzyk, J.: Multistage Fuzzy Control: A Prescriptive Approach. Wiley, New York (1997)

    MATH  Google Scholar 

  51. Zhu, B., Xu, Z.S.: Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Trans. Fuzzy Syst. 22(1), 35–45 (2014)

    Article  Google Scholar 

  52. Chen, S.J., Hwang, C.L.: Fuzzy Multiple Attribute Decision Making. Springer, Berlin (1992)

    Book  MATH  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 

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Acknowledgments

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).

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Correspondence to Zeshui Xu.

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Gao, J., Xu, Z. & Liao, H. A Dynamic Reference Point Method for Emergency Response Under Hesitant Probabilistic Fuzzy Environment. Int. J. Fuzzy Syst. 19, 1261–1278 (2017). https://doi.org/10.1007/s40815-017-0311-4

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