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Method for large group emergency decision making with complex preferences based on emergency similarity and interval consistency

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  • Xuanhua Xu

    (Business School of Central South University)

  • Yanxia Huang

    (Business School of Central South University)

  • Ke Chen

    (Business School of Central South University)

Abstract

The evolution of emergency scenarios is usually hard to predict. To address this problem, a method for determining the severity of emergencies is proposed based on a decision paradigm obtained from the analysis of similar cases. First, the standardized indicators in the base case are used to input the target event so as to filter out some similar events as references. The severity of reference events is used to acquire the severity of the target event. When the target event evolves into an event whose average severity exceeds that of a major emergency, large group emergency decision making is initiated. Second, based on the fact that there is consistency interval existing in a hesitant fuzzy preference matrix, a method to measure the rationality of experts and determine their weights using an interval consistency composed of the average consistency and standard deviation indices is proposed in this paper. Finally, an example is given to illustrate the effectiveness of the proposed method.

Suggested Citation

  • Xuanhua Xu & Yanxia Huang & Ke Chen, 2019. "Method for large group emergency decision making with complex preferences based on emergency similarity and interval consistency," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(1), pages 45-64, May.
    • Handle: RePEc:spr:nathaz:v:97:y:2019:i:1:d:10.1007_s11069-019-03624-1
    DOI: 10.1007/s11069-019-03624-1
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    References listed on IDEAS

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    1. Zhou, Wei & Xu, Zeshui, 2016. "Generalized asymmetric linguistic term set and its application to qualitative decision making involving risk appetites," European Journal of Operational Research, Elsevier, vol. 254(2), pages 610-621.
    2. Yanbing Ju & Aihua Wang & Tianhui You, 2015. "Emergency alternative evaluation and selection based on ANP, DEMATEL, and TL-TOPSIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 347-379, February.
    3. Jun Yang & Jinhong Chen & Huiliang Liu & Jingchen Zheng, 2014. "Comparison of two large earthquakes in China: the 2008 Sichuan Wenchuan Earthquake and the 2013 Sichuan Lushan Earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 1127-1136, September.
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    Cited by:

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    2. Shi, Lingyuan & Yang, Xin & Chang, Ximing & Wu, Jianjun & Sun, Huijun, 2023. "An improved density peaks clustering algorithm based on k nearest neighbors and turning point for evaluating the severity of railway accidents," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    3. Heidary-Dahooie, Jalil & Rafiee, Mostafa & Mohammadi, Mehdi & Meidute-Kavaliauskienė, Ieva, 2022. "Proposing a new LSGDM framework based on BWM with hesitant fuzzy information for prioritizing blockchain adoption barriers in supply chain," Technology in Society, Elsevier, vol. 71(C).
    4. Milad Zamanifar & Timo Hartmann, 2020. "Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 1-25, October.
    5. Tang, Ming & Liao, Huchang, 2021. "From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey," Omega, Elsevier, vol. 100(C).

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