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A multi-stage conflict style large group emergency decision-making method

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Abstract

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.

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Acknowledgments

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.

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Correspondence to Xuan-hua Xu.

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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”.

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Communicated by V. Loia.

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Cai, Cg., Xu, Xh., Wang, P. et al. A multi-stage conflict style large group emergency decision-making method. Soft Comput 21, 5765–5778 (2017). https://doi.org/10.1007/s00500-016-2155-5

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