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Post-Natural Disasters Emergency Response Scheme Selection: An Integrated Application of Probabilistic T-Spherical Hesitant Fuzzy Set, Penalty-Incentive Dynamic Attribute Weights, and Non-Compensation Approach
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Post-Natural Disasters Emergency Response Scheme Selection: An Integrated Application of Probabilistic T-Spherical Hesitant Fuzzy Set, Penalty-Incentive Dynamic Attribute Weights, and Non-Compensation Approach
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Journal Article
Post-Natural Disasters Emergency Response Scheme Selection: An Integrated Application of Probabilistic T-Spherical Hesitant Fuzzy Set, Penalty-Incentive Dynamic Attribute Weights, and Non-Compensation Approach
2024
Overview
This paper presents an innovative methodology for the dynamic emergency response scheme selection (ERSS) problem in post-major natural disasters. It employs a combination of subjective and objective composite weights and the integrated ELECTRE-score approach. The study aims to provide a practical approach for continuously determining optimal decision schemes at various time points during the decision period in the aftermath of significant natural disasters while accommodating evolving real-world scenarios. Firstly, the probabilistic T-spherical hesitant fuzzy set (Pt-SHFS) captures decision-makers’ ambivalence and hesitation regarding diverse evaluation attributes of different schemes. Subsequently, Pt-SHFS is integrated with the best–worst method (BWM) to determine subjective weights, followed by the structured CRITIC method to amalgamate subjective weights and derive the final combination weights of criteria. Additionally, this paper proposes applying a penalty-incentive mechanism to establish dynamic attribute weights during scenario evolution. Furthermore, the ELECTRE-score method, which may fully exploit the advantages of non-compensation situations, is adopted to obtain more reliable dynamic optimal decision outcomes. Consequently, based on these foundations, an integrated dynamic ERSS approach is formulated to determine optimal dynamic emergency response schemes. Finally, a case study on the Gansu Jishishan earthquake, sensitivity analysis, comparative analysis, and continuous analysis are conducted to verify the practicality, stability, and effectiveness of the proposed approach. The result shows that the proposed comprehensive approach can depict variances among experts’ information, dynamically adjust attribute weights in response to evolving scenarios, and assign a score range and a representative score to each scheme at each decision state. Sensitivity and comparative analyses show this model has strong stability and dynamics. Furthermore, the proposed approach can effectively deal with the complex dynamic situation in the earthquake rescue process, such as the secondary collapse of buildings after the earthquake, the damage of materials caused by heavy rain, and the occurrence of aftershocks. The model can continuously optimize decision-making and provide scientific and reliable support for emergency decision-making.
Publisher
MDPI AG
