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Decision making on post-disaster rescue routing problems from the rescue efficiency perspective

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  • Liu, Bingsheng
  • Sheu, Jiuh-Biing
  • Zhao, Xue
  • Chen, Yuan
  • Zhang, Wei

Abstract

The efficiency of emergency responses is at the heart of post-disaster rescue routing problems. Previous rescue models address the efficiency issue primarily by minimizing the total travel time, while not taking into account other significant factors in the rescue process, such as the number of affected people and the degree of building damage. To overcome these shortcomings, this paper aims to solve the rescue routing problem by maximizing the arc-based rescue efficiency, which is redefined as the ratio between the primary rescue input and output factors to represent the efficiency of the rescue operations. Therefore, a systematic methodology is proposed to decompose the original rescue routing problem into two decision making phases. First, an extended data envelopment analysis (DEA) model is constructed to evaluate the rescue efficiency along each arc. Specifically, a group decision constraint cone, which refers to the combined output of a group decision from experts, is constructed to distinguish the features and rescue focus of each disaster. Second, an efficiency-based routing model is developed to determine a feasible rescue tour for the entire transportation network, thus achieving the goal of maximizing the total rescue efficiency. An empirical example of a real earthquake disaster in Wenchuan, China, is provided to demonstrate the novelty and practical capabilities of the proposed approach in post-disaster emergency rescue operations. Finally, a comparison analysis is conducted with the traditional time-oriented routing method, and the results show that the method proposed in this study can improve the rescue performance by 21.2%.

Suggested Citation

  • Liu, Bingsheng & Sheu, Jiuh-Biing & Zhao, Xue & Chen, Yuan & Zhang, Wei, 2020. "Decision making on post-disaster rescue routing problems from the rescue efficiency perspective," European Journal of Operational Research, Elsevier, vol. 286(1), pages 321-335.
    • Handle: RePEc:eee:ejores:v:286:y:2020:i:1:p:321-335
    DOI: 10.1016/j.ejor.2020.03.017
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    5. Huang, Cheng-Hao & Huang, Ding-Hsiang & Lin, Yi-Kuei, 2023. "Network reliability prediction for random capacitated-flow networks via an artificial neural network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Aghaie, Sepide & Karimi, Behrooz, 2022. "Location-allocation-routing for emergency shelters based on geographical information system (ArcGIS) by NSGA-II (case study: Earthquake occurrence in Tehran (District-1))," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    7. Wang, Weiqiao & Yang, Kai & Yang, Lixing & Gao, Ziyou, 2023. "Distributionally robust chance-constrained programming for multi-period emergency resource allocation and vehicle routing in disaster response operations," Omega, Elsevier, vol. 120(C).

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