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Redesign strategies of a comprehensive robust relief network for disaster management

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  • Hasani, Aliakbar
  • Mokhtari, Hadi

Abstract

We address the problem of designing as well as redesigning a relief network over multiple periods as a strategic decision which plays a critical role in the post-disaster management. Design of the relief network has a significant impact on the effective performance of disaster response operations. For considering both the uncertainty and dynamism of the decision-making environment, a comprehensive scenario-based robust approach embedded in the rolling horizon framework is proposed. The proposed mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran, which aims to minimize the total cost of network management. Furthermore, restorative strategies are considered to increase the efficiency and robustness of the proposed relief network under disaster. To tackle the proposed optimization model, a heuristic solution algorithm is adopted. The results indicate that the proposed robust relief network provides an affordable access to its demand points in a sustainable manner under disaster. In addition, extensive computational results illustrate the efficiency of the proposed model in dealing with the considered disaster management issues.

Suggested Citation

  • Hasani, Aliakbar & Mokhtari, Hadi, 2018. "Redesign strategies of a comprehensive robust relief network for disaster management," Socio-Economic Planning Sciences, Elsevier, vol. 64(C), pages 92-102.
    • Handle: RePEc:eee:soceps:v:64:y:2018:i:c:p:92-102
    DOI: 10.1016/j.seps.2018.01.003
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    Cited by:

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    7. Shira Daskal & Adar Ben-Eliyahu & Gal Levy & Yakov Ben-Haim & Ronnen Avny, 2022. "Earthquake Vulnerability Reduction by Building a Robust Social-Emotional Preparedness Program," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    8. Yanbin Chang & Yongjia Song & Burak Eksioglu, 2022. "A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty," Annals of Operations Research, Springer, vol. 319(1), pages 1231-1263, December.

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