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Resource location for relief distribution and victim evacuation after a sudden-onset disaster

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  • Eko Setiawan
  • Jiyin Liu
  • Alan French

Abstract

Quick responses to sudden-onset disasters and the effective allocation of rescue and relief resources are vital for saving lives and reducing the suffering of the victims. This article deals with the problem of positioning medical and relief distribution facilities after a sudden-onset disaster event. The background of this study is the situation in Padang Pariaman District after the West Sumatra earthquake. Three models are built for the resource location and deployment decisions. The first model reflects current practice where relief distribution and victim evacuation are performed separately and relief is distributed by distribution centers within administrative boundaries. The second model allows relief to be distributed across boundaries by any distribution center. The third model further breaks down functional barriers to allow the evacuation and relief distribution operations share vehicles. These models are solved directly for small problems and by using a direct approach as well as heuristics for large problems. Test results on small problems show that resource sharing measures, both across boundaries and across different functions, improve on current practice. For large problems, the results give similar conclusions to those for small problems when each model is solved using its own best approach.

Suggested Citation

  • Eko Setiawan & Jiyin Liu & Alan French, 2019. "Resource location for relief distribution and victim evacuation after a sudden-onset disaster," IISE Transactions, Taylor & Francis Journals, vol. 51(8), pages 830-846, August.
    • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:8:p:830-846
    DOI: 10.1080/24725854.2018.1517284
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    Cited by:

    1. Vahdani, Behnam & Veysmoradi, D. & Mousavi, S.M. & Amiri, M., 2022. "Planning for relief distribution, victim evacuation, redistricting and service sharing under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    2. Li, Yuchen & Zhang, Jianghua & Yu, Guodong, 2020. "A scenario-based hybrid robust and stochastic approach for joint planning of relief logistics and casualty distribution considering secondary disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    3. Farahani, Reza Zanjirani & Lotfi, M.M. & Baghaian, Atefe & Ruiz, Rubén & Rezapour, Shabnam, 2020. "Mass casualty management in disaster scene: A systematic review of OR&MS research in humanitarian operations," European Journal of Operational Research, Elsevier, vol. 287(3), pages 787-819.
    4. Shengjie Long & Dezhi Zhang & Shuangyan Li & Shuanglin Li, 2023. "Two-Stage Multi-Objective Stochastic Model on Patient Transfer and Relief Distribution in Lockdown Area of COVID-19," IJERPH, MDPI, vol. 20(3), pages 1-25, January.
    5. Sun, Huali & Li, Jiamei & Wang, Tingsong & Xue, Yaofeng, 2022. "A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    6. Camur, Mustafa C. & Sharkey, Thomas C. & Dorsey, Clare & Grabowski, Martha R. & Wallace, William A., 2021. "Optimizing the response for Arctic mass rescue events," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).

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