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Robust location-allocation network design for earthquake preparedness

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  • Paul, Jomon A.
  • Wang, Xinfang (Jocelyn)

Abstract

We develop robust models for earthquake preparedness by optimizing the number, location, and capacity of distribution centers (DCs). The goal is to minimize the total social costs, which include setup and initial supplies, as well as the deprivation costs associated with delayed access to supplies. The models incorporate various earthquake magnitude-specific uncertainties, such as facility damage, casualty by severity, and travel time. Examining the concept of social costs in light of an emerging concern in humanitarian logistics - the robustness of relief networks, we model two types of robustness: parameter uncertainty within a scenario and relative regret across scenarios. This unique approach reveals (1) the magnitude of social costs in the aftermath of an earthquake; (2) the hidden risks associated with inaccurate modeling of deprivation costs; and (3) the impact of budgetary constraints. We demonstrate the applicability of our approach via a case study featuring the Northridge region in California, which experienced two of the strongest earthquakes recorded in North America in 1971 and 1994.

Suggested Citation

  • Paul, Jomon A. & Wang, Xinfang (Jocelyn), 2019. "Robust location-allocation network design for earthquake preparedness," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 139-155.
  • Handle: RePEc:eee:transb:v:119:y:2019:i:c:p:139-155
    DOI: 10.1016/j.trb.2018.11.009
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    9. Dönmez, Zehranaz & Kara, Bahar Y. & Karsu, Özlem & Saldanha-da-Gama, Francisco, 2021. "Humanitarian facility location under uncertainty: Critical review and future prospects," Omega, Elsevier, vol. 102(C).
    10. Wang, Weiqiao & Yang, Kai & Yang, Lixing & Gao, Ziyou, 2021. "Two-stage distributionally robust programming based on worst-case mean-CVaR criterion and application to disaster relief management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    11. Dayanna Rodrigues da Cunha Nunes & Orivalde Soares da Silva Júnior & Renata Albergaria de Mello Bandeira & Yesus Emmanuel Medeiros Vieira, 2023. "A Robust Stochastic Programming Model for the Well Location Problem: The Case of The Brazilian Northeast Region," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    12. Lu, Xiaohan & Cheng, Chun, 2021. "Locating facilities with resiliency to capacity failures and correlated demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    13. 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).
    14. Yusuf Kuvvetli, 2023. "A goal programming model for two-stage COVID19 test sampling centers location-allocation problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 1-20, March.
    15. Seyed Reza Abazari & Fariborz Jolai & Amir Aghsami, 2022. "Designing a humanitarian relief network considering governmental and non-governmental operations under uncertainty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1430-1452, June.
    16. Zhang, Yuwei & Li, Zhenping & Zhao, Yuwei, 2023. "Multi-mitigation strategies in medical supplies for epidemic outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
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    18. Dalal, Jyotirmoy, 2022. "Food donation management under supply and demand uncertainties in COVID-19: A robust optimization approach," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).

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