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Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters

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  • Baharmand, Hossein
  • Comes, Tina
  • Lauras, Matthieu

Abstract

Locating distribution centers is critical for humanitarians in the immediate aftermath of a sudden-onset disaster. A major challenge lies in balancing the complexity and uncertainty of the problem with time and resource constraints. To address this problem, we propose a location–allocation model that divides the topography of affected areas into multiple layers; considers constrained number and capacity of facilities and fleets; and allows decision-makers to explore trade-offs between response time and logistics costs. To illustrate our theoretical work, we apply the model to a real dataset from the 2015 Nepal earthquake response. For this case, our method results in a considerable reduction of logistics costs.

Suggested Citation

  • Baharmand, Hossein & Comes, Tina & Lauras, Matthieu, 2019. "Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 86-110.
    • Handle: RePEc:eee:transe:v:127:y:2019:i:c:p:86-110
    DOI: 10.1016/j.tre.2019.05.002
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    2. Afshin Kamyabniya & Antoine Sauré & F. Sibel Salman & Noureddine Bénichou & Jonathan Patrick, 2024. "Optimization models for disaster response operations: a literature review," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(3), pages 737-783, September.
    3. Jiaxin Geng & Hanping Hou & Shaoqing Geng, 2021. "Optimization of Warehouse Location and Supplies Allocation for Emergency Rescue under Joint Government–Enterprise Cooperation Considering Disaster Victims’ Distress Perception," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    4. 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).
    5. 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.
    6. Zhang, Lingye & Lu, Jing & Yang, Zaili, 2021. "Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks," European Journal of Operational Research, Elsevier, vol. 293(2), pages 529-546.
    7. Rodríguez-Espíndola, Oscar & Ahmadi, Hossein & Gastélum-Chavira, Diego & Ahumada-Valenzuela, Omar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel, 2023. "Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    8. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    9. Abushaega, Mastoor M. & González, Andrés D. & Moshebah, Osamah Y., 2024. "A fairness-based multi-objective distribution and restoration model for enhanced resilience of supply chain transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    10. Shaoren Wang & Yenchun Jim Wu & Ruiting Li, 2022. "An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies," IJERPH, MDPI, vol. 19(15), pages 1-18, August.

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