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Disaster relief routing under uncertainty: A robust optimization approach

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  • Yinglei Li
  • Sung Hoon Chung

Abstract

This article addresses the Capacitated Vehicle Routing Problem (CVRP) and the Split Delivery Vehicle Routing Problem (SDVRP) with uncertain travel times and demands when planning vehicle routes for delivering critical supplies to a population in need after a disaster. A robust optimization approach is used for CVRP and SDVRP considering the five objective functions: minimization of the total number of vehicles deployed (minV), the total travel time/travel cost (minT), the summation of arrival times (minS), the summation of demand-weighted arrival times (minD), and the latest arrival time (minL), out of which we claim that minS, minD, and minL are critical for deliveries to be fast and fair for relief efforts whereas minV and minT are common cost-based objective functions in the traditional VRP. A new two-stage heuristic method that combines the extended insertion algorithm and tabu search is proposed to solve the VRP models for large-scale problems. The solutions of CVRP and SDVRP are compared for different examples using five different metrics in which we show that the latter is not only capable of accommodating the demand greater than the vehicle capacity but also is quite effective to mitigate demand and travel time uncertainty, and thereby outperforms CVRP in the disaster relief routing perspective.

Suggested Citation

  • Yinglei Li & Sung Hoon Chung, 2019. "Disaster relief routing under uncertainty: A robust optimization approach," IISE Transactions, Taylor & Francis Journals, vol. 51(8), pages 869-886, August.
    • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:8:p:869-886
    DOI: 10.1080/24725854.2018.1450540
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    Cited by:

    1. Bruni, M.E. & Khodaparasti, S. & Beraldi, P., 2020. "The selective minimum latency problem under travel time variability: An application to post-disaster assessment operations," Omega, Elsevier, vol. 92(C).
    2. Davood Shiri & Vahid Akbari & F. Sibel Salman, 2020. "Online routing and scheduling of search-and-rescue teams," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 755-784, September.
    3. Xuming Wang & Jiaqi Zhou & Xiaobing Yu & Xianrui Yu, 2023. "A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model," Sustainability, MDPI, vol. 15(10), pages 1-31, May.
    4. 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).
    5. Balcik, Burcu & Yanıkoğlu, İhsan, 2020. "A robust optimization approach for humanitarian needs assessment planning under travel time uncertainty," European Journal of Operational Research, Elsevier, vol. 282(1), pages 40-57.
    6. Joaquín Pacheco & Manuel Laguna, 2020. "Vehicle routing for the urgent delivery of face shields during the COVID-19 pandemic," Journal of Heuristics, Springer, vol. 26(5), pages 619-635, October.
    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. 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.
    9. Yin, Yunqiang & Yang, Yongjian & Yu, Yugang & Wang, Dujuan & Cheng, T.C.E., 2023. "Robust vehicle routing with drones under uncertain demands and truck travel times in humanitarian logistics," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    10. 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).
    11. 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).

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