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Rollout algorithms for resource allocation in humanitarian logistics

Author

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  • Lina Yu
  • Huasheng Yang
  • Lixin Miao
  • Canrong Zhang

Abstract

Large-scale disasters and catastrophic events typically result in a significant shortage of critical resources, posing a great challenge to allocating limited resources among different affected areas to improve the quality of emergency logistics operations. This article pays attention to the performance of resource allocation, which includes three metrics: efficiency, effectiveness, and equity, respectively corresponding to economic cost, service quality, and fairness. In particular, the effectiveness metric considers human suffering by depicting it as deprivation cost, an economic valuation measurement that has been recently proposed and the equity metric concerns about the service equality at the end of planning horizon. A nonlinear integer model is first proposed and then an equivalent dynamic programming model is developed to avoid the nonlinear terms created by the introduction of the deprivation cost. The dynamic programming method can solve small-scale problems to optimality but meets difficulty when solving medium- and large-scale problems, due to the curse of dimensionality. Therefore, an approximate dynamic programming algorithm, called the rollout algorithm, is proposed to overcome this computational difficulty. The computational complexity of the proposed algorithm is theoretically analyzed. Furthermore, a modified version of the rollout algorithm is presented, with its computational complexity analyzed. Extensive numerical experiments are conducted to test the performance of the proposed algorithms, and the experimental results demonstrate that the initially proposed rollout algorithm yields optimal or near-optimal solutions within a reasonable amount of time. In addition, the impacts of some important parameters are investigated and managerial insights are drawn.

Suggested Citation

  • Lina Yu & Huasheng Yang & Lixin Miao & Canrong Zhang, 2019. "Rollout algorithms for resource allocation in humanitarian logistics," IISE Transactions, Taylor & Francis Journals, vol. 51(8), pages 887-909, August.
  • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:8:p:887-909
    DOI: 10.1080/24725854.2017.1417655
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    Citations

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    Cited by:

    1. Anderson Nunes Silva & Marcele Elisa Fontana, 2024. "A New Model to Consolidate Long-Term Intersectoral Partnerships in Humanitarian and Social Crises Management," Public Organization Review, Springer, vol. 24(1), pages 27-51, March.
    2. 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.
    3. Tanzid Hasnain & Irem Sengul Orgut & Julie Simmons Ivy, 2021. "Elicitation of Preference among Multiple Criteria in Food Distribution by Food Banks," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4475-4500, December.
    4. Yang, Yongjian & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Dhamotharan, Lalitha, 2023. "Distributionally robust multi-period location-allocation with multiple resources and capacity levels in humanitarian logistics," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1042-1062.
    5. Guo, Penghui & Zhu, Jianjun, 2023. "Capacity reservation for humanitarian relief: A logic-based Benders decomposition method with subgradient cut," European Journal of Operational Research, Elsevier, vol. 311(3), pages 942-970.
    6. Moiz Ahmad & Muhammad Babar Ramzan & Muhammad Omair & Muhammad Salman Habib, 2024. "Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios," Mathematics, MDPI, vol. 12(13), pages 1-32, June.
    7. Fan, Yu & Shao, Jianfang & Wang, Xihui, 2023. "Relief items procurement and delivery through cooperation with suppliers and logistics companies considering budget constraints," International Journal of Production Economics, Elsevier, vol. 264(C).
    8. 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).
    9. Liu, Kanglin & Zhang, Hengliang & Zhang, Zhi-Hai, 2021. "The efficiency, equity and effectiveness of location strategies in humanitarian logistics: A robust chance-constrained approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    10. Qiang Wei & Xinyu Gou & Baiyang Zhang, 2024. "Urban Sharing Logistics Strategies against Epidemic Outbreaks: Its Feasibility and Sustainability," Sustainability, MDPI, vol. 16(17), pages 1-29, September.
    11. Fan, Yu & Wang, Xihui & Zhu, Anqi & Shao, Jianfang & Liang, Liang, 2024. "Measuring the shortage cost through deprivation and envy in collaborating contract between the local authority and the enterprise," International Journal of Production Economics, Elsevier, vol. 271(C).
    12. Liu, Kanglin & Liu, Changchun & Xiang, Xi & Tian, Zhili, 2023. "Testing facility location and dynamic capacity planning for pandemics with demand uncertainty," European Journal of Operational Research, Elsevier, vol. 304(1), pages 150-168.

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