A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands

The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, in- cluding the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A Demand prediction-Network optimization-Decision adjustment framework is proposed for the emergency logistic network design. The authors first present an im- proved short-term epidemic model to predict the evolutionary trajectory of the epidemic. Then, considering the uncertainty of the estimated demands, they construct a capacitated multi-period, multi-echelon facility deployment and resource allocation robust optimiza- tion model to improve the reliability of the decisions. To address the conservativeness of robust solutions during the evolution of the epidemic, an uncertainty budget adjustment strategy is proposed and integrated into the rolling horizon optimization approach. The results of the case study show that (i) the short-term prediction method has higher ac-curacy and the accuracy increases with the amount of observed data; (ii) considering thedemand uncertainty, the proposed robust optimization model combined with uncertain-ty budget adjustment strategy can improve the performance of the emergency logisticnetwork; (iii) the proposed solution method is more efficient than its benchmark, espe-cially for large-scale cases. Moreover, some managerial insights related to the emergency logistics network design problem are presented.

• Record URL:
  https://doi.org/10.1016/j.tre.2023.103087
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1366554523000753
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Jianghua
  • Long, Daniel Zhuoyu
  • Li, Yuchen
  • 0000-0001-8674-8017
• Publication Date: 2023-4

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 103087
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 172
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: COVID-19; Demand; Emergency response time; Logistics; Predictive models; Resource allocation
• Subject Areas: Highways; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01876192
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 21 2023 9:27AM