Stochastic network models for logistics planning in disaster relief

Alem, Douglas; Clark, Alistair; Moreno, Alfredo

Abstract

© 2016 Elsevier B.V. All rights reserved. Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable information about the location and needs of victims, possible random supplies and donations, precarious transport links, scarcity of resources, and so on. This paper develops a new two-stage stochastic network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context. The model takes into account practical characteristics that have been neglected by the literature so far, such as budget allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of the model via state-of-art risk measures, such as semideviation and conditional value-at-risk. A simple two-phase heuristic to solve the problem within a reasonable amount of computing time is also suggested. Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model can help plan and organise relief to provide good service levels in most scenarios, and how this depends on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real and random instances.

• Journal Article Type: Article
• Acceptance Date: Apr 21, 2016
• Online Publication Date: May 4, 2016
• Publication Date: Nov 16, 2016
• Deposit Date: Apr 21, 2016
• Publicly Available Date: May 4, 2018
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 255
• Issue: 1
• Pages: 187-206
• DOI: https://doi.org/10.1016/j.ejor.2016.04.041
• Keywords: OR in disaster relief, humanitarian logistics, emergency logistics planning, two-stage stochastic programming, risk-aversion
• Public URL: https://uwe-repository.worktribe.com/output/906074
• Publisher URL: http://dx.doi.org/10.1016/j.ejor.2016.04.041
• Contract Date: Apr 21, 2016