Multistage stochastic programming for integrated network optimization in hurricane relief logistics and evacuation planning

In this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model...

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Bibliographic Details
Published in:Networks 2025-01, Vol.85 (1), p.3-37
Main Authors: Bhattarai, Sudhan, Song, Yongjia
Format: Article
Language:English
Subjects:
Adaptive sampling
disaster relief logistics network optimization
Evacuation
hurricane evacuation
Logistics
Markov chain
Markov chains
multistage stochastic programming
Parameter sensitivity
Policies
Sensitivity analysis
Stochastic programming
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Summary:In this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters.
ISSN:0028-3045
1097-0037
DOI:10.1002/net.22249