Robust dual sourcing inventory routing optimization for disaster relief

This paper considers the problem that a depot replenishes several shelters by aerial and land transportation modes for disaster relief. There are two distinguishing features of our problem: one is routing decisions determine replenishment lead times; the other is that we introduce dual sourcing poli...

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Detalles Bibliográficos
Autores principales: Zheng, Weibo, Zhou, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138234/
https://www.ncbi.nlm.nih.gov/pubmed/37104462
http://dx.doi.org/10.1371/journal.pone.0284971
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author Zheng, Weibo
Zhou, Hong
author_facet Zheng, Weibo
Zhou, Hong
author_sort Zheng, Weibo
collection PubMed
description This paper considers the problem that a depot replenishes several shelters by aerial and land transportation modes for disaster relief. There are two distinguishing features of our problem: one is routing decisions determine replenishment lead times; the other is that we introduce dual sourcing policy into the inventory routing problem. A robust optimization model is proposed to determine the optimal replenishment quantity, replenishment mode, and transportation routes. Then, we decompose the problem into a routing master-problem and a set of inventory sub-problems. A tractable closed-form solution for sub-problem is derived. We further develop an adaptive large neighborhood search algorithm to solve the problem. To demonstrate the feasibility of the algorithm, we conduct a series of numerical experiments on the benchmark test suite with different scales and compare the performance of the proposed algorithm with a genetic algorithm.
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spelling pubmed-101382342023-04-28 Robust dual sourcing inventory routing optimization for disaster relief Zheng, Weibo Zhou, Hong PLoS One Research Article This paper considers the problem that a depot replenishes several shelters by aerial and land transportation modes for disaster relief. There are two distinguishing features of our problem: one is routing decisions determine replenishment lead times; the other is that we introduce dual sourcing policy into the inventory routing problem. A robust optimization model is proposed to determine the optimal replenishment quantity, replenishment mode, and transportation routes. Then, we decompose the problem into a routing master-problem and a set of inventory sub-problems. A tractable closed-form solution for sub-problem is derived. We further develop an adaptive large neighborhood search algorithm to solve the problem. To demonstrate the feasibility of the algorithm, we conduct a series of numerical experiments on the benchmark test suite with different scales and compare the performance of the proposed algorithm with a genetic algorithm. Public Library of Science 2023-04-27 /pmc/articles/PMC10138234/ /pubmed/37104462 http://dx.doi.org/10.1371/journal.pone.0284971 Text en © 2023 Zheng, Zhou https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zheng, Weibo
Zhou, Hong
Robust dual sourcing inventory routing optimization for disaster relief
title Robust dual sourcing inventory routing optimization for disaster relief
title_full Robust dual sourcing inventory routing optimization for disaster relief
title_fullStr Robust dual sourcing inventory routing optimization for disaster relief
title_full_unstemmed Robust dual sourcing inventory routing optimization for disaster relief
title_short Robust dual sourcing inventory routing optimization for disaster relief
title_sort robust dual sourcing inventory routing optimization for disaster relief
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138234/
https://www.ncbi.nlm.nih.gov/pubmed/37104462
http://dx.doi.org/10.1371/journal.pone.0284971
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