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Article: Dynamic Inventory Relocation in Disaster Relief

TitleDynamic Inventory Relocation in Disaster Relief
Authors
Keywordsdisaster relief
dynamic facility location
inventory pre-positioning
mobile inventory
polynomial time algorithm
Issue Date2022
Citation
Production and Operations Management, 2022, v. 31, n. 3, p. 1052-1070 How to Cite?
AbstractThis study investigates dynamic inventory relocation to respond proactively to the changing relief demand forecasts over time. In particular, we examine how to relocate mobile inventory optimally to serve nonstationary stochastic demand at several potential disaster sites. We propose a dynamic relocation model using dynamic programming (DP) and develop both analytical and numerical results regarding optimal relocation policies, the minimum cost-to-go function, and the value of inventory mobility over traditional warehouse pre-positioning. Given the computational complexity of the backwards DP algorithm, we develop a base state heuristic (BSH) for general problems by exploiting the real-world disaster pattern of occurrence. For problems with temporally independent demand, we propose a polynomial time exact algorithm based on a spatial–temporal graph. For problems with spatially independent demand, we design a speedup technique to implement BSH in polynomial time. The proposed model and algorithms are further extended to consider the impact of transportation uncertainties. Numerical experiments show that the proposed algorithms return high-quality decisions only in a small fraction of the time required by an exact algorithm and a myopic algorithm. The proposed model and algorithms are applicable to any type of mobile inventory, facility, or server in similar settings.
Persistent Identifierhttp://hdl.handle.net/10722/336296
ISSN
2023 Impact Factor: 4.8
2023 SCImago Journal Rankings: 3.035
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Yuli-
dc.contributor.authorRichter, Amber R.-
dc.contributor.authorShanthikumar, Jeyaveerasingam George-
dc.contributor.authorShen, Zuo Jun Max-
dc.date.accessioned2024-01-15T08:25:18Z-
dc.date.available2024-01-15T08:25:18Z-
dc.date.issued2022-
dc.identifier.citationProduction and Operations Management, 2022, v. 31, n. 3, p. 1052-1070-
dc.identifier.issn1059-1478-
dc.identifier.urihttp://hdl.handle.net/10722/336296-
dc.description.abstractThis study investigates dynamic inventory relocation to respond proactively to the changing relief demand forecasts over time. In particular, we examine how to relocate mobile inventory optimally to serve nonstationary stochastic demand at several potential disaster sites. We propose a dynamic relocation model using dynamic programming (DP) and develop both analytical and numerical results regarding optimal relocation policies, the minimum cost-to-go function, and the value of inventory mobility over traditional warehouse pre-positioning. Given the computational complexity of the backwards DP algorithm, we develop a base state heuristic (BSH) for general problems by exploiting the real-world disaster pattern of occurrence. For problems with temporally independent demand, we propose a polynomial time exact algorithm based on a spatial–temporal graph. For problems with spatially independent demand, we design a speedup technique to implement BSH in polynomial time. The proposed model and algorithms are further extended to consider the impact of transportation uncertainties. Numerical experiments show that the proposed algorithms return high-quality decisions only in a small fraction of the time required by an exact algorithm and a myopic algorithm. The proposed model and algorithms are applicable to any type of mobile inventory, facility, or server in similar settings.-
dc.languageeng-
dc.relation.ispartofProduction and Operations Management-
dc.subjectdisaster relief-
dc.subjectdynamic facility location-
dc.subjectinventory pre-positioning-
dc.subjectmobile inventory-
dc.subjectpolynomial time algorithm-
dc.titleDynamic Inventory Relocation in Disaster Relief-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/poms.13594-
dc.identifier.scopuseid_2-s2.0-85119852180-
dc.identifier.volume31-
dc.identifier.issue3-
dc.identifier.spage1052-
dc.identifier.epage1070-
dc.identifier.eissn1937-5956-
dc.identifier.isiWOS:000722611900001-

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