A stochastic dual dynamic integer programming based approach for remanufacturing planning under uncertainty

We seek to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. We consider a multi-stage stochastic integer programming approach and use scenario trees to represent the uncertain information structure. We introduce a new dynamic programming formulat...

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Vydáno v:International journal of production research Ročník 61; číslo 17; s. 5992 - 6012
Hlavní autoři: Quezada, Franco, Gicquel, Céline, Kedad-Sidhoum, Safia
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 02.09.2023
Taylor & Francis LLC
Témata:
Algorithms
Computer Science
Decomposition
Dynamic programming
Integer programming
lot-sizing
multi-stage stochastic integer programming
Operations Research
Optimization
Production planning
Remanufacturing
stochastic dual dynamic programming
Lot-sizing
Stochastic dual dynamic programming
Production planning
Remanufacturing
Multi-stage stochastic integer programming
ISSN:0020-7543, 1366-588X
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Shrnutí:We seek to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. We consider a multi-stage stochastic integer programming approach and use scenario trees to represent the uncertain information structure. We introduce a new dynamic programming formulation that relies on a partial nested decomposition of the scenario tree. We then propose a new approximate stochastic dual dynamic integer programming algorithm based on this partial decomposition. Our numerical results show that the proposed solution approach is able to provide near-optimal solutions for large-size instances with a reasonable computational effort.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2022.2120924