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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Abstract 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.
AbstractList 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.
Author Gicquel, Céline
Kedad-Sidhoum, Safia
Quezada, Franco
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  surname: Quezada
  fullname: Quezada, Franco
  email: franco.quezada@usach.cl
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  givenname: Céline
  surname: Gicquel
  fullname: Gicquel, Céline
  organization: LISN, Université Paris Saclay
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  givenname: Safia
  surname: Kedad-Sidhoum
  fullname: Kedad-Sidhoum, Safia
  organization: CEDRIC, CNAM
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Issue 17
Keywords Lot-sizing
Stochastic dual dynamic programming
Production planning
Remanufacturing
Multi-stage stochastic integer programming
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SubjectTerms 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
Title A stochastic dual dynamic integer programming based approach for remanufacturing planning under uncertainty
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