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

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  • Franco Quezada
  • Céline Gicquel
  • Safia Kedad-Sidhoum

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.

Suggested Citation

  • Franco Quezada & Céline Gicquel & Safia Kedad-Sidhoum, 2023. "A stochastic dual dynamic integer programming based approach for remanufacturing planning under uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 61(17), pages 5992-6012, September.
    • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:17:p:5992-6012
    DOI: 10.1080/00207543.2022.2120924
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    Cited by:

    1. Feng Xue & Qiumin Li, 2024. "Forecast Horizon of Dynamic Lot Sizing Problem with Perishable Inventory and Multiple Chain Stores: Shipping and Stockout Cost," Mathematics, MDPI, vol. 12(13), pages 1-17, July.

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