期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 61, 期 17, 页码 5992-6012出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2022.2120924
关键词
Production planning; lot-sizing; remanufacturing; multi-stage stochastic integer programming; stochastic dual dynamic programming
This paper aims to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. A multi-stage stochastic integer programming approach is considered, and scenario trees are used to represent the uncertain information structure. A new dynamic programming formulation is introduced based on a partial nested decomposition of the scenario tree. A new approximate stochastic dual dynamic integer programming algorithm is proposed based on this partial decomposition. The numerical results show that the proposed solution approach can provide near-optimal solutions for large-size instances with a reasonable computational effort.
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.
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