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 extension of the recently published 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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References
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Quezada, F., Gicquel, C., Kedad-Sidhoum, S.: Stochastic dual dynamic integer programming for a multi-echelon lot-sizing problem with remanufacturing and lost sales. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1254–1259. IEEE (2019)
Quezada, F., Gicquel, C., Kedad-Sidhoum, S., Vu, D.Q.: A multi-stage stochastic integer programming approach for a multi-echelon lot-sizing problem with returns and lost sales. Comput. Oper. Res. 116, 104865 (2020)
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Acknowledgments
This work was partially funded by the National Agency for Research and Development (ANID) / Scholarship Program / DOCTORADO BECAS CHILE/2018 - 72190160
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Quezada, F., Gicquel, C., Kedad-Sidhoum, S. (2021). A Partial Nested Decomposition Approach for Remanufacturing Planning Under Uncertainty. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-030-85902-2_71
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DOI: https://doi.org/10.1007/978-3-030-85902-2_71
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