A Partial Nested Decomposition Approach for Remanufacturing Planning Under Uncertainty

Abstract

We seek to optimize the production planning of a threeechelon 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.

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

“…Quezada, Gicquel, and Kedad-Sidhoum (2022) recently investigated how the SDDiP algorithm may be extended to efficiently solve large-size instances of the SULS problem. Preliminary attempts at solving the remanufacturing planning problem studied here with the SDDiP algorithm can be found in Quezada, Gicquel, and Kedad-Sidhoum (2019a) and Quezada, Gicquel, and Kedad-Sidhoum (2021b).…”

“…Remanufacturing is defined as a set of processes trans-forming end-of-life products (also called used products or returns) into like-new fin-ished products, once again usable by customers, mainly by rehabilitating damaged *Corresponding author. Email: franco.quezada@usach.cl *Preliminary results were published in Quezada, Gicquel, and Kedad-Sidhoum (2021b). components (Lund 1984).…”