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 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.

Automated summary

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.