A stochastic dual dynamic integer programming based approach for remanufacturing planning under uncertainty
针对三级再制造系统在不确定数据下的生产计划优化问题，提出一种基于情景树部分嵌套分解的随机对偶动态整数规划算法，数值实验表明该方法能以合理计算量获得大规模问题的近优解。
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.