Multi-Stage Production Planning with Sequence-Dependent Setups and Setup Carry Over In Closed-Loop Supply Chain

This paper studies multi-stage, multi-product, multi-period production planning problem with sequence dependent setups in closed-loop supply chain. Manufacturing and remanufacturing processes of each product are regarded consequently, and both of them could be performed if machine is ready for processing corresponding product. To formulate the problem, a mixed-integer programming (MIP) model is presented and four heuristic algorithms using rolling horizon and a genetic algorithm are developed to solve the model. First two heuristic algorithms are developed based on the original model, but to solve the large instances the other two heuristics and the genetic algorithm are based on the simplified model, which is obtained by elimination of non-permutation sequences of original model solution space. To calibrate the parameters of the proposed genetic algorithm, Taguchi method is applied. The numerical results indicate the efficiency of the proposed meta-heuristic algorithm against MIP-based heuristic algorithms.