Remanufacturing system scheduling of batch products with the consideration of dynamic changes in machine efficiency using an improved artificial bee colony algorithm

Remanufacturing has the ability to bring the quality and condition of the recycled product back to the level of or even better than the new product. As remanufacturing continues to receive more and more attention from industry and academia, the scheduling of remanufacturing systems (RMS) has been extensively studied. However, few studies have not only focused on the dynamic changes (deterioration and learning) in machine efficiency during RMS scheduling but also considered that the three subsystems (disassembly, reprocessing and reassembly) of RMS collaborate with each other during scheduling. Therefore, this study proposes a new RMS scheduling model of batch recycled products with the consideration of dynamic changes in machine efficiency for handling the batch recycled products. It considers not only the collaboration among the three subsystems comprehensively, but also the dynamic changes in machine efficiency, i.e., deterioration effects and learning effects, during the scheduling. To solve this model, an improved artificial bee colony algorithm with a new representation scheme and a new population generating method is proposed. The effectiveness and practicality of this algorithm are examined through simulation experiments and comparison with other baseline algorithms.