No abstract

With the increasingly serious problem of environmental pollution and resource scarcity, remanufacturing has become one of the popular research fields to solve these issues. However, the practical information of end-of-life products is different (e.g. type and degree of damage) because of their various operation conditions, which complicates the reprocessing routes. Therefore, a new remanufacturing system scheduling model is proposed in this study that considers not only the coordination of remanufacturing subsystems but also job-shop-type reprocessing shops related to the diversified reprocessing routes. A hybrid meta-heuristic algorithm combining differential evolution algorithm and biogeography-based optimization algorithm through a new representation scheme is presented to address the model efficiently. Furthermore, the basic algorithms are improved by integrating the self-adaptive parameters, efficient migration and mutation operators, local search strategy, and restart strategy. Simulation experiments are performed to demonstrate the effectiveness and practicality of the proposed method compared with four baseline algorithms.