Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

Remanufacturing end-of-life (EOL) products is an important approach to yield great economic and environmental benefits. A remanufacturing process usually contains three shops, i.e., disassembly, processing and assembly shops. EOL products are dissembled into multiple components in a disassembly shop. Reusable components are reprocessed in a processing shop, and reassembled into their corresponding products in an assembly shop. To realize an overall optimization, we have to integrate them together when making decisions. In practice, a decision-maker usually has to optimize multiple criteria such as cost-related and service-oriented objectives. Additionally, we cannot accurately acquire the detail of EOL products due to their various usage processes. Therefore, multi-objective and uncertainty need to be considered simultaneously in an integrated disassembly-reprocessing-reassembly scheduling process. This work investigates a stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling problem to achieve the expected makespan and total tardiness minimization. To handle this problem, this work develops a multi-objective discrete fruit fly optimization algorithm incorporating a stochastic simulation approach. Its search techniques are designed according to this problem's features from five aspects, i.e., solution representation, heuristic decoding rules, smell-searching, vision-searching, and genetic-searching. Simulation experiments are conducted by adopting twenty-five instances to verify the performance of the proposed approach. Nondominated sorting genetic algorithm II, biobjective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons. By analyzing the results with three performance metrics, we can find that the proposed approach performs better on all the twenty-five instances than its peers. Specifically, it outperforms them by 6.45%-9.82%, 6.91%-17.64% and 1.19%-2.76% in terms of performance, respectively. The results confirm that the proposed approach can effectively and efficiently tackle the investigated problem. (C) 2020 Published by Elsevier Ltd.