Integrated scheduling for remanufacturing system considering component commonality using improved multi-objective genetic algorithm

• Published in: Computers & industrial engineering Vol. 182; p. 109419
• Main Authors: Guo, Jun, Zou, Junfeng, Du, Baigang, Wang, Kaipu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Component commonality; Energy consumption; Improved multi-objective genetic algorithm; Remanufacturing system; Scheduling; Energy consumption; Component commonality; Scheduling; Remanufacturing system; Improved multi-objective genetic algorithm
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2023.109419

•Extend the existing remanufacturing scheduling problem with the concepts of component commonality.•Develop an IMOGA with several approaches and heuristic strategies to obtain high-quality solutions for above problem.•Comparative experiments are executed on several test instances to verify the performance of IMOGA. The existing researches on scheduling for remanufacturing system explicitly or implicitly subjects to the component matching requirements while ignoring the repaired component commonality, which is inconsistent with reality. Thus, this paper proposes a novel integrated scheduling method for remanufacturing system with disassembly-reprocessing-reassembly considering component commonality, where components obtained by reprocessing no longer only be used to reassemble their original product, but also be used to reassemble other remanufacturing products. And a mathematic model is formulated to simultaneously minimize the completion time and total energy consumption. Then, an improved multi-objective genetic algorithm (IMOGA) with a new double-layer representation scheme is developed to handle the considered problem. In the IMOGA, a left-shift strategy is developed to utilize workstation idle time and a component-relink strategy is designed to solve the reassembly decision with component commonality. In addition, the crossover and mutation operators based on grouping strategy are designed to enhance algorithm search ability. After, a local search with two heuristic strategies is proposed to further improve the quality of solutions in the elite set. Finally, a series of comparative experiments are carried out and the results show that IMOGA can tackle this scheduling problem effectively.