Joint optimization of flexible job shop scheduling and preventive maintenance under high-frequency production switching

• Published in: International journal of production economics Vol. 269; p. 109163
• Main Authors: Wang, Yu, Xia, Tangbin, Xu, Yuhui, Ding, Yutong, Zheng, Meimei, Pan, Ershun, Xi, Lifeng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2024
• Subjects: Flexible job shop scheduling; High-frequency production switching; Hybrid meta-heuristics algorithm; Preventive maintenance; Variable operating conditions; Hybrid meta-heuristics algorithm; Variable operating conditions; High-frequency production switching; Preventive maintenance; Flexible job shop scheduling
• ISSN: 0925-5273; 1873-7579
• DOI: 10.1016/j.ijpe.2024.109163

To secure a larger market share in the dynamically evolving personalized market demand, enterprises have to adopt a more flexible production mode. In this context, the implementation of parallel production with multiple product types necessitates frequent switching between different products, imposing elevated requirements on machine fault-free operation and stable production. There is a compelling motivation to investigate the joint optimization of the flexible job shop scheduling problem and preventive maintenance (FJSSP-PM). However, existing research has primarily focused on utilizing predetermined maintenance as inputs for optimizing production scheduling, while overlooking the uncertain maintenance requirements of machines during the production process. Therefore, this paper proposes a two-stage joint optimization model to simultaneously address three subproblems: machine assignment, operation sequencing, and maintenance arrangement. Specifically, the first stage involves constructing a mathematical model for the FJSSP to minimize penalties for tardiness and workload balancing. Subsequently, a comprehensive failure rate model is developed to determine maintenance requirements for machines with production-dependent failure behavior, particularly under high-frequency production switching scenarios. Furthermore, a feedback-updated strategy is proposed to achieve synchronous optimization of production and maintenance activities in an economically and operationally efficient manner. Given the complexity of the problem, a hybrid meta-heuristic algorithm is designed to effectively handle large-scale cases. The effectiveness of the proposed model in terms of cost-savings has been verified compared with existing relevant methods. •Joint optimization of job scheduling and preventive maintenance is investigated.•The machine failure process under high-frequency production switching is molded.•The dynamic balance between production and maintenance has been achieved.•The DPSO-VNS algorithm balances between solution quality and computational effort.•Results help enterprises meet machine failure-free operation and flexible production.