Simultaneous optimization of flexible job shop scheduling and preventive maintenance using a multi-objective JAYA algorithm

With the increasing complexity, integration, and flexibility of modern manufacturing systems, effective coordination between production scheduling and machine health management has become crucial for ensuring system performance and operational reliability. However, in both industrial applications and academic research, these decisions are often treated separately, leading to resource conflicts, scheduling delays, and suboptimal machine utilization. To address this challenge, this study proposes a simultaneous optimization framework for flexible job shop scheduling and preventive maintenance (FJSP&PM). A multi-objective model is developed to jointly minimize job tardiness, maintenance costs, and machine workload imbalance, while explicitly capturing the interdependence between task execution and machine health evolution. To tackle the NP-hard nature of the FJSP&PM, the multi-objective JAYA algorithm (MOJAYA) is proposed with a diversity-driven initialization strategy and a simultaneous decision-making decoding mechanism. Extensive experiments demonstrate that MOJAYA significantly enhances solution quality and Pareto front distribution. Moreover, comparative results with representative hierarchical optimization strategies confirm the superiority of the proposed synchronized decision-making mechanism in reducing overall operational costs and improving scheduling stability. The findings also highlight the substantial impact of objective weighting on decision outcomes, underlining the importance of carefully tuned multi-objective configurations.