Simulative dynamic preventive machine maintenance policy: time efficient stochastic job shops
通过仿真实验研究随机作业车间中年龄和固定间隔两种预防性维护策略，结合路径柔性和交货期紧度等指标，优化多目标调度框架，降低能耗和浪费。
Modern manufacturing systems aim for maximum production output with minimal maintenance time. Increased maintenance time in job shops leads to reduced production efficiency and energy waste. Dynamic job shops, characterized by stochastic processing times, sequence-dependent setup times (Sd-St), and two distinct preventive maintenance (PM) policies, age-based and fixed interval pose significant scheduling challenges. Key performance measures, such as routing flexibility and due-date tightness, enhance machine efficiency, and maintain production quality. There is a growing demand from industry leaders for solutions that minimize energy consumption and production waste while optimizing job-shop maintenance timing. Simulation offers a cost-effective approach that does not require pilot field programmes, providing faster and more accurate results. This article develops simulation experiment that incorporates key performance measures to demonstrate an optimal setup for a multi-objective optimization framework applied to flexible job shop scheduling problems (FJSSPs). Our results indicate that integrating PM policies and routing flexibility within the scheduling framework significantly improves key performance measures. We identify an efficient maintenance scheduling policy for a ten-machine system with optimal setup times, focusing on metrics, such as mean tardiness, maximum tardiness, and work-in-progress (WIP) jobs. The interactive impact between these factors underscores the potential for more efficient and robust scheduling strategies, supporting modern manufacturing systems. Efficient production not only enhances operational sustainability but also reduces environmental impacts by potentially minimizing production delays.