A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem

Traditional scheduling less account of human-related dynamic events: worker skill degradation and worker mandatory rest. However, in actual production, workers experience fatigue accumulation that decreases work efficiency, thereby decreasing the precision of jobs, increasing rework rates, and even elevating processing risks. It conflicts with the idea of industrial resilience and human well-being for Industry 5.0. Therefore, a human-centric dynamic distributed flexible job shop scheduling problem (HDDFJSP) has been researched in this paper. Firstly, a multi-objective mathematical model of HDDFJSP is proposed to minimize makespan, worker fatigue, and scheduling deviation. Secondly, a Q-learning improved differential evolution (QLIDE) is designed to solve the HDDFJSP. In the QLIDE, a new four-layer encoding method and two initialization strategies are proposed to generate a high-quality initial population and a novel mutation strategy and two auxiliary mutation methods are designed to enhance the algorithm's exploitation capabilities. Furthermore, three neighborhood search strategies are introduced and combined with mutation operations as part of the Q-learning action phase to improve population convergence and diversity. Thirdly comparative test with four other well-known algorithms has been conducted and the results demonstrate the significant superiority of the QLIDE. Finally, the QLIDE is applied to solve a real case of a labor intensive hydraulic cylinder manufacturing enterprise. The results indicate that considering rescheduling can effectively help production managers to handle dynamic event of humans during the intelligent manufacturing systems.