A reinforcement learning-enhanced multi-objective Co-evolutionary algorithm for distributed group scheduling with preventive maintenance

In the context of the global impetus towards sustainable development and in response to grow market demands, there is a critical need for multi-regional, multi-objective, and flexible production models. Under this background, this article first formulates a mathematical model of a distributed group scheduling problem with preventive maintenance (DFGSP_PM), in which the machine's maintenance level drops below a preset threshold, preventive maintenance is triggered. Second, a reinforcement learning-enhanced multi-objective co-evolutionary algorithm (QCMOEA) is proposed. It incorporates a collaborative evaluation mechanism tailored to the characteristics of the coupled problems to extensively explore the solution space. To retain a balance between convergence and distribution properties, a solution selection strategy based on double-rank and cosine similarity approaches is utilized. Additionally, a Q-learning mechanism is adopted to dynamically select the optimal strategy during enhancing evolution for the group population. Furthermore, a three-stage increasing efficiency and reducing consumption strategy is designed by dynamically changing the machine speed. Finally, by conducting a comparative analysis with four existing metaheuristic algorithms across 405 test cases, the proposed algorithm demonstrates superior optimization capabilities in addressing this complex DFGSP_PM problem.