A Learning-Assisted Bi-Population Evolutionary Algorithm for Distributed Flexible Job-Shop Scheduling With Maintenance Decisions

In the post-pandemic era, more manufacturers have expedited the shift from centralized to distributed manufacturing to enhance supply chain resilience. Along with this, the distributed shop floor scheduling problem has attracted much attention from academia, one of which is the distributed flexible job-shop scheduling problem (DFJSP). Nonetheless, the majority of research on DFJSPs overlooks crucial real-world necessities, such as multi-objective decision making and preventive maintenance (PM). Thus, this article suggests a multi-objective DFJSP with PM (DFJSP/PM) as a new variant of the DFJSP. The aim is to achieve a trade-off between production and maintenance to minimize the makespan, maintenance cost, and energy consumption. To this end, we establish a mathematical model and then customize a learning-assisted bi-population evolutionary algorithm (LBPEA) to solve it. In LBPEA, a novel encoding mechanism is proposed to initialize the population randomly. Then, a neighborhood search heuristic is designed to enhance the population's quality. To balance the convergence and diversity of the population, a bi-population evolution idea is introduced during the environmental selection. Besides, a two-stage local search (LS) process is adaptively triggered to balance the allocation of computational resources between exploration and exploitation. At the first stage, a reinforcement learning mechanism is employed to intelligently select LS operators to adjust either the operations' sequence or assignment to different factories and machines, while the second stage is to adjust the number and placement of maintenance decisions. Experimental results show that LBPEA has excellent performance in terms of convergence and diversity when solving the proposed multi-objective DFJSP/PM.