Research on flexible job-shop scheduling problem considering equipment preventive maintenance

In response to the Flexible Job Shop Scheduling Problem (FJSP) with consideration of equipment preventive maintenance, this paper proposes a discrete Jaya algorithm. The algorithm is initialized by randomly generating machine selection codes and operation processing order codes. Subsequently, it employs POX (Partially-Mapped Crossover) and MPX (Multi-Parent Crossover) methods for crossover and updating. Then, Pareto sorting is used to help identify non-dominated solutions that achieve a balance between multiple objective functions, generating a Pareto front that meets the output requirements. Following this, experiments are conducted, and the results of the proposed discrete Jaya algorithm are compared with those of the Genetic Algorithm (GA), Simulated Annealing Algorithm (SA), and Particle Swarm Optimization (PSO) to verify its performance in solving the makespan (Cmax). The results demonstrate that the discrete Jaya algorithm designed in this paper has achieved superior solutions in terms of Cmax, reflecting the algorithm's effectiveness and global optimization capabilities. Additionally, corresponding high-quality Gantt charts are provided.