Distributed Hybrid Flow Shop Scheduling with Considering Preventive Maintenance

This research is centered around the exploration of a distributed hybrid flow shop scheduling problem (DHFSP), which incorporates flexible periodic preventive maintenance (PM) for equipment. The maintenance strategy adopts a mixed mode for single and dual workers, while also considering equipment speed variation. A mathematical optimization model is constructed with the objective of minimizing the maximum completion time and the maintenance cost of workers. A four-layer encoding structure is designed, containing five essential factors: factory, job, machine, speed, and worker. In this study, a multi-objective honey badger algorithm (MOHBA) is designed. Considering capabilities of global optimization, MOHBA also exhibits good robustness for solving optimization problems. Comparative algorithms of the fast non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective seagull optimizer (MOSOA) are designed. From the Pareto front distribution and the quality of solutions, the results confirm MOHBA’s effectiveness. It can effectively solve the distributed hybrid flow shop scheduling and preventive maintenance with variable speeds.