An efficient two-stage optimization algorithm for a flexible job shop scheduling problem with worker shift arrangement

Due to the involvement of workers, scheduling production jobs necessitates consideration of worker shifts in most production activities. In this study, we address a flexible job shop scheduling problem with worker shift arrangement, considering constraints such as job priority, limited resources, and resource unavailability. To minimize the overdue days of low-priority jobs and ensure the timely delivery of high-priority jobs, we establish a mixed-integer programming model to allocate production resources, process sequencing, and schedule worker shifts. An improved differential evolution algorithm is proposed and designed such that overdue days and worker overtime of all jobs are calculated. Furthermore, we develop a two-stage intelligent optimization algorithm. First, we design a two-segment chromosome encoding and decoding method. Then, we propose generation strategies that follow the urgency of the priority rule to generate high-quality initial chromosomes. In adaptive worker shift adjustment, we prioritize high-priority jobs to align with delivery times. We conducted experiments to validate our model and algorithm by comparing them against four well-known intelligent optimization algorithms. Our improved algorithm proves to be highly beneficial in job and worker scheduling as it effectively minimizes overdue days and arranges worker overtime.