Dual-resource flexible job shop scheduling considering worker proficiency differences

• The paper is based on the “people-oriented manufacturing” advocated by Industry 5.0. • The auxiliary production time is incorporated into the scheduling model. • The improved optimization algorithm improves the efficiency of scheduling problems. • Cases validate the efficiency of considering individual difference in reducing time. • Results provide reference for enterprises to deploy differentiated workers. Currently, the increasing attention to resource constraints in production systems is driven by the notable distribution of workers across all age groups within the production shop and the discrepancy of their proficiency in operating machines. It takes time and knowledge to improve workers’ proficiency. And as the workforce retires later, the longer working hours of older workers can help them gain more experience. With the improvement of accumulated experience, the time spent on human-related tasks such as machine debugging, checking, cleaning, and other necessary preparation processes before processing will be significantly reduced. Considering these factors is crucial when making production decisions, necessitating the adaptation of job assignments to suit the capabilities of individual workers. Through this approach, the economic indicators of ‘people-oriented manufacturing’ advocated by Industry 5.0 and workshop production can be jointly realized. In this scenario, a mathematical model is constructed with the objective of minimizing the sum of setup time and processing time for the double-resource flexible job shop scheduling problem (DRCFJSP), considering the differences in worker skill levels and varying process setup times based on this. The model takes the skills and proficiency of workers into consideration as well. Given the problem’s characteristics, the migratory bird optimization algorithm (MBO) is applied to address this issue. Finally, a comparative experiment is carried out on a simulation example. The experimental results verify that incorporating the influence of workers with different proficiency on preparation time into the scheduling model can significantly optimize the total production completion time.