JOB SHOP SCHEDULING PROBLEMS WITH DYNAMIC BREAK TIME UNDER FATIGUE AND RECOVERY EFFECTS

This paper addresses the Job Shop Scheduling Problem (JSSP) with dynamic break times, focusing on the impact of worker fatigue and recovery on processing time. Traditional scheduling models assume deterministic processing times, but this research acknowledges the variability introduced by human factors, specifically fatigue, which can lead to musculoskeletal disorders, increased error frequency, safety issues, and decreased productivity. Rest breaks are identified as an effective strategy to mitigate fatigue, with various manufacturing environments demonstrating the benefits of incorporating rest breaks into scheduling processes. The paper’s main contribution is the development of a Mixed Integer Linear Programming (MILP) model and an Ant Colony Optimization (ACO) algorithm to address the JSSP with dynamic break times. The results indicate a significant reduction in makespan when dynamic break times are incorporated. The average makespan reduction for problem sizes (jobs x machines) was 8.75% for 10 x 5, 13.28% for 15 x 5, 66.41% for 10 x 10, 69.18% for 15 x 10, and 74.27% for larger problems. In conclusion, this research suggests the need for advanced scheduling models that incorporate human factors, support rest breaks in work policies, and help decision-makers balance productivity with worker fatigue management.