چکیده: (69 مشاهده)
Machine scheduling critically influences production efficiency. This study focuses on the Non-Identical Parallel Machine Scheduling Problem (NIPMSP) and explicitly incorporates human factors, namely worker fatigue, into an exact optimization framework. A mixed-integer nonlinear programming (MINLP) model where fatigue accumulation and recovery dynamically affect processing times. The model is verified and validated using secondary datasets and manual calculations, and solved with Gurobi across multiple configurations (2–3 machines; 6–20 jobs). Results show that the model consistently produces feasible solutions; higher fatigue rates increase makespan, whereas higher recovery rates reduce it. Sensitivity analyses on the fatigue rate, recovery rate, and percentage of jobs influenced by fatigue further confirm these trends. Incorporating worker fatigue enables more realistic makespan minimization and provides actionable managerial insights to balance throughput and workforce well-being.
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