Multi-Objective Optimization of Production Scheduling Using NSGA-II Considering Maintenance and Energy Constraints

Production scheduling plays an important role in improving productivity, machine utilization, and operational efficiency in modern manufacturing industries. This research presents a multi-objective optimization framework for production scheduling using the NSGA-II algorithm considering maintenance and energy constraints. The proposed model integrates production scheduling, preventive maintenance planning, and energy-aware optimization to minimize makespan, reduce energy consumption, and improve machine reliability simultaneously. Different scheduling parameters such as processing time, machine allocation, maintenance intervals, and energy utilization were analyzed using simulation-based experimentation. The NSGA-II optimization approach generated Pareto-optimal solutions that significantly improved production efficiency and reduced maintenance downtime and peak energy demand. Comparative analysis with traditional scheduling approaches demonstrated superior performance of the proposed model in terms of machine utilization, operational cost reduction, and sustainability improvement. Statistical analysis including ANOVA and regression analysis validated the effectiveness of the optimization framework. The proposed intelligent scheduling system provides strong industrial applicability for smart manufacturing and Industry 4.0 environments. The study also highlights future opportunities for integrating artificial intelligence, IoT, and digital twin technologies in intelligent production scheduling systems.