A Systematic Review of Multi-Objective Production Scheduling Integrating Maintenance and Energy Optimization

Abstract

Production scheduling has become one of the most critical functions in modern manufacturing systems due to increasing industrial complexity, sustainability requirements, and the rapid adoption of Industry 4.0 technologies. Traditional scheduling approaches mainly focused on productivity and makespan minimization; however, modern manufacturing industries require integrated optimization of production efficiency, maintenance planning, and energy utilization. This review article presents a systematic review of multi-objective production scheduling approaches integrating maintenance and energy optimization in intelligent manufacturing environments. The study critically analyzes various scheduling models including flow shop, job shop, and flexible manufacturing systems along with optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), NSGA-II, Simulated Annealing (SA), Ant Colony Optimization (ACO), and artificial intelligence-based methods. The review also investigates maintenance-aware scheduling models, predictive maintenance integration, energy-efficient scheduling strategies, and Industry 4.0-enabled smart manufacturing systems. Furthermore, recent research trends including green manufacturing, cloud-based scheduling, digital twin applications, and autonomous production systems are discussed. The analysis identifies major research gaps such as lack of real-time industrial implementation, computational complexity, insufficient sustainability metrics, and limited generalized optimization frameworks. The review concludes that hybrid AI-driven optimization approaches integrated with IoT, cyber-physical systems, and digital twin technologies offer significant potential for developing intelligent, adaptive, and sustainable production scheduling systems for future smart manufacturing and Industry 5.0 environments.

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