Driving energy efficiency in scheduling manufacturing: An advanced instance generation and a metaheuristic approach

• Developed an instance generation framework for energy-efficient Job Shop Scheduling. • Proposed a metaheuristic combining genetic algorithms with machine learning. • Metaheuristic approach outperformed traditional solvers on complex scheduling tasks. • Achieved significant improvements in energy efficiency and scheduling metrics. • Benchmark results emphasize scalability and flexibility in highcomplexity scenarios. In pursuit of sustainability, modern manufacturing faces complex scheduling challenges to optimize energy, labor, and machine utilization. Many such problems can be formulated as a Job-Shop Scheduling Problem (JSP), augmented with sustainability requirements. We propose an instance generation framework for JSP incorporating energy-related factors (notably machine speed scaling and associated energy consumption) to enable realistic and diverse benchmarking. The generator uses probabilistic distributions to create complex scenarios mirroring real-world conditions, allowing robust evaluation of scheduling algorithms under varying machine speeds, processing times, and energy demands. In addition, we develop a novel metaheuristic for energy-efficient scheduling: a genetic algorithm (GA) enhanced with machine-learning-based adaptation. The GA employs a task-based representation that treats machine speed as a decision variable, optimizing the trade-off between makespan, energy usage, and tardiness. Experimental results demonstrate that the proposed GA significantly outperforms state-of-the-art exact solvers (e.g., GUROBI, CPLEX, GECODE) on larger problem instances. While exact solvers find optimal solutions for small instances, they struggle to scale; in contrast, our GA consistently achieves near-optimal solutions efficiently for large-scale, energy-aware scheduling problems.