Towards Sustainable Factories: A Systematic Review of Energy-Conscious Job-Shop Scheduling Models and Algorithms

Job-shop scheduling plays a pivotal role in sustainable manufacturing because scheduling decisions strongly influence energy consumption, machine utilization, and environmental performance. Traditional job-shop scheduling research has mainly optimized makespan, throughput, and tardiness; however, growing sustainability pressures and Industry 4.0 technologies have shifted attention toward energy-conscious scheduling. This review systematically analyzes 2083 publications retrieved from SCOPUS, Web of Science, and IEEE Xplore to map the evolution of energy-efficient job-shop scheduling (EEJSS) models, methods, and industrial applications. Compared with prior surveys, this work contributes a sector-specific analysis, an updated classification of energy-aware models, and the first structured mapping of EEJSS research to sustainability and Industry 4.0 capabilities. Further, challenges such as computational complexity, absence of standardized energy benchmarks, limited industrial deployment, and narrow sustainability metrics are addressed. Overall, this review consolidates the state of EEJSS and positions energy-aware scheduling as a foundational enabler of low-carbon, resilient, and intelligent manufacturing systems.