Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

In recent years, people have paid more and attention to traditional manufacturing's environmental impact, especially in terms of energy consumption related emissions carbon dioxide. Except for adopting new equipment, production scheduling could play an important role reducing the total a manufacturing plant. Machine tools waste considerable amount because their underutilization. Consequently, saving can be achieved by switching machines standby or off when they lay idle comparatively long period. Herein, we first introduce objectives minimizing non-processing consumption, weighted tardiness earliness, makespan into typical model-the job shop problem, based on machine status framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic MinMax (MM) Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used solve problem. optimization generate Pareto set solutions so that managers flexibly select schedule from these non-dominated schedules priorities. Three sets numerical experiments been carried out extended Taillard benchmark verify this three-objective model's effectiveness algorithm. results show has obtained better most test problem instances than NSGA-II NSGA-III. Furthermore, reduced 46%-69%, which 13-83% consumption.