Enhanced NSGA-II for multi-objective energy-saving flexible job shop scheduling

To achieve green targets, manufacturing enterprises need to propose an effective energy-saving strategy for production scheduling. In this paper, a multi-objective energy-saving flexible job shop-scheduling problem (MO_EFJSP) is formulated with three criteria of optimizing the makespan, the total delay time and the total power consumption. To efficiently solve the MO_EFJSP, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is developed. The proposed ENSGA-II has two main innovative aspects: i) the diversity of children population in a local search is achieved by performing different neighborhood search procedures on the sparse solution space so that the accuracy of the current solution is improved; ii) the weighted method is applied to select the desirable compromised solution from the Pareto solution set. By conducting extensive computational experiments based on benchmark instances and real-world case studies, it is verified that the proposed ENSGA-II is applicable for saving power consumption in a flexible job shop system. Consequently, this study makes a significant contribution to the field of green (energy-saving or energy-efficient) production scheduling.