Multi-objective Green Flexible Job Shop Scheduling Based on Improved Dual Population Genetic Algorithm

In order to enable the scheduling plan to achieve energy reduction and emission reduction in manufacturing production without reducing processing efficiency, a mathematical model of the multi-objective green flexible job shop scheduling problem with makespan, total energy consumption of machines and total carbon emissions as the optimization objectives is established. Aiming at the problem that the traditional dual population genetic algorithm has a small difference in the initial resolution set when solving the above model, resulting in high repeatability of the scheduling scheme, an improved dual population genetic algorithm is proposed to solve it. Firstly, a two-stage coding is adopted to simplify the algorithm process, and a multi-objective improved global-local-random search initialization method is proposed to increase the diversity of the population solution set. Then, a fitness population segmentation method is proposed to divide the population; Subsequently, evolutionary operations are carried out on the two populations respectively, and population selection is conducted after merging to improve the quality of the next generation of populations; Finally, the normalization method is adopted for comprehensive evaluation to select the optimal scheduling scheme. The improved dual population genetic algorithm is verified and compared through the improved Brandimarte dataset and fact example data. The results show that the improved dual population genetic algorithm has great advantages in solving the multi-objective green flexible job shop scheduling problem.