Research on Low-carbon Application of Improved Non-dominated Sorting Genetic Algorithm

Abstract

An improved genetic algorithm with elitist strategy (INSGA-II) is proposed to solve the multi-objective problem for low-carbon job shop scheduling. In this paper, a heuristic algorithm is introduced in the initial population stage, and the weight aggregation method is used to constrain the total completion time and carbon emissions. The elite strategy is improved by using simulated annealing method to replace the son with the parent to improve the quality of the replacement population. The improved non dominated sorting genetic algorithm with elitist strategy can obtain Pareto optimal solution set faster and obtain higher population diversity in the initial stage. The experimental results show that the convergence speed and diversity of the algorithm have been improved to a certain extent. On the basis of considering the machine load, the maximum completion time is minimized. When two machines with different carbon emissions in the same processing time are processed, the machine with low carbon emission will be selected optimally.