Multi-Objective fuzzy flexible jobshop of dynamic scheduling optimization based on hybrid-nondominanted sorting algorithm

Multi-objective flexible job-shop scheduling problem is of great significance to improve the efficiency of the production system. However, most studies lack dynamic scheduling experiments. In this paper, a hybrid-nondominated sorting algorithm based on NSGA-II (Non-dominated Sorting Genetic Algorithm) is proposed to optimize problem considering the influence of transportation time window. Co-evolution PSO (Particles Swarm Optimization) based on Cauchy mutation and weight mapping crossover are used to generate new solutions to improve the ability of local search and global detection. The proposed method has better solution with convergence and distributivity. The chromosome is decoded by a dynamic fuzzy greedy interpolation method based on exact time. The algorithm is applied to famous benchmarks to verify the effectiveness of the proposed method in dynamic scene.