A Large-Scale Multi-Objective Flexible Job-Shop Scheduling Algorithm Based on Differential Evolution and Angle-Based Weight Vector Assignment

Traditional large-scale multi-objective evolutionary algorithms perform poorly on the large-scale Flexible Job-Shop Scheduling Problems (FJSP), which are characterized by discrete decision variables, large-scale decision space, and sparse non-dominated solutions. In order to improve the sparsity of non-dominated solutions and enhance the diversity as well as convergence of the population, a Large-Scale Multi-Objective Flexible Job-Shop Scheduling Algorithm Based on Differential Evolution and Angle-Based Weight Vector Assignment (MOEA/D-ADE) is proposed. The optimization objectives of this scheduling problem are minimizing the maximum completion time, the total machine energy consumption, and the mean squared error of the machine load. Firstly, an initial population is formed by screening out high-quality individuals from the decision space based on their individual process equilibrium coefficients using a knowledge-based solution filtering approach. Secondly, to obtain non-dominated solutions, the proposed algorithm employs Differential Evolution (DE) to enhance its global search capabilities. Then it calculates the angle between the offspring and the corresponding weight vector of the parent to achieve the reallocation of the offspring. Finally, the algorithm realizes adaptive adjustment of the mutation probability based on the diversity of individuals in the neighborhood. The proposed method is applied to 10 large-scale FJSP and compared with 3 multi-objective evolutionary algorithms, and the effectiveness of the algorithm is finally verified.