Disruption management for solving FJSP with improved genetic algorithm

Abstract

Abstract In order to solve the Multi-objective flexible job shop scheduling, an improved cloud adaptive genetic annealing algorithm is proposed. Firstly, the cross-variation, improved fitness calculations, and selection operations are built. Moreover, a novel multi-objective optimization model for flexible job-shop scheduling (FJSP) is established. At the same time, the idea of gradual optimization for the target is introduced to obtain the optimal scheme of FJSP. According to the similarity test of Hamming ones, the cross operation is selected. It can improve the efficiency and convergence of the proposed algorithm. The experiment result verifies the effectiveness of the proposed improved cloud adaptive genetic annealing algorithm (CAGA), and which is compared with other algorithms experiments the existing classical algorithms.