NSGA-II algorithm and application for multi-objective flexible workshop scheduling
Yahui Wang, Shi Ling, Zhang Cai, Liuqiang Fu, Xiangjie Jin
Abstract
Based on the study of multi-objective flexible workshop scheduling problem and the learning of traditional genetic algorithm, a non-dominated sorting genetic algorithm is proposed to solve and optimize the scheduling model with the objective functions of processing cycle, advance/delay penalty and processing cost. In the process of optimization, non-dominated fast ranking operator and competition operator are used to select the descendant operator, which improves the computational efficiency and optimization ability of the algorithm. Non-repetitive non-dominant solutions and frontier sets are found in the iteration operation to retain the optimal results. Finally, taking a manufacturing workshop as an example, the practicability of the proposed algorithm is verified by the simulation operation of the workshop scheduling information and the comparison with other algorithms. The results show that the algorithm can obtain the optimal solution more quickly than the unimproved algorithm. The improved algorithm is faster and more effective in searching, and has certain feasibility in solving the job shop scheduling problem, which is more suitable for industrial processing and production.