Flexible job shop scheduling problem based on hybrid multi-objective genetic algorithm

SONG Chang-xing, RUAN Jing-kui, WANG Chen
(School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China)

Abstract: Aiming at the problem of multi-objective flexible job shop scheduling, a mathematical model of scheduling with maximum completion time, total machine load, and bottleneck machine load as targets was established, and a solution method based on hybrid multi-objective non-dominated sorting genetic algorithm II (HMO-NSGA-II) was proposed. Firstly, the initial population with uniform distribution was obtained by combining global selection with fast selection. Secondly, the cross-mutation operator was improved adaptively to improve the searching ability of the population. Then, in view of the limitations of the elite strategy in maintaining population diversity, an elite retention mechanism was designed, and the improved harmony search algorithm was introduced to improve the quality of individuals in the elite library. Finally, the benchmark case Kacem test set, BRdata data set and actual production cases were used for testing. The results indicate that HMO-NSGA-II is used to solve multi-objective flexible job shop scheduling problems with high solution accuracy and fast convergence. The method can provide decision-makers with feasible and effective scheduling schemes in actual production, and has good practical value.
Key words: adaptive operator; non-dominated sorting genetic algorithm II(NSGA-II); hybrid optimization algorithm; flexible job shop scheduling