Improved NSGA-II for Dual-resource Flexible Job Shop Scheduling Considering Worker Load

Authors

• Guohui Zhang, School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
• Yuan Ren, School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
• Changjun Wu, College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
• Xiaofei Kou, School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China

Abstract

For the dual-resource-constrained flexible job shop scheduling problem considering worker load, an evolutionary algorithm integrating reinforcement learning was proposed. A three-stage encoding conforming to the problem characteristics was designed, and three initialization methods were combined to improve the population quality; a left-insertion decoding method based on worker load was designed to ensure that the completion time of the operation is less than the maximum processable time of the worker on the current day; two neighborhood structures based on the critical path were constructed to enhance the local exploration ability of the population; reinforcement learning was integrated to enable the mutation rate and crossover rate of the algorithm to change adaptively according to the population quality. Simulation experiments verified the superiority of the algorithm.

Recommended Citation

Zhang, Guohui; Ren, Yuan; Wu, Changjun; and Kou, Xiaofei (2026) "Improved NSGA-II for Dual-resource Flexible Job Shop Scheduling Considering Worker Load," Journal of System Simulation: Vol. 38: Iss. 6, Article 10.
DOI: 10.16182/j.issn1004731x.joss.25-0607
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss6/10

First Page

1598

Last Page

1612

CLC

TP18

Recommended Citation

Zhang Guohui, Ren Yuan, Wu Changjun, et al. Improved NSGA-II for Dual-resource Flexible Job Shop Scheduling Considering Worker Load[J]. Journal of System Simulation, 2026, 38(6): 1598-1612.

Corresponding Author

Zhang Guohui

DOI

10.16182/j.issn1004731x.joss.25-0607