Flexible Job Shop Scheduling Method Based on Collaborative Agent Reinforcement Learning Algorithm

Authors

Jian Li, School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China; Henan Collaborative Innovation Center for Advanced Manufacturing of Mechanical Equipment, Luoyang 471000, China
Huankun Li, School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China
Pengbo He, School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China
Huabei Wang, School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China
Liping Xu, School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China; Henan Collaborative Innovation Center for Advanced Manufacturing of Mechanical Equipment, Luoyang 471000, China
Kui He, School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China; Henan Collaborative Innovation Center for Advanced Manufacturing of Mechanical Equipment, Luoyang 471000, China

Abstract

Abstract: To enhance the efficiency of flexible job shop scheduling, this paper develops a Markov decision process with specific constraints tailored to the scheduling problem. A cooperative agent reinforcement learning method is proposed to solve the problem of concurrent selection of workpieces and machines. During the construction of the Markov decision process, a disjunctive graph is introduced to represent the state characteristics. Two agents are introduced to select the workpieces and machines. The reward parameters governing the entire scheduling process are established by predicting variations in the minimum-maximum completion time across different time points. A GIN(graph isomorphic network) graph neural network is embedded in the solving procedure to extract the relevant state information. Encoder and decoder components are respectively set for the workpiece and machine agent to output two action strategies. The PPO(proximal policy optimization) algorithm and D3QN algorithm are used to train the decision network parameters for these agents. Algorithm hyperparameters, determined through the orthogonal experiment method, are compared with standard benchmarks and those in existing literature. The results demonstrate the significant superiority of the proposed method in solving the flexible job shop scheduling problem, further substantiating the feasibility and effectiveness of the method.

Recommended Citation

Li, Jian; Li, Huankun; He, Pengbo; Wang, Huabei; Xu, Liping; and He, Kui (2024) "Flexible Job Shop Scheduling Method Based on Collaborative Agent Reinforcement Learning Algorithm," Journal of System Simulation: Vol. 36: Iss. 11, Article 17.
DOI: 10.16182/j.issn1004731x.joss.23-0978
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss11/17

First Page

2699

Last Page

2711

CLC

TP278

Recommended Citation

Li Jian, Li Huankun, He Pengbo, et al. Flexible Job Shop Scheduling Method Based on Collaborative Agent Reinforcement Learning Algorithm[J]. Journal of System Simulation, 2024, 36(11): 2699-2711.

DOI

10.16182/j.issn1004731x.joss.23-0978