Fusion Q-Learning Algorithm for Open Shop Scheduling Problem with AGVs
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Published:2024-01-31
Issue:3
Volume:12
Page:452
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Wen Xiaoyu1ORCID, Zhang Haobo1, Li Hao1, Wang Haoqi1, Ming Wuyi12, Zhang Yuyan1ORCID, Zhang Like1
Affiliation:
1. Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China 2. Guangdong Intelligent Engineering Technology Research Center for Manufacturing Equipment & Guangdong HUST Industrial Technology Research Institute, Huazhong University of Science and Technology, Dongguan 523770, China
Abstract
In accordance with the actual production circumstances of enterprises, a scheduling problem model is designed for open-shop environments, considering AGV transport time. A Q-learning-based method is proposed for the resolution of such problems. Based on the characteristics of the problem, a hybrid encoding approach combining process encoding and AGV encoding is applied. Three pairs of actions are constituted to form the action space. Decay factors and a greedy strategy are utilized to perturb the decision-making of the intelligent agent, preventing it from falling into local optima while simultaneously facilitating extensive exploration of the solution space. Finally, the proposed method proved to be effective in solving the open-shop scheduling problem considering AGV transport time through multiple comparative experiments.
Funder
National Natural Science Foundation of China Henan Province University Science and Technology Innovation Talent Support Plan Key Scientific and Technological Research Projects in Henan Province
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
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