We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于多动作深度强化学习的 纺机制造车间调度方法.
- Authors
纪志勇; 袁逸萍; 巴智勇; 樊盼盼; 田芳
- Abstract
The spinning machine manufacturing shop scheduling problem is a flexible Job-Shop scheduling problem with complex process constraints and sequence-dependent setup times. This paper proposed a multi-action deep reinforcement learning algorithm with the optimization objective of minimizing the maximum completion time to ensure the quality of the scheduling solution and improve the on-time order delivery capability of the enterprise. Firstly, this paper modeled the scheduling problem as a multi-Markov decision process. Then, in order to predict the probability distribution of selecting different processes and machines, this paper designed two encoders for the two sub-problems of workpiece selection and machine selection of spinning machine manufacturing plant scheduling, for defining the process selection policy and machine selection policy, respectively. In the process selection encoder, it used a graphical neural network to encode the disjunctive graph to reduce the impact of problem size on the quality of the solution. Based on this, the paper designed a reinforcement learning training algorithm with multiple action spaces for the two substrategies. Finally, it validated the proposed method on a real production case of a spinning machine manufacturing company. The results show that the method exhibits good performance on problems of different scales, is able to obtain higher quality scheduling solutions compared with other comparative algorithms, and the model has better generalization ability and stability.
- Subjects
DEEP reinforcement learning; REINFORCEMENT learning; SETUP time; MACHINE shops; SCHEDULING
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2023, Vol 40, Issue 11, p3247
- ISSN
1001-3695
- Publication type
Article
- DOI
10.19734/j.issn.1001-3695.2023.03.0134