Optimization of flow shop scheduling based on genetic algorithm with reinforcement learning. Issue 1 (1st April 2022)

• Record Type: Journal Article
• Authors: Gao, Xiaoyu; Yang, Shipin; Li, Lijuan
• Is Part Of: Journal of physics. Volume 2258:Issue 1(2022)
• Publication Date: 2022-04-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2258/1/012019
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Genetic algorithm, as a kind of evolutionary algorithm, has the characteristics of easy operation and global search, but its stochasticity is relatively strong and highly susceptible to parameters. When facing a large-scale scheduling problem, a strategy is needed to improve the parameter adaptability to make its solution more effective. Reinforcement learning, as an optimization method, has a strong autonomous learning capability. Therefore, this paper proposes a genetic algorithm based on reinforcement learning, which uses Q-learning to self-learning the crossover probability and improve the generalization ability of genetic algorithm, so as to achieve the solution of large-scale replacement flow shop scheduling problem.