A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem
By Ronghua Chen, Bo Yang, Shi Li, Shilong Wang. Computers & industrial engineering, 2020
481 citations recorded by the source corpus.
Abstract As an important branch of production scheduling, flexible job-shop scheduling problem (FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have been proposed to solve FJSP, but their key parameters cannot be dynamically adjusted effectively during the calculation process, which causes the solution efficiency and quality not being able to meet the production requirements. Therefore, a self-learning genetic algorithm (SLGA) is proposed in this paper, in which genetic algorithm (GA) is adopted as the basic optimization method and its key parameters are intelligently adjusted based on reinforcement learning (RL). Firstly, the self-learning model is analyzed and constructed in SLGA, SARSA algorithm and Q-Learning algorithm are applied as the learning methods at initial and later stages of optimization, respectively, and the conversion condition is designed. Secondly, the state determination method and reward method are designed for RL in GA environment. Finally, the learning effect and performance of SLGA in solving FJSP are compared with other algorithms using two groups of benchmark data instances with different scales. Experiment results show that the proposed SLGA significantly outperforms its competitors in solving FJSP.
Subfields: Artificial Intelligence, Control and Systems Engineering, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/8f42836788140777348cec7a908f1ddf2e9dceac
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Citations in the corpus
- Genetic algorithms: theory, genetic operators, solutions, and applications (2023, unknown): “GAs have been used to solve optimization problems in several applications such as image processing [54–57], scheduling [54, 58, 59], clustering [60–62], software engineering [63–66], natural language processing [67, 68], and recommender system (RS) [52, 69, 70].”
- Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning (2023, methodology): “are computed with respect to the best solutions in [43]), which also includes results from the recent DRL [34] and GA [26], [27] baselines.”