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A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem
یک الگوریتم ژنتیک خودآموز مبتنی بر یادگیری تقویتی برای مسئله زمان بندی انعطاف پذیر مشاغل فروشگاهی -2020 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 selflearning
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. Keywords: Flexible job-shop scheduling problem (FJSP) | Self-learning genetic algorithm (SLGA) | Genetic algorithm (GA) | Reinforcement learning (RL) |
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