Reinforcement Learning for Dynamic Job-Shop Scheduling with Setup Time Constraints - Kamil MUSIAŁ; Artem BALASHOV; Joanna KOCHAŃSKA; Dagmara ŁAPCZYŃSKA (Communications of International Proceedings, 2025)
Optimization in job-shop production systems is a very complex task, requiring simultaneous analysis of many factors. This article presents just this type of production in which the problem was to schedule job-shop tasks with significant setup times. In this case, the use of reinforcement learning (RL) was proposed, with particular emphasis on algorithm evaluation: Q-learning, actor-critic algorithms, deep neural networks (DQN), as well as advanced neural graph-based architectures (GNN). As a result of the analysis, it was shown that methods that are based on deep reinforcement learning (DRL) allow to achieve better results than traditional distracting heuristics and metaheuristic algorithms. Neural networks, which are based on heterogeneous graphs, are particularly useful for modeling relationships between machines and operations in the face of dynamically occurring changes in the production schedule. In this article, the methods described are analyzed in the context of production in small and medium-sized enterprises (SMEs), for which intensive technological development is a challenge. Ways of implementing RL solutions for such enterprises were also proposed.
Keywords: Job-shop; optimization of production schedules; neural networks; Industry 4.0