[2603.27066] Dynamic resource matching in manufacturing using deep reinforcement learning

Computer Science > Machine Learning arXiv:2603.27066 (cs) [Submitted on 28 Mar 2026] Title:Dynamic resource matching in manufacturing using deep reinforcement learning Authors:Saunak Kumar Panda, Yisha Xiang, Ruiqi Liu View a PDF of the paper titled Dynamic resource matching in manufacturing using deep reinforcement learning, by Saunak Kumar Panda and 2 other authors View PDF HTML (experimental) Abstract:Matching plays an important role in the logical allocation of resources across a wide range of industries. The benefits of matching have been increasingly recognized in manufacturing industries. In particular, capacity sharing has received much attention recently. In this paper, we consider the problem of dynamically matching demand-capacity types of manufacturing resources. We formulate the multi-period, many-to-many manufacturing resource-matching problem as a sequential decision process. The formulated manufacturing resource-matching problem involves large state and action spaces, and it is not practical to accurately model the joint distribution of various types of demands. To address the curse of dimensionality and the difficulty of explicitly modeling the transition dynamics, we use a model-free deep reinforcement learning approach to find optimal matching policies. Moreover, to tackle the issue of infeasible actions and slow convergence due to initial biased estimates caused by the maximum operator in Q-learning, we introduce two penalties to the traditional Q-learn...