A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems

作     者：Martins, Miguel S. E. Sousa, Joao M. C. Vieira, Susana 

作者机构：Univ Lisbon IDMEC Inst Super Tecn P-1049001 Lisbon Portugal 

出 版 物：《APPLIED SCIENCES-BASEL》 (Appl. Sci.)

年 卷 期：2025年第15卷第3期

页      面：1211-1211页

核心收录：

基　　金：Fundacao para a Ciencia e a Tecnologia (FCT) [UIDB/50022/2020, 2020.08776.BD] LAETA Programatic Funding [UIDP/50022/2020] 

主　　题：combinatorial optimization reinforcement learning state space action mapping reward design industry, innovation and infrastructure 

摘      要：This paper presents a systematic review on reinforcement learning approaches for combinatorial optimization problems based on real-world industrial applications. While this topic is increasing in popularity, explicit implementation details are not always available in the literature. The main objective of this paper is characterizing the agent-environment interactions, namely, the state space representation, action space mapping and reward design. Also, the main limitations for practical implementation and the needed future developments are identified. The literature selected covers a wide range of industrial combinatorial optimization problems, found in the IEEE Xplore, Scopus and Web of Science databases. A total of 715 unique papers were extracted from the query. Then, out-of-scope applications, reviews, surveys and papers with insufficient implementation details were removed. This resulted in a total of 298 papers that align with the focus of the review with sufficient implementation details. The state space representation shows the most variety, while the reward design is based on combinations of different modules. The presented studies use a large variety of features and strategies. However, one of the main limitations is that even with state-of-the-art complex models the scalability issues of increasing problem complexity cannot be fully solved. No methods were used to assess risk of biases or automatically synthesize the results.