# A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
## 1 Introduction  
Production scheduling is considered as one of the most critical elements of manufacturing management in aligning production activities with business objectives, in ensuring a smooth flow of goods resources, and in supporting company's ability to remain competitive in the marketplace. Scheduling algorithms play an important role in enhancing production efficiency and effectiveness, and therefore have long been a subject of extensive research in various interdisciplinary domains, such as industrial engineering, automation, and management science [1]. The production scheduling tasks can be solved using three main types of step-by-step procedures such as exact algorithms, heuristic algorithms, and meta-heuristic algorithms [2, 3]. Although an exact algorithm can theoretically guarantee the optimum solution, the NP-hardness of major problems makes them impossible to address effectively and efficiently [4]. Heuristics use a set of rules to create scheduling solutions quickly and effectively without consideration of global optimization. Furthermore, the creation of rules is heavily reliant on a thorough comprehension of the particulars of the situation [5, 6, 7, 8]. Whereas meta-heuristics can produce good scheduling solutions in a reasonable amount of computing time, the way search operators create them significantly depends on the specific situation at hand [9, 10, 11, 12, 13]. In addition, the iterative search process poses challenges in terms of time consumption and applicability in real-time scenarios when dealing with large-scale problems. Scheduling approaches based on reinforcement learning (RL) have proven to be a useful tool in this regard. Reinforcement learning is a subfield within the broader domain of machine learning. RL is considered one of the most perspective approaches for robust cooperative scheduling, which allows production managers to interact with a complex manufacturing environment, learn from previous experience, and select optimal decisions. It involves the process of an agent autonomously selecting and executing actions to accomplish a given task. The agent learns via experience and aims to maximize therewards it receives in certain scenarios. The primary goal of RL is to optimize the cumulative reward obtained by an agent through the evaluation and selection of actions within a dynamic environment [14; 15; 16; 17; 18; 19; 20]. The most current development in artificial intelligence technology has allowed successful application of RL in sequential decision-making problems with multiple objectives which are usable for robot scheduling and control [21; 22]. The research on production scheduling using RL since 1998 has been evolving as advancing optimization techniques compared to metaheuristics. RL significantly improves the computational efficiency of addressing scheduling problems. Numerous research articles of RL on production scheduling have been undertaken since its inception in 1996, establishing a substantial and valuable foundation (see few of them, e.g., [23; 24; 25; 26; 27]), but only two recent review papers [28; 29] paid attention to this subject. However, both the papers published in 2021 were focused on different perspectives of RL-based scheduling algorithms than presented in this paper. In other words, the content of the proposed paper is in a disjunctive and complementary relation to the two mentioned studies. The research question of this paper is multi-faceted including multiple features of this domain that require separate answers. The main features that this paper will address are: What are the main emerging research areas in the field? Which related topics are being covered in production scheduling based on RL? What are typical implementation domains within RL applied to production scheduling? Its novelty lies in providing the additional analyses and assessments regarding RL in production scheduling.