# A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
## 8 Conclusions  
The existing body of research on production scheduling primarily consists of studies conducted within the domains of Computer Science, Engineering, Mathematics, Decision Sciences, Business, Management and Counting, Energy, Chemical Engineering, and Material Science. These scientific disciplines, notably 'Engineering' and 'Computer Science' have exerted significant influence on development of production scheduling based on RL. The examination of bibliometric findings frequently indicates that an increase in the quantity of published articles is associated with recognition of progressive trends in the subject. Also, for this reason, a bibliometric analysis is becoming more and more beneficial in a variety of academic fields since it makes mapping scientific information and analyzing research development objective and repeatable. The use of this method enables us to identify the networks of scientific collaboration, to establish connections between novel study themes and research streams, as well as show the connections between citations, co-citations and published productivity in the field. The main contribution of this article can be summarized by two steps:  
1. This review brings additional insights into RL in production scheduling by providing the following new features: * The citation trend for RL on production scheduling field from 1996 to 2024 (see Figure 5) has been carried out that shows that increasing trend starting from 2019 to present rapidly continues in noticeable rise in their number. * The analysis of the most relevant sources from the viewpoint of number of their citations (see Figure 7) has been performed to identify the most impacted sources where the latest knowledge in this field is available. * The most influential authors in this domain were identified (see Table 3) for the determination of the cutting-edge state in RL-based production scheduling. * The quantitative comparison of deterministic and uncertain scheduling methods based on RL has been conducted (see in Table 5). It showed that deterministic scheduling methods using RL is in a dominant position against RL-based uncertain scheduling techniques. * The quantitative comparison of stochastic and fuzzy scheduling methods has been given in Figure 10(b). From the comparison, it has been observed that RL is predominantly used in stochastic scheduling methods comparing with its application in fuzzy scheduling methods. 2. The contribution of this paper can be also seen in the following activities and resulting statements: * The publication trend for RL in production scheduling field has been updated by mapping period from 1996 to 2024 (see Figure 5). It showed that exponential growth of the publications starting from 2019 [28] rapidly continues in noticeable rise in their number. * The updated list of the most relevant sources published research on RL in production scheduling was compiled in Figure 6. It has been found that five of ten sources identified until 2021 belong among top ten sources identified until 2024. It means that due to the growing interest about this research field, at the same time, the number of journals covering this topic is increasing. * Actualization of the quantitative comparison of the most frequent deterministic scheduling methods based on RL was provided in Figure 10(a). In this context, the previously identified trend has been confirmed that RL-based scheduling algorithms are mostly used to solve job shop scheduling problems, while flow shop production presents the second most important type of scheduling problems. * The updated categorization of newly developed RL algorithms has been elaborated and presented in Figure 3 that can provide better implementation support for decision making in real-world problems. * The updated classification of scheduling methods is provided in Figure 10. In principle, it can be used for easier selection of the scheduling techniques to solve specific types of problems.