# A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
## 6 Bibliometric Analysis of Studies on RL in Production Scheduling
### Identification of the Related Research Areas  
The goal of this part of the article is to identify areas of research in which the issue of production scheduling based on RL is of interest. For this purpose, the keyword analysis application has been employed to help separate important research themes which has received a high interest of researchers from less important ones. To obtain relevant and representative categories not including less significant ones, the following setting has been used: Minimum number of occurrence of keywords--14. Based on this restriction, 88 keywords meet the threshold from the total 3767 keywords. Those main keywords that were produced automatically from the titles in the papers on production scheduling based on RL along with their occurrence are shown in Figure 9.  
As can be seen, topics can be divided into five clusters based on a computer algorithm, while each cluster has a different color as shown in Figure 9. The keywords co-occurrence map highlights these clusters where the darker the color, the greater the density value is. It allows to identify relevant research topics and their mutual relationships. Based on the obtained bibliometric results extracted from VOSviewer, ten related topics that are very close to the explored research domain were identified as shown in Table 4.  
The results from Table 4 point to their consistency with practical reality and operation research goals. For example, in recent years, there has been evidently increased interest in using reinforcement learning for optimization of real-time job scheduling tasks [76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86]. This fact can be correlated with the continuing trend of mass customization in the production of consumer goods [87; 88]. As known, for mass customization is characteristic to meet dynamically changing user requirements in time, while customized products need to be completed by different deadlines. Accordingly, efficient real-time job-scheduling algorithms based on DRL become essential. The next important method that is ranked among the top 10 co-occurrences keywords is production control. It uses different control techniques to meet production targets regarding production schedules and quality (see, e.g., [89; 90; 91; 92]). The next important co-occurred keyword in Table 3--multi agent systems. In general, incorporating multi-agent systems into reinforcement learning for production scheduling offers numerous advantages in terms of flexibility, scalability, and adaptability [93; 94; 95]. By enabling decentralized decision-making and continuous learning, these systems can effectively handle the complexities and dynamics of modern production environments leading to more efficient and resilient scheduling solutions [96]. Among the co-occurrences keywords it is possible to highlight also'smart manufacturing' that represents the implementation domain of production scheduling based on RL. Even though smart manufacturing has also become a buzzword, which also has its drawbacks, this conception is gradually being established as the new manufacturing paradigm. On the other hand, complexity of smart manufacturing network infrastructures becomes higher and higher, and the uncertainty of such manufacturing environment becomes a serious problem [25]. These facts lead to the necessity of applying advanced dynamic planning solutions that also includes production scheduling using RL. This paragraph simultaneously answers to the main research question formulated in Section 1. Further, application of RL in production scheduling will be here analyzed through bibliometric means from a viewpoint of different scheduling problems.