# A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
## 6 Bibliometric Analysis of Studies on RL in Production Scheduling  
The bibliometric analysis of RL in the context of production scheduling is focusing here on its theoretical foundations and practical implications, between the years 1996 and 2024. The first two phases involve the collection of bibliographic data, which was gathered via Scopus database. The search was restricted to this database due to its prominence as a comprehensive repository of scientific literature and its frequent utilization in academic assessments. The inclusion criteria for the purpose of analysis presented in Sections 6.1-6.4 encompassed publications that contained the terms \"reinforcement learning\" and 'production' and'scheduling'. Besides that, the terms \"reinforcement learning\" and \"deterministic scheduling\" or \"reinforcement learning\" and \"uncertain scheduling\" were used for analysis carried out in Section 6.5. In the next two phases of the data sorting and presentation, the \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{3}{c}{**Category: Environment**} \\ \hline \multicolumn{2}{c}{**Scheduling Type**} & \multicolumn{1}{c}{**Reward**} & \multicolumn{1}{c}{**Contribution**} \\ \hline Complex Job Shop [18] & Penalties & Advanced DRL algorithm optimizing production scheduling, highlighting adaptability, visibility, and optimization. \\ Flow Shop [25] & Finished order vs. overdue penalty & Efficient manufacturing schedule considering labour skills and adaptive machinery. \\ Flexible Job Shop [74] & Tardiness/machine utilization rate; energy cost; late products & Online rescheduling minimizing tardiness or maximizing machine usage. MARL method optimizing for efficiency and environmental targets. \\ Job Shop [54] & Penalties & DQN technique for minimizing task completion time in dynamic scheduling. MADRL optimizing system-level performance considering workpiece interactions. \\ \hline \multicolumn{3}{c}{**Category: Method**} \\ \hline Complex Job Shop [18] & Penalties & DRL algorithm for optimizing scheduling, improving adaptability, visibility, and optimization. \\ Flow Shop [25] & Finished order vs. overdue penalty & Efficient schedule considering labour skills and machinery interactions. \\ Flexible Job Shop [24] & Tardiness/machine utilization rate; energy cost; late products & Reinforcement learning minimizing completion times in dynamic job scheduling. \\ \hline \multicolumn{3}{c}{**Category: Reward**} \\ \hline Complex Job Shop [62] & Penalties & Job scheduling with multi agent in resource pre-emption learning \\ Flow Shop [60] & Finished order vs. overdue penalty; energy cost; late products & Optimizing production plants for efficiency and sustainability. \\ Flexible Job Shop [19] & Tardiness/machine utilization rate; energy cost; late products & Addressing reinforcement algorithm in scheduling, aiming at decision making in Flexible Job Shop. \\ \hline \end{tabular}
\end{table} 
Table 1: Contributions of reinforcement learning on production scheduling. Microsoft Excel and VOSviewer software 1.6.20 were employed to extract the necessary information, such as the annual scientific outputs, most relevant sources, most cited author, and keyword co-occurrence. The process of selected studies for mentioned research methodology is synthetized in Figure 4, according to PRISMA guidelines. Using the above-specified keywords and search criteria (for Sections 6.1-6.4 n = 634 publications and for Section 6.5 n = 734 publications), together 1368 co-authored articles were found until 27 May 2024, when all document types were included in review analysis (new studies n = 1154 and studies included in previous version of review n = 214). Review analysis of Sections 6.1-6.4 included 634 co-authored articles searched within \"Article title, Abstract and Keywords\".