# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 2 Literature Review
Improved scheduling quality and efficiency were demonstrated through the autonomous learning of optimal actions in complex operational settings [22]. An integrated scheduling method was developed, incorporating dispatching strategies and the conflict-free routing of autonomous mobile robots (AMRs) in flexible job shops. Enhanced coordination and reduced delays were realized by simultaneously optimizing job assignments and AMR paths [23]. A digital-twin-driven service model was proposed to optimize the allocation of manufacturing resources within a shared production environment. Significant improvements in resource utilization and operational flexibility were achieved, highlighting the value of virtualization and real-time data in modern manufacturing systems [24]. Heuristics [25], meta-heuristics [26], swarm optimization [27], immune algorithms [28], knowledge-based systems [29], fuzzy logic, neural networks [30; 31; 32] and hybrid techniques [33] are the predominant strategies that have been developed over the years to tackle the dynamic scheduling problem [34]. Real-time information during production on a shop floor is essential to identify the current situation, the level of progress until a certain checkpoint and the issues that have been raised, so as to react and execute a rescheduling task if needed [35]. Apart from the above-mentioned approaches, data-driven methods such as machine learning (ML) are receiving attention. A toolbox of agents was developed to schedule tasks within a bicycle industry paint shop. Improved coordination and reduced processing delays were achieved by dynamically assigning operations to the available resources [36]. A deep reinforcement learning framework was implemented to enhance tool life prior to failure. Increased efficiency and extended tooling usage were obtained by autonomously adjusting the cutting parameters based on real-time performance feedback [37]. A deep reinforcement learning approach was applied in assembly sequence planning while accounting for user preferences. Enhanced flexibility and user satisfaction were realized as the algorithm adapted assembly orders to specific constraints and requirements [38]. A comprehensive review of deep reinforcement learning methods was presented for smart manufacturing in the Industry 4.0 and 5.0 contexts. Emerging trends, potential benefits and practical implications were identified, offering insights for future research and industrial implementation [39]. The ascension of Industry 4.0 has positioned ML approaches as an appealing solution to tackle manufacturing challenges due to the availability of data, high computing power and large storage capacity. In this context, researchers have tried to develop and apply new technologies and algorithms [40]. An intelligent scheduling approach using reinforcement learning was developed and evaluated. Enhanced decision-making capabilities and improved scheduling performance were demonstrated by adapting to changing conditions in real time [41]. In another approach, to select the best dispatch rule for a machine at each decision point, a neural network was trained, using shop floor characteristics inherited from a simulation [42]. When there are two or more jobs in the queue, the model decides which rule a machine should follow in order to choose the next job for processing. However, despite the large amount of ML applications for scheduling, in recent years, another popular ML approach has garnered attention: reinforcement learning (RL). [PERSON] and [PERSON] [43] define RL as being simultaneously a problem, a class of solution methods that work well on a class of problems and the field in which these problems and their solution methods are studied. The key issue that this technique raises is that no prior knowledge of previously applied schedules is needed. The purpose of RL is for the agent to learn an optimal, or nearly optimal, policy that maximizes the reward. [PERSON] et al. [44] presented a DRL agent for automatic decision-making for a chemical reactor operation.