# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 2 Literature Review
The model was successfully implemented in a simulation designed from historical production data, and, as expected, the testing results outperformed those of human schedulers. Another solution was proposed by [PERSON] et al. [45] that considered an AI scheduler with self-organizing and self-learning capabilities, developed through multiple training sessions and the formulation of a composite reward function, to enable scheduling for multi-objective learning. The trained scheduling agent decides the optimal job allocation directly via a single machine. A proximal policy optimization (PPO) combined with dispatch rules and a simulation was proposed by [PERSON] and [PERSON] [46] for the training of an agent to decide the optimal distribution of a job's tasks when a random job arrives. [PERSON] and [PERSON] [47] proposed a methodology to minimize reconfiguring actions in a production system. In their work, DRL was implemented to make autonomous decisions with a built-in DES model. As a result of self-learning, the agent outperformed the conventional First-In-First-Out (FIFO) dispatching rule by completing assigned order lists while minimizing reconfiguration actions. [PERSON] et al. [48] proposed a DRL-based approach to select the best resource for job processing in a given state. A deep reinforcement learning framework was developed to manage dynamic scheduling in smart manufacturing environments, leveraging real-time data for continuous decision-making. Notable improvements in responsiveness and resource utilization were reported, demonstrating the potential of AI-driven approaches to adapt schedules under shifting operational conditions [49]. [PERSON] et al. [22] implemented DRL for the determination of process allocation to resources. The agent was able to handle a single process at a time. Thus, when iterations of this process occurred, the agent achieved the allocation of all processes on the machines. Combining reinforcement learning and dispatch rules, a single machine agent that employs Q-learning was proposed by [PERSON] and [PERSON] [50]. Through RL, a decision-making policy is created for the selection of the appropriate dispatching rule for a resource each time this is required. The scheduling agent proposes the job allocation policy for a single machine. Improved scheduling performance and reduced processing times were achieved by dynamically adapting the dispatching decisions based on the current shop conditions. A variation considering the continuous state features as input in a deep Q-network (DQN) for the approximation of a state-action value function was proposed. The agent proposes a dispatching rule each time a new job arrives and needs to be assigned to a machine [51]. [PERSON] et al. [52] proposed a graph neural network (GNN) to solve job shop scheduling as the agent can learn dispatch rules from scratch with elementary raw features. The agent selects the optimal dispatch rule for job allocation at each decision point for the production resources. [PERSON] [53] proposed a DRL approach implementing a DQN and dispatching rules for dynamic job scheduling to determine the most suitable dispatching rule at each rescheduling point. The agent selects one custom composite dispatching rule that will be applied to all machines. [PERSON] et al. [54] proposed a multi-agent deep reinforcement learning approach for dynamic job shop scheduling environments. An adaptive multi-objective multi-task scheduling method was introduced, using hierarchical deep reinforcement learning to manage complex operational requirements under multiple objectives [55]. Meanwhile, a hybrid intelligence approach combining deep reinforcement learning with an attention mechanism was proposed to enhance dynamic job shop scheduling, offering improved responsiveness and decision quality [56]. Both studies underscore the growing influence of AI-driven techniques in optimizing modern manufacturing processes [56]. Nevertheless, the aforementioned approaches do not implement a large-scale solution.