# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 6 Discussion  
The results show that the DRL approach for scheduling is a good solution with promising results and outperforms traditional methods. More specifically, the DRL agent is able to propose a schedule that results in a shorter makespan than the other scheduling approaches by 85%. This means that, in 85/100 testing scenarios, the DRL proposes a more efficient schedule, resulting in a shorter production makespan. Comparing the DRL with each other algorithm separately shows that the DRL performs better in 98% of the cases compared to RANDOM, 98% compared to SPT, 96% compared to LPT and 90% compared to MTRPJ. Lastly, in terms of the average makespan achieved by the algorithms, the DRL results in a makespan that is shorter than the ones achieved by RANDOM, SPT, LPT and MTRJ by 20%, 30%, 13% and 7.5%, respectively.  
To further validate the findings, 95% confidence intervals were computed for the mean makespan achieved by the DRL approach and the other methods. The intervals demonstrate a statistically significant improvement in the makespan performance for DRL across all test scenarios. Additionally, a paired \(t\)-test was conducted, with the \(p\)-values confirming that the differences in the makespan between DRL and the baseline methods were statistically significant (\(p<0.05\)). Moreover, variability in the DRL performance across different scenarios was observed, which was due to the dynamic nature of the production workloads and the DRL agent's policy adjustments based on state exploration. Despite this, DRL consistently outperformed the other methods, reflecting its ability to adapt effectively to uncertain and diverse conditions.  
Traditional optimization methods, such as genetic algorithms (GAs), are well established for their ability to handle complex scheduling problems through global search heuristics [6]. However, these methods often require significant computational time and manual parameter tuning, particularly for dynamic and uncertain production environments. In contrast, DRL offers several distinct advantages. DRL adapts dynamically to changing workloads and system states without requiring complete rescheduling from scratch, leading to significantly reduced computational times. While these methods often require significant computational resources during training due to their iterative and data-intensive nature, their ability to generalize across varying scenarios significantly outweighs this initial cost. Furthermore, DRL integrates decision-making flexibility by learning from the environment, rather than relying on predefined heuristic rules [46; 54]. It effectively navigates vast state and action spaces, making it particularly suited for dynamic and uncertain environments. While traditional methods excel in deterministic scenarios, they may struggle with real-time adaptability in stochastic environments, a challenge addressed effectively by DRL. Additionally, DRL's capacity to optimize the scheduling decisions while generalizing across different problem scales further positions it as a competitive alternative for modern manufacturing systems [51]. Despite its computational intensity during training, DRL achieves superior long-term adaptability and scalability, as highlighted by the results of this study, with an observed makespan reduction of up to 20%. The proposed implementation demonstrates notable advantages, such as scalability to larger production setups and adaptability to dynamic workloads. This aligns with other findings in the literature, such as those of [PERSON] [60], who highlighted the efficiency of DQNs in approximating optimal policies in high-dimensional state spaces. Furthermore, by leveraging state space reduction and task prioritization, the computational burden is minimized without sacrificing the solution quality, underscoring its practical applicability in smart manufacturing environments [51].