# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 7 Conclusions  
In conclusion, this study has successfully introduced and implemented a novel approach to production scheduling, employing a DRL agent guided by a DNN. The DRL scheduling agent exhibited remarkable performance, achieving an 85% improvement in selecting optimal dispatch rules (SPT, LPT and MTRJ) for each resource in a scheduling problem instance. The comparison against traditional rules, such as SPT, LPT, MTRJ and random job selection, highlighted the superiority of the DRL agent, particularly when applied to real-world data from the bicycle industry. The development of a DES model, replicating the dynamics of the bicycle industry, served as a crucial training basis for the DRL agent. The results not only validate the effectiveness of the DRL agent but also emphasize its potential as a valuable tool to enhance production scheduling, especially in conjunction with established dispatch rules. While DRL demonstrates significant potential in dynamic scheduling, it has limitations that warrant consideration. Training DRL models, such as the implemented deep Q-network, is computationally intensive, requiring substantial time and resources, especially for large-scale or complex production environments [60]. Additionally, the proposed implementation does not fully capture real-world uncertainties, such as supply chain disruptions and the addition of new products or product variants. Lastly, DRL's sensitivity to hyperparameter tuning and the potential for suboptimal policies in highly stochastic settings pose challenges, requiring careful design and the evaluation of the reward structure and state action representation. As future work, we envision expanding the scope of this study by incorporating additional dispatch rules like FIFO and the Earliest Due Date (EDD) into the DRL scheduling agent framework. Furthermore, future endeavors may explore the integration of standardized models, such as the asset administration shell concept (AAS), to parameterize the DRL agent. These anticipated extensions aim to further refine and generalize the applicability of the DRL-based approach, contributing to the ongoing evolution of intelligent production scheduling methodologies. Another topic for improvement is the optimization goal, seeking to include more parameters, such as energy efficiency, the production rate, etc., resulting in a multi-objective DRL agent in a manufacturing environment. The comparison of such multi-objective agents' implementations, following a central architecture and methodology, is a topic of great interest, especially when considering other production environments with different characteristics and conditions, where more limitations or applicability issues may occur. Scalability advancements and framework validation in more complex environments will be addressed in future research as well. This will be also supported by a variability analysis of the deployed scenarios to identify the implications for the DRL performance related to the workload or production setup scale. In addition, the effects of the environmental parameters, requirements and limitations may be another topic of great intertest, especially in larger production systems. Dynamic environments with more unexpected events could also be addressed with the implementation of the DRL framework, such as changes in the capacity and the processing power of resources or urgent production orders and delivery. Lastly, when the scale of the problem is much greater, the state representation and state space reduction could be a topic for further research, regarding the impact of the state space formulation on the quality of the decisions. Conceptualization, K.A., N.N. and D.M. ; Methodology, K.A., P.M. and E.B. ; Software, P.M. and E.B. ; Validation, P.M. and E.B. ; Formal analysis, K.A. ; Data curation, P.M.; Writing--original draft, P.M. and E.B. ; Writing--review & editing, K.A., P.M., N.N. and D.M. ; Supervision, K.A., N.N. and D.M. ; Project administration, K.A. and N.N. ; Funding acquisition, K.A.