## 6 Discussion  
Due to the results presented above, one can suggest the proposed mechanism's integration in real-world manufacturing plants. To better understand its applicability, let us examine the functionality of the presented reinforcement learning-based optimization framework in a real-world industry context. In this regard, the mechanism could gather data on the state of manufacturing machines involved in plants. These data could range from the quality of output inventory to the suppliers providing raw materials. Corresponding to the gathered data, the mechanism could make adjustments to operations, processes, output, and returned products, aiming at improving the revenue stream of the plant. Specifically, it was indicated in the previous section that the mechanism prefers reusing manufactured material over producing new items. Under these terms, the generation of low-cost products is supported by remanufacturing and refurbishing low-quality and used goods contained in inventory. Furthermore, the mechanism could improve the efficiency and productivity of implemented manufacturing machine by generating make-to-stock items, given the demand awareness demonstrated by production policies, e.g., BS. Along with this, the machine could sustain the near-perfect condition of machines and output products through ad hoc maintenance policies, such as CBM. Summarizing the mentioned merits, the proposed reinforcement learning-based optimization framework supports smart lean-green manufacturing and enhances the decision-making happened on highly complex plants by intelligently automating the operation scheduling. Finally, despite being evaluated in a two-stage system, the proposed approach could be handily employed for a plethora of manufacturing systems, e.g., assembly systems, that regularly process items over multiple stages. In this context, the nature of the reinforcement learning/parametric control approach could be marginally modified by the decision-makers. This modification is feasible, as the approach does not require of an explicit model of manufacturing environments and involves configurable parameters that provide effective control over manufacturing and maintenance operations. Therefore, the presented mechanism could be considered as a robust and easily-configurable framework that could be employed in a breadth of real-world manufacturing applications, such as manufacturing scheduling. Despite its strengths, the application of the proposed approach in real-world production systems might involve several challenges. This paragraph examines two challenges that might hinder the real-world implementation of the presented mechanism. Plausible solutions to these challenges are presented as well. The first challenge is the learning process of the agents. The agents would require a high amount of data to achieve optimal behavior within real-world production systems, given their complexity. This process could  
Figure 13: The impact of (**a**) \(\beta\) and (**b**) \(\gamma\) on the performance of the system. be costly and resource-intensive, as the RL agents learn by exploiting/exploring the state-action space. To tackle such a challenge, it would be sensible to implement the presented mechanism for the control of specific components of the systems, e.g., processing machines. The second challenge is the cross-domain application of the derived policies, as they are applicable to specific environments. To this, data mining techniques could be used for the creation of general production rules based on the derived policies. The feasibility of such an approach has been demonstrated in several publications [29; 33]. Finally, the presented framework could be used and expanded by researchers, as it efficiently implements lean green in multi-stage manufacturing systems. In this regard, the framework is capable of minimizing material consumption, improving productivity and quality of the integrated operations, along with assuming an eco-friendly approach with lean green practices.