## 5 Analysis of Simulation Experiments
### Cost Analysis  
This section analyzes the performance of RL-based mechanisms in terms of costs. The evaluated mechanisms are namely RL(EK-CBM), RL(BS-CBM), RL(PM), RL(CM), RL(CBM), and RL(R-Learning). The aim of the analysis is to investigate the impact of maintenance, remanufacturing and refurbishing costs on the cost-effectiveness of the investigated system under those mechanisms. In respect to that aim, the selection of the RL-based mechanisms was based on their performance depicted in Figures 5 and 6. According to these figures, RL(EK-CBM) and RL(BS-CBM) are the best-performing mechanisms, while  
Figure 5: A performance comparison of RL-based frameworks.  
Figure 6: The cost-effectiveness of best-performing RL-based frameworks against maintenance-based RL ones.  
RL(PM), RL(CM), RL(CBM), and RL(R-Learning) are the worst-performing ones. Given that, the analysis of this section attempts to explain their performance by assessing the number of maintenance, remanufacturing, and refurbishing operations they authorized within the system.  
Figure 7 shows the maintenance costs attained by different versions of the proposed scheduling mechanism. In this figure, RL(R-Learning) procured increased costs compared to its counterparts. This illustrates that the decision-making agent of this version decided to recurrently maintain the system to prevent degradation. Due to this repetition, essential processes, such as remanufacturing and manufacturing, were likely delayed, resulting in decreasing the productivity of the system, which is merely verified in Figure 5. Moreover, the maintenance-related costs of the versions integrating only ad hoc maintenance policies are lesser than the ones of RL(EK-CBM) and RL(BS-CBM). It is likely that the two-stage system under the latter mechanisms was maintained in late degradation stages. Finding itself in these stages, the system tends to procure increased costs maintenance-related costs, as mentioned in Section 4. Despite that, RL(EK-CBM) and RL(BS-CBM) managed to be sufficiently profitable as suggested by Figures 5 and 6.  
Figure 8a,b illustrate how variations in the proposed mechanism perform in terms of remanufacturing and refurbishing-related costs. Based on these figures, an interesting note should be made in respect to the attained costs. That is, the costs relevant to refurbishing activities are relative higher than the ones procured after conducting remanufacturing operations. This indication can be attributed to the significant quantity of items received from customers, considering the number of refurbished products illustrated in Figure 7. Clearly, the system revenue stream is depended on the refurbishing of returned items. Furthermore, similar to Figure 7, the costly version of the mechanism is RL(R-Learning), procuring the highest remanufacturing and refurbishing costs compared to its counterparts. That is, this indication suggests that the described variation's agent decided on frequently generating remanufactured and refurbished items to decrease the amount of faulty and return products, respectively.  
In terms of refurbishing activities, Figure 8a suggests that RL(BS-CBM) receives lesser costs compared to the other variations. By merely integrating BS policy, the mechanism achieves at generating stock of products by transmitting explicit information regarding demand to facilities. In this context, BS-integrated mechanism outperforms EK-based one, despite being the most profitable variant throughout the simulations. In terms of conducted remanufacturing operations, though, Figure 8b illustrates the cost-effectiveness of both RL(BS-CBM) and RL(EK-CBM). This can be likely attributed to the decreased storage of faulty products. In that case, the majority of the manufactured items are of standard quality, as the system is well-maintained under the CBM policy. Thus, remanufacturing activities are considered to be redundant.  
Figure 7: An analysis of the procured maintenance costs.