## 5 Results and Conclusions  
Figure 3 presents a cost-benefit plot of results found by the NSGA-II, with a selection rate of 0.25, crossover rate of 0.5 and mutation rate of 0.75 (based on the values used in [15]), evolving a population of 50 individuals for 400 generations.  
Once the solutions are obtained, the possible additional restrictions related to them can be taken into consideration concurrently. This situation gives the decision maker the opportunity of selecting the best maintenance strategy externally, combining short-term business profitability and long-term asset management strategy terms.  
Another enriching feature of the developed approach is that, after defining \"a posteriori\" decision criteria, it is possible to assign optimal CBM levels to items, monitor them through edge IoT devices. In addition, it can detect through edge computing devices that the expected behavior of the system is not the one simulated, and then simulations can be run back again to re-optimize the system. The holistic view of the initiative is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(C_{\boldsymbol{\kappa}}\) & \(\boldsymbol{\tau}\) & \(C_{\textit{hsl}}\) & \(\rho\) & \(\boldsymbol{\alpha}\) & \(\boldsymbol{h_{0}}\) & \(\boldsymbol{V_{0}}\) & \(c_{\textit{hcm}}\) & \(c_{\textit{hpm}}\) \\ \(\boldsymbol{\in}\) & \(u\)/\(\boldsymbol{h^{2}}\) & \(\boldsymbol{\in}\)/\(\boldsymbol{h}\) & \(\boldsymbol{10^{-3}}\) & & \(\boldsymbol{\text{fail}}/\boldsymbol{h}\) & \(u\)/\(\boldsymbol{h}\) & \(\boldsymbol{\in}\)/\(\boldsymbol{h}\) & \(\boldsymbol{\in}\)/\(\boldsymbol{h}\) \\ \hline
6 & 0.02 & 150 & 1 & 0.04 & 0 & 900 & 175 & 160 & 2 \\ \hline \end{tabular}
\end{table}
Table 6: Productive and cost parameters for the painting station.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(C_{\boldsymbol{\kappa}}\) & \(\boldsymbol{\tau}\) & \(C_{\textit{hsl}}\) & \(\rho\) & \(\boldsymbol{\alpha}\) & \(\boldsymbol{h_{0}}\) & \(\boldsymbol{V_{0}}\) & \(c_{\textit{hcm}}\) & \(c_{\textit{hpm}}\) \\ \(\boldsymbol{\in}\) & \(u\)/\(\boldsymbol{h^{2}}\) & \(\boldsymbol{\in}\)/\(\boldsymbol{h}\) & \(\boldsymbol{10^{-3}}\) & & \(\boldsymbol{\text{fail}}/\boldsymbol{h}\) & \(u\)/\(\boldsymbol{h}\) & \(\boldsymbol{\in}\)/\(\boldsymbol{h}\) & \(\boldsymbol{\in}\)/\(\boldsymbol{h}\) \\ \hline \hline
6 & 0.0017 & 25 & 1 & 0.03 & 0 & 180 & 45 & 30 \\ \hline \end{tabular}
\end{table}
Table 5: Productive and cost parameters for the injection machines.  
It is worth remarking that the approach has raised the interest of a company within the Deusto Digital Industry Chair. Therefore, the efforts of the chair will be oriented to consolidate this initiative.  
**Author Contributions:** A.G., writing, conceptualization and investigation; A.O.-Z. and A.S., conceptualization and investigation; and T.A. and E.A., writing and investigation.  
**Funding:** This research was funded by the HAZITEK call of the Basque Government, project acronym HORDAGO.  
**Acknowledgments:** We would like to thank the partners of the Deusto Digital Industry Chair (Eke-Tar, General Electric, Idom, Accenture, Fundacion Telefonica, and Fundacion BBK) for the interest and support shown during this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 4: CBM optimization scheme of the project approach.  
Figure 3: Results of the optimization.