# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 4 Results
### Experimental Campaign
Consequently, in this last case, we observed equivalent results between the two methods in terms of convex hull participation, but due to the lower number of Pareto-optimal solutions generated, the exact method was outperformed in terms of the hypervolume parameter. In conclusion, the increased available time allowed the exact method (SCIP 2-OBJ) to find many more solutions, while the heuristic (NSGA-II) was very fast in the early generations but failed to improve significantly with increased time, which instead benefited the exact method in sampling the solution domain more precisely. This demonstrates that for practical applications where time is critical, the heuristic approach is preferable as it rapidly produces good-quality solutions before converging asymptotically. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Exp.**} & \multicolumn{2}{c}{**No. Pareto-Optimal**} & \multicolumn{2}{c}{**No. Dominated**} & \multicolumn{2}{c}{**No. Solutions in the**} & \multicolumn{2}{c}{**Hypervolume**} \\  & \multicolumn{2}{c}{**Solutions**} & \multicolumn{2}{c}{**Solutions**} & \multicolumn{2}{c}{**Convex Hull**} & \multicolumn{2}{c}{**Negative2-OBJ**} \\ \hline
**ID** & **NSGA-II** & **SCIP 2-OBJ** & **NSGA-II** & **SCIP 2-OBJ** & **NSGA-II** & **SCIP 2-OBJ** & **NSGA-II** & **SCIP 2-OBJ** \\ \hline
1 & 9 & 12 & 1 & 3 & 4 & 2 & 1279 & 1187 \\
2 & 16 & 30 & 4 & 2 & 6 & 2 & 5328 & 5074 \\
3 & 19 & 34 & 0 & 31 & 8 & 0 & 6620 & 5078 \\
4 & 24 & 3 & 2 & 1 & 5 & 2 & 6691 & 57,348 \\
5 & 27 & 11 & 7 & 1 & 6 & 5 & 240,599 & 223,702 \\
6 & 32 & 9 & 6 & 1 & 6 & 5 & 808,678 & 630,125 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental campaign results. Lastly, to evaluate the benefits of our automated method for resource allocation and scheduling in healthcare, we conducted a comparative analysis against the manual practices currently used by managers. Due to data confidentiality constraints (DoD), the manual solution is not disclosed, as it would reveal the decision-making strategies currently adopted by healthcare managers. Instead, we focus on demonstrating the relative improvements achieved by our approach. Specifically, we compare the manual solution provided by experienced managers with the Pareto-optimal solutions generated by our automated system. Since the Pareto-optimal solutions generated by our method outperform the manual one, we report the percentage of cost reduction and quality improvement achieved by the specific solution that dominates the manual schedule. This provides a clear numerical assessment of the advantages of our method, both in terms of financial efficiency and schedule quality, over traditional decision-making practices. The results in Table 7 highlight the clear benefits of moving from manual scheduling to the automated NSGA-II approach. Because managers previously allocated resources manually, the data now show that this automated method can lead to significant gains. Although the first test scenario shows no difference, subsequent scenarios consistently demonstrate cost savings (between 2% and 12%) and improved quality outcomes (between 3% and 20%). These benefits become more pronounced as the scenarios grow more complex, with more patients and devices involved. This trend reflects the increasing difficulty for human managers to juggle the many variables needed for optimal scheduling at larger scales. Adopting automated tools like NSGA-II offers healthcare facilities a strong opportunity to cut costs while improving patient care--especially in complex, high-demand environments. Figure 6: Exp 1 Pareto comparison.