# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 5 Discussion  
The experimental results offer significant insights into the comparative performance of the NSGA-II heuristic and the SCIP 2-OBJ exact approach for multi-objective healthcare resource allocation. These findings contribute to our understanding of algorithmic performance in complex decision-making and provide practical guidance for real-world applications.  
Consistently, NSGA-II outperformed SCIP 2-OBJ regarding solution quality, as indicated by superior hypervolume and convex hull representation, particularly in moderate complexity scenarios (experiments 1-3). This aligns with previous research highlighting NSGA-II's efficacy in navigating complex Pareto fronts [31].  
However, in highly complex scenarios (experiments 4-6), the exact method showed improved relative performance, especially in solution quality, supporting the theoretical notion that exact methods may excel in certain complex domains [35].  
A key observation was the distinct convergence patterns. NSGA-II demonstrated rapid initial convergence, ideal for time-sensitive healthcare applications, as emphasized in previous studies [36]. Conversely, the exact method exhibited gradual improvement with increased computational resources, suggesting its suitability for offline analysis, consistent with observed time-performance trade-offs [37].  
Scalability experiments (experiments 4-6) revealed that NSGA-II maintained its ability to find high-quality solutions with increasing complexity, though with some relative performance degradation, extending previous scalability studies [38]. Interestingly, the exact method's sampling improved in larger solution spaces, suggesting that the \(\epsilon\)-constraint approach benefits from increased solution diversity, mitigating time constraints.  
The dominance of NSGA-II solutions in the convex hull underscored the importance of comprehensive evaluation metrics. Combining hypervolume analysis with convex hull representation, building on previous hypervolume indicator work [39], provided a more robust evaluation framework.  
Practically, NSGA-II's rapid high-quality solution generation makes it ideal for time-critical healthcare resource allocation. Its ability to balance competing objectives suits dynamic healthcare environments.  
The proposed system has several practical implications for real-world healthcare operations. First, it streamlines the patient monitoring workflow by automating the assignment of IoT devices, which reduces reliance on manual planning and minimizes the risk of human error. This leads to more accurate and efficient resource use, especially in high-demand or time-sensitive scenarios. Second, the dynamic re-optimization feature allows the system to adapt in real time to changes such as new patient admissions or shifts in clinical conditions. This ensures that device allocation remains optimal even in unpredictable or rapidly evolving environments, supporting better responsiveness in patient care. Finally, the scalable architecture makes the solution suitable for a wide range of healthcare settings--from small medical centers to large hospitals--offering long-term benefits in terms of operational efficiency, cost savings, and improved patient outcomes. This posi  
\begin{table}
\begin{tabular}{c c c} \hline \hline Exp. & \
\begin{tabular}{c} **Cost Saving with Automatic** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Quality Improvement with** \\ **Automatic Method** \\ \end{tabular} \\ \hline
1 & 0\% & 0\% \\
2 & 2\% & 3\% \\
3 & 5\% & 7\% \\
4 & 8\% & 12\% \\
5 & 11\% & 18\% \\
6 & 12\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between automated method (NSGA-II) and manual scheduling.  
tions the system as a valuable tool for modernizing healthcare resource management and delivering higher-quality care with reduced administrative burden.