# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 6 Conclusions  
This study addressed the complex challenge of optimally allocating IoT medical devices to patients in healthcare environments by formulating the task as a bi-objective scheduling problem that explicitly balances operational cost with quality of care. We incorporated a rich set of real-world constraints--such as device compatibility, setup durations, time availability windows, and battery limitations--into a novel optimization framework. To solve this NP-hard problem efficiently, we developed a tailored heuristic based on the NSGA-II algorithm. The results of our study confirm the successful achievement of all research objectives. First, the proposed model offers a realistic and flexible structure that captures the trade-offs inherent in clinical scheduling, enabling decision-makers to make data-driven choices. Second, the NSGA-II-based approach generates diverse high-quality solutions rapidly: meaningful options appear within seconds, and a refined Pareto front is available in under a minute, making it suitable for real-time use. Third, our evaluation shows that the method outperforms both baseline heuristics and manual scheduling approaches, with an average reduction of 6.3% in operational costs (reaching up to 12%) and improvements in care quality of up to 20% (with an average of 10%) based on patient-priority metrics. These findings provide a clear and positive answer to our research question, demonstrating that a bi-objective scheduling strategy can effectively balance economic and clinical considerations, even under realistic constraints. The practical implications of this research are considerable. Healthcare facilities can deploy the system as part of real-time scheduling dashboards to assist staff in planning and revising device assignments efficiently. In high-pressure environments like emergency rooms or intensive care units, the system's rapid re-optimization capability can support immediate responses to shifting clinical needs. Moreover, by embedding the algorithm into medical center information systems, healthcare providers can automate the scheduling of connected devices in smart healthcare settings. In mobile or remote care environments, where resource constraints are more severe, the model supports strategic, data-driven allocation to maximize both coverage and impact. Despite promising results in optimizing IoT medical device allocation via bi-objective scheduling, limitations exist. The simulation might not fully reflect real-world unpredictability like sudden patient changes or device failures. The model's accuracy depends on reliable input data, potentially limiting it in data-scar settings. While NSGA-II performed well, its scalability for much larger systems needs further study. Lastly, user adoption and integration into current hospital workflows were not evaluated, which could affect real-world implementation. Looking ahead, the proposed framework opens up multiple promising avenues for both theoretical advancement and practical deployment in the broader context of smart and connected healthcare. Beyond immediate healthcare facility scheduling needs, this approach can serve as a foundation for developing fully autonomous healthcare logistics systems, where device assignment, personnel scheduling, and patient flow are coordinated in real time. Its adaptability positions it as a core component in next-generation hospital management platforms, potentially integrating with electronic health records (EHRs), remote monitoring systems, and AI-driven clinical decision support. Furthermore, the methodology is applicable to other resource-constrained domains beyond healthcare, such as eldercare facilities, disaster response units, or home-based telehealth services, where balancing efficiency with service quality is equally critical. Future research will focus on investigating how priority configurations influence the trade-offs between cost and quality of care, with the goal of identifying optimal weight settings and dynamic adjustment strategies.