# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 1 Introduction
Our specific objectives are as follows:  
* Formulate the IoT device allocation problem as a bi-objective scheduling model that captures essential real-world constraints. * Design and implement an efficient heuristic solution approach based on NSGA-II that generates high-quality Pareto-optimal solutions within practical time constraints. Evaluate the proposed approach against both existing methods and manual scheduling practices to demonstrate its practical value in healthcare settings. This paper proposes a novel bi-objective optimization model that explicitly addresses the trade-off between operational costs and quality of care in IoT device scheduling. We develop a tailored heuristic solution based on the NSGA-II algorithm, enhanced with domain-specific constraints such as device compatibility, setup durations, availability windows, and battery lifespan. Experimental results demonstrate the effectiveness of our approach. Compared to baseline heuristics and manual scheduling, our method achieves up to a 12% reduction in operational costs (with an average of 6.3%) and care quality improvements of up to 20% (with an average of 10%) based on weighted patient-priority metrics. The system rapidly produces diverse high-quality solutions within just a few seconds, and within less than a minute, it reliably approximates an accurate Pareto front--making it well suited for real-time decision support. These findings significantly advance the field by demonstrating that intelligent, constraint-aware scheduling can simultaneously optimize for clinical and economic outcomes in dynamic healthcare environments. To address the challenge of allocating IoT medical devices in healthcare, we formulated the problem as a bi-objective scheduling model. This allowed us to represent key real-world elements--such as patients, devices, time windows, and care priorities--within a structured optimization framework, aligning them with classic scheduling constructs like jobs, machines, and processing times. The model incorporates essential operational constraints, including device-patient compatibility, setup times, availability windows, continuous monitoring requirements, and battery limitations. These constraints ensure the formulation realistically captures the complexities of hospital environments. By modeling the trade-offs between cost and quality of care, the framework generates a Pareto front of optimal solutions. This empowers healthcare decision-makers with flexible, data-driven options that support efficient resource use and improved patient outcomes. The proposed IoT device allocation model, which balances cost and quality of care, falls within the class of computationally intensive multi-objective scheduling problems. Hard constraints--such as time windows, device compatibility, battery limits, and setup times--further increase its complexity. As even single-objective variants are NP-hard, solving the bi-objective version requires navigating a large, non-convex solution space. Due to the limitations of exact methods for large instances, heuristic and metaheuristic approaches like evolutionary algorithms are preferred for efficiently approximating the Pareto front under realistic time constraints. To address the complexity of allocating IoT medical devices in healthcare, this study presents a heuristic approach based on the NSGA-II algorithm, tailored for bi-objective scheduling. The method uses genetic representations to explore large solution spaces, optimizing for both operational cost and care quality. NSGA-II's evolutionary operators--crossover, mutation, and elitism--refine solutions to generate a diverse Pareto front, giving decision-makers flexible, high-quality options. Unlike single-objective or rigid heuristic methods, our approach dynamically adjusts NSGA-II parameters and integrates real-world constraints like device compatibility and battery limits. Its multi-run, time-bounded design ensures practicality in time-sensitive hospital environments.