Adaptive Hospital Resource Optimization Model (AHROM): A Mathematical Optimization Framework for Healthcare Operational Efficiency under Constrained Resource Environments

Abstract

 Healthcare systems continue to experience increasing operational pressure due to growing patient demand, limited healthcare resources, workforce shortages, and rising operational costs. Efficient hospital resource allocation remains critical for improving healthcare delivery performance and maintaining service quality. This study proposes the Adaptive Hospital Resource Optimization Model (AHROM), a novel mathematical optimization framework designed to improve hospital resource allocation under constrained operational environments. The framework integrates hospital operational indicators, Hospital Pressure Index (HPI), adaptive allocation mechanisms, multi-objective optimization, and adaptive swarm optimization to support efficient resource distribution. Simulated hospital operational data, consisting of 365 daily observations representing one year of hospital activities, were used to evaluate the proposed framework. Variables included patient demand, hospital bed availability, staff availability, equipment availability, waiting time, resource utilization, service quality, and operational cost. Results demonstrated that AHROM reduced waiting time by 34.3%, improved resource utilization by 17.6%, increased service quality by 14.4%, and reduced operational costs by 14.8%. Furthermore, adaptive swarm optimization improved optimization performance by 97.1% and achieved stable convergence after approximately 65 iterations. Comparative analysis demonstrated AHROM's superior performance over traditional allocation approaches and fixed optimization methods. The findings suggest that AHROM provides an effective mathematical decision-support framework that improves healthcare operational efficiency and hospital resource utilization in constrained healthcare environments.