Optimization of Emergency Healthcare System with Priority Based Queuing Model and Dynamic Programming

This study introduces a priority-based multi-class, multi-server accumulating priority queuing system designed to balance waiting times and resource utilization under both normal and high-demand conditions. Dynamic adjustment of priority rates ensures minimal delays for high-priority patients during peak periods while maintaining fairness across patient groups. System performance is evaluated using metrics such as queue length, priority accumulation, and a fairness index that capture complex, nonlinear interactions between resource allocation strategies and patient flow. Integration of these performance metrics strengthens the model’s ability to respond to real-world variability and ensures robust performance under unpredictable demand. Advanced queuing frameworks, including Markov-modulated and non-Markovian models, further reveal trade-offs between fairness and efficiency, providing additional insights into adaptive healthcare system design. Class-specific, metric-focused simulations validate system reliability and effectiveness. In addition, dynamic programming is employed to optimize resource allocation by balancing urgency and waiting time, thereby enhancing cost efficiency and fairness. Visualizations such as resource utilization plots and patient distribution graphs support informed and effective management decisions.