Multi-objective simulation optimization for medical capacity allocation in emergency department

Abstract

This study aims to investigate medical resource allocation problems by multi-objective simulation optimization to address the long-term overcrowding situations experienced in hospital emergency departments (EDs). With resource constraints considered, decision makers at EDs must determine the number of doctors, nurses, lab technicians, and other medical equipment to allocate efficiently these medical resources and simultaneously minimize the average patient length of stay in the system and the medical resource wasted cost. Therefore, this study first proposes a multi-objective stochastic optimization model to identify the optimal number of all medical resources at the EDs. In addition, a multi-objective simulation optimization algorithm by integrating non-dominated sorting particle swarm optimization (NSPSO) with multi-objective computing budget allocation (MOCBA) and an ED simulation model is developed and constructed to address this problem, respectively. Specifically, NSPSO searches for potential solutions to medical resource allocation problems. MOCBA identifies effective sets of feasible Pareto medical resource allocation solutions and effective allocation of simulation replications. An ED simulation model based on the operation flows of EDs in Taiwan was constructed to estimate the expected performance value of each resource allocation solution generated by NSPSO. The effectiveness and performance of integrated NSPSO and MOCBA was verified by computational experiments.