A simulation based GA for multi-objective optimization in patient flow distribution

The imbalanced development among different levels of healthcare facilities has become a major social issue in China´s urban healthcare system. In this research, we propose a methodology to find the optimal macro-level patient flow distribution in terms of multi-dimension inputs and outputs for the hierarchical healthcare system. This research integrates Discrete-Event Simulation (DES), multi-objective programming and simulation optimization. The problem is solved by the ideal multi-objective optimization procedure. We develop an algorithm integrating DES and Genetic Algorithm (GA), where the DES model is functioned as the “fitness function” to find the Pareto optimal solution set. A case study of a two-level healthcare system, which consists of the Peking University Third Hospital (PUTH) and fifteen healthcare centers in Haidian District, Beijing, China, is carried out to implement the multi-objective simulation optimization model. The result of case study confirms the imbalance of the patient flow distribution and provides a Pareto optimal set for decision makers.