Stochastic Health Examination Scheduling Problem based on Genetic Algorithm and Simulation Optimization

Health examination scheduling (HES) plays an important role in utilizing the limited medical resources efficiently while ensuring quality of service for customers in health examination institutions. This study focuses on solving stochastic HES problem with random service time to minimize the expected total cost. To solve this problem, a two-stage simulation optimization algorithm is proposed. To further enhance the efficiency of the simulation, ordinal optimization (OO) strategy is adopted and genetic algorithm (GA) is used as an iterative optimization strategy. The optimal computational budget allocation (OCBA) method is embedded into the rough simulation evaluation stage of the GAOO algorithm, thereby forming a global and adaptive optimization allocation mechanism of simulation resources. Finally, the computational results show that the proposed algorithm outperform the heuristic scheduling rules by achieving better solutions.