A Particle Swarm Optimization-based Method for Multi-objective Operating Room Planning Problem

This research addresses operating room planning problem with surgical procedure time follow normal distribution. From risk aversion and humane point, a surgical planning mathematical model with the objective of minimizing the risk of operation plans cannot be completed and the condition of patients with infections is developed. A particle swarm algorithm for solving multi-objective optimization is designed. To take advantage of particle swarm algorithm solving continuous problem, the coding scheme transfers discrete optimization to continuous optimization. To produce well-distributed Pareto fronts, our approach uses a variation of the adaptive grid and epsilon-dominated. Real data is used for the experiment and Pareto optimal solution is obtained. Experiment results show the rationality of the proposed model and the effectiveness of the algorithm. Under different values of parameters, the character of algorithm is analyzed.