Multi-objective simulation optimization:: A case study in healthcare management

This study presents an approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. This method was modified to perform the search considering the mean and the variance of the responses. This way, the selection process of the genetic algorithm is performed stochastically, and not deterministically like most of the approaches reported in the literature. The methodology was tested using a simulation model of a cancer treatment facility created by the authors. The multi-objective optimization heuristic was successfully used to improve the performance of the model relative to four different system objectives. Empirical results show that the methodology is capable of generating an important part of the Pareto optimal frontier, mostly concentrated in the center portion, where practical solutions are generally located.