Practical genetic algorithms
Healthcare II: multi-objective simulation optimization for a cancer treatment center
Proceedings of the 33nd conference on Winter simulation
Multiobjective simulation optimization using an enhanced genetic algorithm
WSC '05 Proceedings of the 37th conference on Winter simulation
Proceedings of the 38th conference on Winter simulation
Computers and Industrial Engineering
Simulation-optimization mechanism for expansion strategy using real option theory
Expert Systems with Applications: An International Journal
A provably convergent heuristic for stochastic bicriteria integer programming
Journal of Heuristics
A Lexicographic Nelder-Mead simulation optimization method to solve multi-criteria problems
Computers and Industrial Engineering
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This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.