Extending the effectiveness of simulation-based DSS through genetic algorithms

Many real life ill-structured problems involve high uncertainty and complexity preventing application of analytical optimization techniques in building effective decision support systems (DSS). These systems may employ simulation method and search for a “good” solution through “what-if” analysis. However, this method is very time consuming and often overlooks the consideration of many promising alternative solutions. A genetic algorithm (GA) automates the search for “good” solutions by finding near-optimal solutions and increases effectiveness of DSS. This paper introduces a hybrid method based on the combination of Monte-Carlo simulation and genetic algorithms. The combined method is illustrated through application to the marketing mix problem to improve the process for searching and evaluating alternatives for decisional support. The paper compares two methods: MC and MC+GA. It also discusses ways for dealing with crisp and soft constraints contained in the example problem. A business game environment is chosen for experiments. The results of the experiments show that the GA-based approach outperforms human “what-if” method in terms of effectiveness and efficiency.