We present a new technique for finding solutions to facility location problems. Traditional techniques are usually sensitive to small details of the problem. We present a more general method which generates rules that can be applied to a range of problems. Because these rules are more general, they help researchers build intuitions about their models and help managers more easily apply the model?s result to real world situations.

We use a classifier to learn about the important features of the model. To generate new rules, we use a genetic algorithm. Although classifiers are traditionally used to find key features of documents or other similar objects, we find that they can also discover important attributes of facility location models. Based on these attributes, the classifier chooses what facilities should be built. While still only a proof of concept, our results indicate classifiers are a reasonable tool for solving facility location problems.