The genetic algorithm, a search algorithm based on the mechanics of natural selection and popula tion genetics, is implemented using a discrete- event computer simulation language. The impe tus behind this study is to integrate a tool typi cally used in the development of descriptive stud ies with a potentially powerful method of optimi zation. The facilities needed to implement a genetic algorithm using a traditional computer language are remarkably similar to a set of functions already existing in most discrete-event computer simula tion languages. Three methods of integrating the genetic algorithm and computer simulation are discussed, then a two-phase study is conducted to assess the feasibility of the theoretical discussion. A free-ranging automated guided vehicle example illustrates the results. Computational experience suggests the genetic algorithm is relatively adapt able for use in a discrete-event computer simula tion environment. Within this environment, ad ditional support for the decision maker is pro vided by combining the power of representational modeling with optimization capability.
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