Optimising a complex discrete event simulation model using a genetic algorithm

A steelworks model is selected as representative of the stochastic and unpredictable behaviour of a complex discrete event simulation model. The steel-works has a number of different entity or object types. Using the number of each entity type as parameters, it is possible to find better and worse combinations of parameters for various management objectives. A simple real-coded genetic algorithm is presented that optimises the parameters, demonstrating the versatility that genetic algorithms offer in solving hard inverse problems.