OPTIMIZING 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 steelworks has a number of different entity of object types. Usin g the number of each entity type as parameters, it is possible to find better and worse combinations of parameters for various management ob jectives. A simple real-coded genetic algorithm is presented that opti mises the parameters, demonstrating the versatility that genetic algor ithms offer in solving hard inverse problems.