Simulation optimization for earthmoving operations using genetic algorithms

This paper presents a methodology for simulation optimization utilizing genetic algorithms and applies it to a newly developed simulation-based system for estimating the time and cost of earthmoving operations. The genetic algorithm searches for a near-optimum fleet configuration that reduces project total cost, and considers a set of qualitative and quantitative variables that influence earthmoving operations. Qualitative variables represent the models of equipment used in each fleet scenario, whereas quantitative variables represent the number of items of equipment involved in each scenario. Pilot simulation runs were carried out for all configurations generated by the developed algorithm, and a complete simulation analysis was then performed for the fleet recommended by the algorithm. The numerical example demonstrates the use of the proposed methodology and illustrates its essential features.