Trucking simulation using genetic algorithms

Genetic Algorithms (GAs) are stochastic search and optimization methods inspired by the mechanisms of natural adaptation. In the last two decades they have been researched and applied in a variety of areas. Currently GAs are used extensively in solving complex optimization problems with large but finite search space. This thesis studies two genetic algorithms applied to a trucking simulation problem where trucks travel among dealers in a country and transport commodities from producers to retailers and from retailers to consumers. Both trucks and retailers attempt to survive and make the most individual profits. Trucks and retailers evolve simultaneously in the simulation. Their evolution progress in two economy types is examined. The results show different effectiveness of these two algorithms in the two economy types