FARM MACHINERY SELECTION USING SIMULATION AND GENETIC ALGORITHMS

Computer simulation and genetic algorithms were used to optimize peanu t farm machinery selection. The objective of optimization was to maxim ize net returns above machinery costs. A computer simulation model was used to determine net returns above machinery costs. The simulation m odel determined net returns above machinery costs for a given machiner y set, but did not find an optimum machinery set. The optimum machiner y set was determined using two search schemes-an exhaustive search and an artificially intelligent search. The exhaustive search scheme invo lved running the simulation model with all possible machinery sets, an d then selecting the machinery set that produced the highest returns. Alternatively, genetic algorithms were used as an intelligent search s cheme to generate machinery sets for the simulation model. A genetic a lgorithm found a near-optimal solution in 10% of the total time requir ed by the exhaustive search. Modifications in the generic algorithm no t only reduced the search time by half; but also improved the quality of the solutions.