| Abstract [eng] |
Genetic algorithms are one of the best ways to solve a problem for which little is known. They are very general algorithms and will work well in any search space. All you need to know is what you need for the solution to be able to do well, and the genetic algorithm will be able to create a high quality solution. Genetic algorithms use the principles of selection and evolution to produce several solutions to a given problem. In this research, we used genetic algorithms to solve production scheduling problems. There were three problems in total, each with a different difficulty, which were approached by using different kinds of mutation (exchange, inversion, insertion) and crossover (cycle, order, position based) combinations, and different population sizes (10, 25, 50, 75). Each mutation and crossover pairs were generated ten times and then the best solution was registered. Each generation was timed, so that we could compare time required to solve each problem using each population size and mutation / crossover pair. The results have shown, that position based crossover and insertion mutation pair gives the best solutions while solving all three of these problems. Also, the results have shown that insertion mutation gives the best solutions among other mutations. Although the largest population (size 75) was expected to produce the best solutions, it failed to find a better solution then the population of size 50. In conclusion, although the results have shown what type of mutation and crossover pair gave the best results for these production scheduling problems, it does not mean, that we would get the same results while solving completely different scheduling problems. |
