This study aims to analyze the effect of genetic algorithm parameters on the distribution mileage of the largest logistics service provider in Central Jakarta using a general full factorial design and ANOVA test. This study revealed that all factors and three types of interactions were statistically significant in influencing the distribution mileage. Moreover, the combination of population size, crossover probability, mutation probability, and the number of iterations = (90, 1.00, 0.010, 800) generated the lowest mean value and was significantly different from other combinations. This study provides a different approach to analyzing those factors and interactions that influence finding the shortest route.
Keywords: Genetic algorithms; population size; crossover probability; mutation probability; iteration number; shortest route; general full factorial design; ANOVA; TSP