Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route

During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.