Abstract

The main objective of this study was to implement a computational prototype in two stages: the first stage primarily focused on generating efficient routes based on an evolutionary algorithm. In other words, the complex computational problem was solved in the first stage. The second stage then shifted its focus towards determining the fleet size and frequencies using an allocation algorithm. This approach was designed to address the complex combinatorial search problem within a public transportation network. In the first stage, the prototype utilizes the metaheuristic known as Genetic Algorithms (GA). Within the GA operators, an innovative method called "aggregated crossover" is employed, with an additional mutation procedure that maintains feasible descendants. In the second stage, an allocation algorithm is used, taking into account the routes generated in the first stage. The results demonstrate that in the first stage, the GA metaheuristic consistently delivers highly efficient routes in each run, confirming that the combinatorial complexity of the problem is effectively resolved in this initial phase. These results were validated on Mandl's Swiss Road network, showing superior solutions compared to those presented in previous studies. Notably, the execution time for this process is only 35 minutes.