A comparative analysis of crossovers in genetic algorithms for route optimization: case studies from Astana and Shymkent, Kazakhstan

Abstract The computation of optimal routes considering multiple factors is a key challenge in operations research, with significant impact on practical decision-making and real-world efficiency. Optimal bus transit routes require efficiency across multiple factors in order to achieve savings in time, cost, fuel consumption, and vehicle amortization. In such constrained urban routing settings, the impact of genetic algorithm (GA) crossover operators remains insufficiently explored, particularly for Path-TSP formulations derived from existing bus transit networks. In this paper, we present a comparative analysis of a genetic algorithm employing different crossover methods. The proposed approach is applied to optimize bus transit routes for key destinations within urban areas. For users with limited time and resources, our framework provides a practical and versatile solution, demonstrating its applicability through experiments on real-world datasets from Astana and Shymkent, Kazakhstan. Our experiments show a good match with existing results reported in the literature. The effectiveness of this approach is validated on real-world datasets, and the results demonstrate strong performance in terms of runtime efficiency, the number of feasible solutions generated, and the frequency of recovering optimal routes.