Applications of X-Heuristics and data science in sustainable transportation and smart cities

Rapid urbanization and the increasing demand for sustainable transportation solutions present complex challenges in smart cities. Addressing these challenges requires advanced optimization techniques and decision-making. This thesis explores the application of x-heuristics and data science to solve critical optimization problems related to transportation networks and urban mobility. This work also emphasizes the real-world applicability of its methodologies by engaging in European or Spanish research projects aimed at improving smart city infrastructure. Through collaborations with international and Spanish research institutions, this thesis contributes to the development of policies, innovative mobility solutions, and optimization frameworks adapted to the evolving needs of urban environments. By taking advantage of a multidisciplinary approach that combines operations research, artificial intelligence, and data science, this research provides a foundation for advancing the state of the art in smart city transportation and sustainable mobility. Ultimately, this research contributes to the broader goal of building smart, resilient, and sustainable cities by employing the power of optimization and data science.