Spatial Optimization of Electric Vehicle Charging Infrastructure in Highly Heterogeneous Cities: A Monte Carlo Tree Search Approach Integrating Socioeconomic and Mobility Indicators

This work proposes a spatial optimization framework based on Monte Carlo Tree Search (MCTS) to support infrastructure planning in complex urban environments. The challenge lies in integrating diverse geospatial and socioeconomic data to balance efficiency, defined as potential demand, with territorial equity, related to mobility needs. The approach formulates the problem as a sequential decision process, capturing the interdependence of location choices and enabling structured exploration of the solution space. Unlike traditional optimization methods that rely on local heuristics or require strong simplifications, this framework accommodates non-linear relationships and competing objectives without sacrificing system complexity. The use of MCTS effectively balances exploration and exploitation, making it well-suited for high-dimensional, non-convex spatial problems. This methodology offers a flexible and scalable tool for urban planning, adaptable to various contexts and constraints. It supports generating solutions that are both efficient and aligned with equity considerations, providing valuable guidance for decision-making in rapidly evolving urban systems.