Smart data and decision support system for local optimization of electric charging stations

This paper introduces an innovative Decision Support System (DSS) for the local optimization of Electric Vehicles (EV) charging station placement, integrating open data with advanced operational research. In contrast to existing works that typically isolate economic, geographic, or routing factors, our approach synergistically incorporates demographic statistics (ISTAT), geographic data (OpenStreetMap), and commuting patterns to optimize infrastructure deployment. The system is implemented through a Web Application that enables real-time geospatial simulation and cost analysis. Key contributions include a hybrid mathematical model combining Integer Linear Programming with algorithmic strategies—Greedy heuristics, Simple Plant Location Problem (SPLP), and Analytic Hierarchy Process (AHP). This multi-method approach allows users to explore and compare diverse planning scenarios based on multiple optimization criteria. By enabling both global and local optima evaluation, the DSS advances the state of the art in EV infrastructure planning, offering scalable, user-centered, and open-data-driven tools for sustainable urban mobility.