This example tackles the growing challenge of optimally placing and sizing electric vehicle (EV) charging stations in urban environments. With EV adoption rising, particularly in the United States, the need for accessible and cost-effective charging infrastructure is critical. Many EV owners rely on home charging, but the availability of well-positioned public charging stations supports market growth and alleviates "range anxiety," where drivers worry about running out of power before reaching a charger.
The goal is to maximize accessibility and minimize infrastructure costs, taking into account location density, demand patterns, and geographical constraints. To address this, the example uses the Vulture algorithm, a Particle Swarm Optimization (PSO) method adept at solving non-linear, non-convex problems in continuous spaces. By effectively planning charging station deployment, urban planners can enhance EV infrastructure, helping cities advance toward sustainability goals and facilitating the broader shift to cleaner transportation.
The problem was proposed as part of the 15th Annual AIMMS-MOPTA Optimization Modeling Competition.