  Electric vehicle (EV) supply equipment location and allocation (EVSELCA)
problems for freight vehicles are becoming more important because of the
trending electrification shift. Some previous works address EV charger location
and vehicle routing problems simultaneously by generating vehicle routes from
scratch. Although such routes can be efficient, introducing new routes may
violate practical constraints, such as drive schedules, and satisfying
electrification requirements can require dramatically altering existing routes.
To address the challenges in the prevailing adoption scheme, we approach the
problem from a fixed-route perspective. We develop a mixed-integer linear
program, a clustering approach, and a metaheuristic solution method using a
genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach
simplifies the problem by grouping customers into clusters, while the GA
generates solutions that are shown to be nearly optimal for small problem
cases. A case study examines how charger costs, energy costs, the value of time
(VOT), and battery capacity impact the cost of the EVSELCA. Charger costs were
found to be the most significant component in the objective function, with an
80\% decrease resulting in a 25\% cost reduction. VOT costs decrease
substantially as energy costs increase. The number of fast chargers increases
as VOT doubles. Longer EV ranges decrease total costs up to a certain point,
beyond which the decrease in total costs is negligible.

Optimization and Control