The Fuel Refueling Location Problem (FRLP) consists of finding the optimal siting of charging stations for electric vehicles (EVs), specifically considering the limited travel range of EVs. This problem becomes particularly relevant when the goal is to facilitate long-distance travel using EVs. A variant of the FRLP, known as the Deviation Fuel Refueling Location Problem (DFRLP), accounts for the possibility that drivers may deviate from their preferred routes to ensure sufficient charging for completing their trips. In this presentation, we study formulations of the FRLP and DFRLP used in the literature, examining their strengths. Furthermore, we explore approaches to tighten the formulations.

In this study, we model the congestion in the electric vehicle (EV) fast chargers as a traffic assignment problem (TAP) with recharging constraints. The model aims to find the most congested stations and to further analyze them to increase the service level. We also work on a gradient projection algorithm implementation for the problem. As the case study, we analyze the Province of Quebec, Canada in a collaboration with Hydro-Quebec, the administration responsible for establishing and maintaining EV charging stations. Preliminary computational results revealed that the proposed model can estimate the congestion in the stations accurately.

We consider a provider of electric vehicle charging that operates a network of charging stations and uses time-varying pricing to maximize profit and reduce the impact on the electric grid. We propose a bilevel model with a single leader and multiple disjoint followers. The customers (followers) makes decisions independently from each the other. The provider (leader) sets the prices for each station at each time slot, and ensures there is enough energy to charge. The charging choice of each customer is represented by a combination of a preference list of (station, time) pairs and a reserve price. The proposed model takes thus into accounts for the heterogeneity of customers with respect to price sensitivity and charging preferences. We define a single level reformulation based on the reformulation for the rank pricing problem. Computational results highlight the efficiency of the new reformulation and the impact of the model on the grid peak.

The problem of planning the installation or extension of EV charging infrastructure has attracted considerable research attention in the scientific community. One interesting thread in this literature relates to the design of charging networks for supporting a privately owned fleet of EVs. In this paper, we consider the problem of optimizing the charging and parking infrastructure for supporting an autonomous fleet of ride-hailing vehicles with the objective of maximizing its expected operational profit.
The dynamic and stochastic nature of ride-hailing services makes it challenging to measure the expected operational profit of a particular charger configuration. To overcome this issue, we make use of a simulator to estimate the expected operational profit associated with each solution and formulate the problem as a discrete simulation-based optimization (DSO) problem.
We implemented a novel DSO method based on the classical algorithm of Nested Partitions (NP). Our proposed approach uses the mathematical structure of the feasible space of the problem to construct auxiliary integer linear programming formulations to inform partitioning and sampling. We discuss a strategy for biasing the proposed sampling strategy, so as to improve the average quality of the sampled solutions, and boost the overall performance.
To illustrate the effectiveness of our method, we consider a case study in the context of New York City and compare the proposed strategy to NP, and to the solutions obtained through a non-simulation-based heuristic.