MB11 - EV Charging Infrastructure & Facility Location

Transit agencies are adopting electric buses to reduce emissions, but their limited range complicates operations. This study integrates electric bus scheduling with fast-charging station location planning. A path-based ILP model, solved using branch-and-price algorithm, minimizes system costs. A case study evaluates model performance and identifies the most suitable bus type.

Electric vehicle adoption requires efficient charging infrastructure planning. We propose an optimization model to determine the location and capacity of EV charging stations while accounting for user behavior. The framework uses rank-based choice models to represent user preferences along feasible paths and incorporates capacity limits, seasonal demand, and budget constraints. Structural properties of the payoff function yield stronger formulations. Experiments using our industrial partner data demonstrate the model’s effectiveness for supporting charging network expansion decisions.

In this talk, we study a congested facility location problem in which each facility is modeled as an M/M/c queue. We assume that users are self-interested and choose which facility to patronize so as to minimize their expected total time, defined as the sum of travel time and waiting time. This behavior leads to a bilevel problem in which the upper-level decision maker, the network designer, seeks an effective configuration of facilities, while the lower-level problem corresponds to a user equilibrium model. We propose a flexible single-level reformulation that can accommodate various objectives related to overall system efficiency as well as fairness. The resulting reformulation is a convex mixed-integer nonlinear program that can be solved efficiently. We evaluate our solution approach through a case study of a real-world EV charging station network in Montreal, demonstrating the practical relevance of our method.

As fast-charging demand grows, ensuring the operational resilience of electric vehicle (EV) infrastructure under congestion becomes a key challenge in large-scale transportation systems. We study a bilevel charging facility location problem in which EV drivers choose paths based on a disutility function incorporating travel time, charging stops, and queuing delays modeled via an M/M/c system. We develop both arc-flow and path-based bilevel formulations and derive equivalent single-level mixed-integer linear programming (MILP) reformulations using strong duality and piecewise-linear approximations of queuing delays. To address the computational challenges arising from large-scale transportation networks, we propose an exact decomposition algorithm (DA) that iteratively solves a location master problem and an evaluation subproblem capturing path choice and congestion effects. Several acceleration strategies are introduced to improve convergence speed significantly. Computational results show that the proposed DA substantially outperforms the direct solution of the single-level MILP formulations using a commercial solver on small networks. Extensive experiments on large real-world networks from California and a case study in Quebec demonstrate strong scalability, with most instances solved to optimality or near-optimality within reasonable time limits. Benchmark comparisons on a closely related problem from the literature further show that the proposed DA consistently outperforms the state-of-the-art algorithm in both solution quality and computational robustness.