Mehrnaz Ghamami

Planning charging infrastructure for plug-in electric vehicles in city centers

ABSTRACT Installing charging facilities in existing parking lots in city centers is considered an effective measure to encourage the adoption of plug-in electric vehicles (PEV). This article is concerned with the problem of locating these facilities so as to minimize the total system cost. The problem is formulated as a fixed charge facility location model with charging capacity constraints. The proposed model extends the existing work by allowing unserved demands and considering drivers’ preference for familiar parking lots. Accordingly, inconvenience costs are introduced for unserved demands and for those who have to change their parking lots in order to charge at work. The proposed model is not only always feasible, but also introduces a pricing mechanism so that the level of service (measured by the cost of unserved demands) can be traded off with the infrastructure cost. In addition, a stochastic version of the proposed model is developed to address the effects of uncertain PEV market penetration rate on the planning of charging facilities. A case study is conducted to analyze the sensitivity of the models to different cost components, and to compare the results of the stochastic and deterministic models.

Solving Detour-Based Fuel Stations Location Problems

This article studies the problem of locating fuel stations to minimize the extra cost spent in refueling detours, which belongs to a class of discretionary service facility location problems. Unlike many studies of similar problems in the literature, the proposed model considers capacity constraints and compares different ways to incorporate them in the formulation. Note that ignoring the capacity constraint results in nonoptimal and unrealistic solutions. The proposed models are solved using both an off-the-shelf solver CPLEX and a specialized meta-heuristic method Simulated Annealing developed in this study. The solution methods are tested and compared in two case studies; a test benchmark network and a large-scale network. An effort to overcome the memory limitation of CPLEX through more compact formulation was partially successful: it results in a model that is less tightly bounded by its linear relaxation and hence is much more difficult to solve. In contrast, the Simulated Annealing algorithm scales better and is able to consistently yield high-quality solutions with a reasonable amount of computation time.

Introducing a Design Framework for a Multi-Modal Public Transportation System, Focusing on Mixed-Fleet Bike-Sharing Systems

Bike-sharing is increasingly becoming more popular. Electric bikes as an emerging transportation technology have extended range and are less physically demanding, compared with regular bicycles, thus they can be incorporated into regular bike-sharing systems to attract more users. This study aims at capturing the users’ preference, while considering investors’ limitations and societal cost and benefits of each mode. The problem is defined as a mixed-integer non-liner problem, with nonlinear objective function and constraints. Because of the computationally challenging nature of the problem, a metaheuristic algorithm based on simulated annealing algorithm is proposed for its solution. The performance of the algorithm is tested in this study and convergence patterns are observed. The main findings of this study which are derived from the hypothetical numerical example, include but are not limited to: (1) the most popular public modes are bus and pedelec, because these two modes (bus and pedelec) are less expensive and have the ability to traverse longer distances in comparison to similar modes (i.e., e-scooter/car and bike), and (2) for small communities with short travel distances (feasible within the ranges of active modes), users would not choose fuel-consuming modes, and thus their choice is insensitive to fuel cost.