scholarly journals The Electric Vehicle Routing Problem with Capacitated Charging Stations

2021 ◽  
Author(s):  
Aurélien Froger ◽  
Ola Jabali ◽  
Jorge E. Mendoza ◽  
Gilbert Laporte
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Branch And Cut ◽  
Continuous Time Model ◽  
Time Model ◽  
Routing Problem ◽  
Multiple Charging ◽  
Charging Stations

Electric vehicle routing problems (E-VRPs) deal with routing a fleet of electric vehicles (EVs) to serve a set of customers while minimizing an operational criterion, for example, cost or time. The feasibility of the routes is constrained by the autonomy of the EVs, which may be recharged along the route. Much of the E-VRP research neglects the capacity of charging stations (CSs) and thus implicitly assumes that an unlimited number of EVs can be simultaneously charged at a CS. In this paper, we model and solve E-VRPs considering these capacity restrictions. In particular, we study an E-VRP with nonlinear charging functions, multiple charging technologies, en route charging, and variable charging quantities while explicitly accounting for the number of chargers available at privately managed CSs. We refer to this problem as the E-VRP with nonlinear charging functions and capacitated stations (E-VRP-NL-C). We introduce a continuous-time model formulation for the problem. We then introduce an algorithmic framework that iterates between two main components: (1) the route generator, which uses an iterated local search algorithm to build a pool of high-quality routes, and (2) the solution assembler, which applies a branch-and-cut algorithm to combine a subset of routes from the pool into a solution satisfying the capacity constraints. We compare four assembly strategies on a set of instances. We show that our algorithm effectively deals with the E-VRP-NL-C. Furthermore, considering the uncapacitated version of the E-VRP-NL-C, our solution method identifies new best-known solutions for 80 of 120 instances.

scholarly journals Electric Vehicle Routing Problems with Stochastic Demands and Dynamic Remedial Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xianlong Ge ◽  
Ziqiang Zhu ◽  
Yuanzhi Jin
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Heuristic Algorithm ◽  
Search Algorithm ◽  
Rapid Development ◽  
Remedial Measures ◽  
Routing Problem ◽  
Stochastic Demands ◽  
Hybrid Heuristic Algorithm ◽  
Charging Stations

With the rapid development of e-commerce, logistic enterprises must better predict customer demand to improve distribution efficiency, so as to deliver goods in advance, which makes logistics stochastic and dynamic. In order to deal with this challenge and respond to the concept of “green logistics,” an electric vehicle routing problem with stochastic demands (EVRPSD) and proactive remedial measures is investigated, and an EVRPSD model with probability constraints is established. At the same time, a hybrid heuristic algorithm, combining a saving method and an improved Tabu search algorithm, is proposed to solve the model. Moreover, two insertion strategies with the greedy algorithm for charging stations and dynamic nodes are introduced. Finally, a large number of experimental data show that the heuristic algorithm proposed in this paper is feasible and effective.

scholarly journals An iterated local search algorithm for the vehicle routing problem with convex time penalty functions

2008 ◽  
Vol 156 (11) ◽  
pp. 2050-2069 ◽  
Author(s):  
Toshihide Ibaraki ◽  
Shinji Imahori ◽  
Koji Nonobe ◽  
Kensuke Sobue ◽  
Takeaki Uno ◽  
...  
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Penalty Functions ◽  
Local Search Algorithm ◽  
Routing Problem

scholarly journals Electric vehicle routing problem with backhauls considering the location of charging stations and the operation of the electric power distribution system

TecnoLógicas ◽  
2019 ◽  
Vol 22 (44) ◽  
pp. 1-20 ◽  
Author(s):  
Luis Carlos Cubides ◽  
Andrés Arias Londoño ◽  
Mauricio Granada Echeverri
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Power Distribution ◽  
Vehicle Routing Problem ◽  
Distribution Network ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Routing Problem ◽  
Battery Capacity ◽  
Charging Stations

Logistics companies are largely encouraged to make greener their operations through an efficient solution with electric vehicles (EVs). However, the driving range is one of the limiting aspects for the introduction of EVs in logistics fleet, due to the low capacity provided by the batteries to perform the routes. In this regards, it is necessary to set up a framework to virtually increase this battery capacity by locating EV charging stations (EVCSs) along the transportation network for the completion of their routes. By the other side, the Distribution Network Operators (DNOs) express the concern associated with the inclusion of new power demands to be attended (installation of EVCSs) in the Distribution Network (DN), without reducing the optimal power supply management for the end-users. Under these circumstances, in this paper the Electric Vehicle Routing Problem with Backhauls and optimal operation of the Distribution Network (EVRPB-DN) is introduced and formulated as a mixed-integer linear programming model, considering the operation of the DN in conditions of maximum power demand. Different candidate points for the EVs charging are considered to recharge the battery at the end of the linehaul route or during the backhaul route. The problem is formulated as a multi-objective approach where the transportation and power distribution networks operation are modeled. The performance and effectiveness of the proposed formulation is tested in VRPB instance datasets and DN test systems from the literature. Pareto fronts for each instance are presented, using the ε-constraint methodology.

scholarly journals Electric Vehicle Routing with Public Charging Stations

2021 ◽  
Author(s):  
Nicholas D. Kullman ◽  
Justin C. Goodson ◽  
Jorge E. Mendoza
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Combustion Engine ◽  
Routing Problem ◽  
Charging Infrastructure ◽  
Industry Standard ◽  
Routing Strategy ◽  
Dual Bound ◽  
Routing Policies ◽  
Charging Stations

We introduce the electric vehicle routing problem with public-private recharging strategy in which vehicles may recharge en route at public charging infrastructure as well as at a privately-owned depot. To hedge against uncertain demand at public charging stations, we design routing policies that anticipate station queue dynamics. We leverage a decomposition to identify good routing policies, including the optimal static policy and fixed-route-based rollout policies that dynamically respond to observed queues. The decomposition also enables us to establish dual bounds, providing a measure of goodness for our routing policies. In computational experiments using real instances from industry, we show the value of our policies to be within 10% of a dual bound. Furthermore, we demonstrate that our policies significantly outperform the industry-standard routing strategy in which vehicle recharging generally occurs at a central depot. Our methods stand to reduce the operating costs associated with electric vehicles, facilitating the transition from internal-combustion engine vehicles.

scholarly journals The Electric Vehicle Routing Problem with Time Windows under Travel Time Uncertainty

2021 ◽  
Vol 2095 (1) ◽  
pp. 012032
Author(s):  
Dan Wang ◽  
Hong Zhou
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Time Windows ◽  
Routing Problem ◽  
Time Uncertainty ◽  
Travel Time Uncertainty

Abstract Due to environmental friendliness, electric vehicles have become more and more popular nowadays in the transportation system. For many express companies, it is more and more important to meet the predetermined time window of customers. The uncertainty in travel times often causes uncertain energy consumption and uncertain recharging time, thus electric vehicles may miss the time windows of customers. Therefore, this paper addresses the electric vehicle routing problem with time windows under travel time uncertainty, which aims to determine the optimal delivery strategy under travel time uncertainty. To solve this problem, a robust optimization model is built based on the route-dependent uncertainty sets. However, considering the complexity of the problem, the robust model can only solve few instances including the small number of customers. Thus, a hybrid metaheuristic consisting of the adaptive large neighborhood search algorithm and the local search algorithm is proposed. The results show that the algorithm can obtain the optimal solution for the small-sized instances and the large-sized instances.

Sign in / Sign up

Export Citation Format

Share Document