The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

International audienceDue to new regulations and further technological progress in the field of electric vehicles, the research community faces the new challenge of incorporating the electric energy based restrictions into vehicle routing problems. One of these restrictions is the limited battery capacity which makes detours to recharging stations necessary, thus requiring efficient tour planning mechanisms in order to sustain the competitiveness of electric vehicles compared to conventional vehicles. We introduce the Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and recharging stations (E-FSMFTW) to model decisions to be made with regards to fleet composition and the actual vehicle routes including the choice of recharging times and locations. The available vehicle types differ in their transport capacity, battery size and acquisition cost. Furthermore, we consider time windows at customer locations, which is a common and important constraint in real-world routing and planning problems. We solve this problem by means of branch-and-price as well as proposing a hybrid heuristic, which combines an Adaptive Large Neighbourhood Search with an embedded local search and labelling procedure for intensification. By solving a newly created set of benchmark instances for the E-FSMFTW and the existing single vehicle type benchmark using an exact method as well, we show the effectiveness of the proposed approach

A parallel matheuristic for the technician routing problem with electric and conventional vehicles

The technician routing problem with conventional and electric vehicles (TRP-CEV) consists in designing service routes taking into account the customers’ time windows and the technicians’ skills, shifts, and lunch breaks. In the TRP-CEV routes are covered using a fixed and heterogeneous fleet of conventional and electric vehicles (EVs). Due to their relatively limited driving ranges, EVs may need to include in their routes one or more recharging stops. In this talk we present a parallel matheuristic for the TRP-CEV. The approach works in two phases. In the first phase it decomposes the problem into a number of “easier to solve” vehicle routing problems with time windows and solves these problems in parallel using a GRASP. During the execution of this phase, the routes making up the local optima are stored in a long-term memory. In the second phase, the approach uses the routes stored in the long-term memory to assemble a solution to the TRP-CEV. We discuss computational experiments carried on real-world TRP-CEV instances provided by a French public utility and instances for the closely-related electric fleet size and mix vehicle routing problem with time windows and recharging stations taken from the literature.

An adaptive large neighborhood search approach for solving the electric vehicle routing problem with time windows

The Electric Vehicle Routing Problem with Time Windows (E-VRPTW) is an extension to the well-known Vehicle Routing Problem with Time Windows (VRPTW). Different from VRPTW, the fleet in E-VRPTW consists of electric vehicles (EVs) which have a limited driving range due to their battery charge capacities. Since the battery charge level decreases proportional to the distance traveled, an EV may need to visit recharging stations to have its battery recharged in order to be able to continue servicing the customers along its route. The recharging may take place at any battery level and after the recharging the battery is assumed to be full. Recharging time is proportional to the amount charged. The number of stations is usually small and the stations are dispersed in distant locations, which increases the difficulty of the problem. In this thesis, we propose an Adaptive Large Neighborhood Search (ALNS) method to solve this problem. ALNS is based on the destroy-and-repair framework where at any iteration the existing feasible solution is destroyed by removing some customers and recharging stations from their routes and then repaired by inserting the removed customers to the solution along with the stations when recharging is necessary. Several removal and insertion algorithms are applied by selecting them dynamically and adaptively based on their past performances. The new solution is accepted according to the Simulated Annealing criterion. Our approach combines the removal and insertion mechanisms from the literature with some new mechanisms designed specifically for E-VRPTW. To test the performance of the proposed ALNS we use the instances and benchmark results presented in by Schneider et al (2014). Our computational results show that the proposed method is effective in finding good solutions in reasonable amount of time

Efficient heuristic algorithms for location of charging stations in electric vehicle routing problems

Indexación: Scopus.This work has been partially supported by CONICYT FONDECYT by grant 11150370, FONDEF IT17M10012 and the “Grupo de Logística y Transporte” at the Universidad del Bío-Bío.. This support is gratefully acknowledged.Eco-responsible transportation contributes at making a difference for companies devoted to product delivery operations. Two specific problems related to operations are the location of charging stations and the routing of electric vehicles. The first one involves locating new facilities on potential sites to minimise an objective function related to fixed and operational opening costs. The other one, electric vehicle routing problem, involves the consolidation of an electric-type fleet in order to meet a particular demand and some guidelines to optimise costs. It is determined by the distance travelled, considering the limited autonomy of the fleet, and can be restored by recharging its battery. The literature provides several solutions for locating and routing problems and contemplates restrictions that are closer to reality. However, there is an evident lack of techniques that addresses both issues simultaneously. The present article offers four solution strategies for the location of charging stations and a heuristic solution for fleet routing. The best results were obtained by applying the location strategy at the site of the client (relaxation of the VRP) to address the routing problem, but it must be considered that there are no displacements towards the recharges. Of all the other three proposals, K-means showed the best performance when locating the charging stations at the centroid of the cluster. © 2012-2018. National Institute for R and D in Informatics.https://sic.ici.ro/wp-content/uploads/2018/03/Art.-8-Issue-1-2018-SIC.pd

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Electric vehicle routing problem with time dependent waiting times at recharging stations

In the Electric Vehicle Routing Problem with Time Windows (EVRPTW) the vehicles have a limited driving range and must recharge their battery at some points on their route. The recharging stations have a limited capacity and the newly arriving vehicles may have to queue before being recharged. In this study, we model the EVRPTW considering time-dependent queueing times at the stations. We allow but penalize late arrivals at customer locations and at the depot. We minimize the cost of vehicles, drivers, energy, and penalties for late arrivals. We formulate the problem as a mixed integer linear program and solve small instances with CPLEX. For the larger instances, we develop a matheuristic which is a combination of Adaptive Large Neighborhood Search and of the solution of a mixed integer linear program. We perform an extensive experimental study to investigate the impact of queueing at the recharging stations on the routing decisions. The results show that waiting at the stations may increase the total cost by 1%–26%, depending on the problem type and queue length. We also observe that recharges tend to shift to less crowded mid-day hours due to the time-dependent waiting times