A matheuristic method for the electric vehicle routing problem with time windows and fast chargers

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is an extension of the well-known VRPTW where electric vehicles (EVs) are used instead of internal combustion engine vehicles. An EV has a limited driving range due to its battery capacity and may need recharging to complete its route. Recharging can be made at any battery level and may be at any quantity up to the battery capacity. Furthermore, the stations may be equipped with chargers with different power supply, power voltage, maximum current options which affect the recharge duration. In this study, we model the EVRPTW by allowing partial recharges with three recharging configurations which can be referred to as normal, fast and super-fast recharges. In faster options, the battery is charged with the same energy in a shorter time but at a higher cost. Our objective is to minimize the total recharging cost while operating minimum number of vehicles. We formulated this problem as a mixed integer linear program and solved the small instances using CPLEX. To solve the larger problems, we develop a matheuristic approach which couples the Adaptive Large Neighborhood Search (ALNS) approach with an exact method. Our ALNS is equipped with various destroy-repair algorithms to efficiently explore the neighborhoods and uses CPLEX to strengthen the routes obtained. We carried out extensive experiments to investigate the benefits of fast recharges and test the performance of our algorithm using benchmark instances from the literature. The results show the effectiveness of the proposed matheuristic and demonstrate the benefits of fast chargers on the fleet size and energy costs

Recharge strategies for the electric vehicle routing problem with time windows in deterministic and stochastic environments

Due to increasing concerns about greenhouse gas emissions in recent years, many companies have had an interest in using alternative fuel vehicles in their fleets. Electric vehicles (EVs) are one of these vehicles and they have various advantages such as zero tailpipe emissions, low maintenance costs and low energy consumption. However, their acquisition costs are higher compared to the conventional vehicles and recharging the battery may take significant amount of time compared to the short fueling times. Hence, to overcome these challenges, logistics decisions have to be made effectively. The problem of planning EVs’ activities has been introduced to the literature as the Electric Vehicle Routing Problem (EVRP), which is a special case of the classical VRP where the fleet consists of EVs. The difference between this problem and the classical VRP is that vehicles have batteries as the energy source and the battery is being discharged while the EV is traveling. Hence, the EVs may recharge their batteries at the recharging stations to continue their routes. These stations are located at distant locations and there are few of them compared to the common fuel stations. Recharging may be performed at any level of the battery and the recharging time increases with the recharge amount. In some stations, there may be different chargers which vary in terms of charging speed. For instance, fast chargers recharge the battery faster, but they incur higher cost. Furthermore, EVs may wait in the queue at the stations since there may be other EVs which arrive earlier and wait for ii service. In this dissertation, we address four problems which consider these different features of the EVRP. First, we study the EVRP with Time Windows where the batteries can be recharged partially at the recharging stations. Second, we extend this problem where the recharging stations are equipped with multiple types of chargers which differ by recharging rates and unit recharging costs. Next, we consider a stochastic environment where an EV may wait in the queue before recharging due to other EVs that have arrived earlier at that station. The waiting times depend on the time of the visit during the day, i.e., they are longer in the rush hours. Furthermore, the recharging time is assumed to be a nonlinear function of the energy recharged. In the final problem, we consider random waiting times at the recharging stations. In this case, the EVs do not have information about the queue lengths of the stations before they arrive at. We propose Adaptive Large Neighborhood Search heuristics and matheuristics to solve these problems effectivel

Vehicle routing and location routing with intermediate stops:A review

This paper reviews the literature on vehicle routing problems and location rout-8 ing problems with intermediate stops. We classify publications into different categories from both an application-based perspective and a methodological perspective. In addition, we analyze the papers with respect to the algorithms and benchmark instances they present. Furthermore, we provide an overview of trends in the literature and identify promising areas for further research.</p

Vehicle routing and location routing with intermediate stops:A review

This paper reviews the literature on vehicle routing problems and location rout-8 ing problems with intermediate stops. We classify publications into different categories from both an application-based perspective and a methodological perspective. In addition, we analyze the papers with respect to the algorithms and benchmark instances they present. Furthermore, we provide an overview of trends in the literature and identify promising areas for further research.</p

A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs

Electric vehicle routing problem with flexible deliveries

Growing concerns about the climate change have forced governments to initiate tighter environmental regulations and tougher emission reduction targets, increasing the interest on electromobility. Logistics operators started employing electric vehicles (EVs) and must face new operational planning challenges. Moreover, with an ever-growing interest in e-commerce, parcel delivery is taking new shapes by offering flexible delivery options to the customers. To mitigate these issues, we introduce the Electric Vehicle Routing Problem with Flexible Deliveries (EVRP-FD), where the customers are served using a fleet of EVs that can recharge their batteries along their routes. In this problem, a customer may specify different delivery locations for different time windows. Our objective is to serve the customers while minimising the total travelled distance using minimum number of vehicles. We first give the mathematical model and then develop a hybrid Variable Neighbourhood Search coupled with Tabu Search by proposing new mechanisms to solve the problem effectively. Then, we verify the performance of our algorithm on instances from the literature. We also introduce new instances for the EVRP-FD and perform an extensive computational study to investigate the trade-offs associated with different operational factors. Finally, we present a case study in Nottingham, UK to provide further insights

Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation

[EN] The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Do C. Martins, L.; Tordecilla, RD.; Castaneda, J.; Juan-Pérez, ÁA.; Faulin, J. (2021). Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation. Energies. 14(16):1-30. https://doi.org/10.3390/en14165131130141