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Front. Eng    2021, Vol. 8 Issue (3) : 370-389    https://doi.org/10.1007/s42524-021-0157-1
REVIEW ARTICLE
A review on the electric vehicle routing problems: Variants and algorithms
Hu QIN1, Xinxin SU2, Teng REN3, Zhixing LUO4()
1. School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
2. School of Management, Huazhong University of Science and Technology, Wuhan 430074, China; Department of Management Sciences, City University of Hong Kong, Hong Kong, China
3. School of Transportation and Logistics, Central South University of Forestry and Technology, Changsha 410004, China
4. School of Management and Engineering, Nanjing University, Nanjing 210093, China
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Abstract

Over the past decade, electric vehicles (EVs) have been considered in a growing number of models and methods for vehicle routing problems (VRPs). This study presents a comprehensive survey of EV routing problems and their many variants. We only consider the problems in which each vehicle may visit multiple vertices and be recharged during the trip. The related literature can be roughly divided into nine classes: Electric traveling salesman problem, green VRP, electric VRP, mixed electric VRP, electric location routing problem, hybrid electric VRP, electric dial-a-ride problem, electric two-echelon VRP, and electric pickup and delivery problem. For each of these nine classes, we focus on reviewing the settings of problem variants and the algorithms used to obtain their solutions.
Keywords electric vehicles      routing      recharging stations      exact algorithms      metaheuristics     
Corresponding Author(s): Zhixing LUO   
Just Accepted Date: 29 March 2021   Online First Date: 18 May 2021    Issue Date: 13 July 2021
 Cite this article:   
Hu QIN,Xinxin SU,Teng REN, et al. A review on the electric vehicle routing problems: Variants and algorithms[J]. Front. Eng, 2021, 8(3): 370-389.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0157-1
https://academic.hep.com.cn/fem/EN/Y2021/V8/I3/370
Fig.1  An example solution of the VRP.
Fig.2  An example solution for EVRP.
Fig.3  The number of papers published in recent nine years.
Fig.4  The number of papers published in each journal.
Fig.5  Word cloud diagram of journal names.
Fig.6  EVRP classes surveyed in this study.
Fig.7  The ETSP and its variants.
References Approaches Results Advantages Disadvantages
Erdoğan and Miller- Hooks (2012) Constructive heuristics Tested on a set of 40 self-generated instances with 20 customers and 12 real cases with up to 500 customers and performed well Fast Low quality
Koç and Karaoglan (2016) Branch-and-cut algorithm Optimally solved 22 out of 40 small instances with 20 customers Optimal solution Slow
Montoya et al. (2016) Two-phase heuristic Yielded new best-known solutions for 8 instances, and achieved the best solutions for 40 instances Fast Near-optimal solution
Yavuz (2017) Iterated beam search algorithm Outperformed CPLEX and all previous heuristics on large instances Be able to either exactly or heuristically solve the problem –
Leggieri and Haouari (2017) MIP model-based reduction procedure Better than the branch-and-cut algorithm proposed by Koç and Karaoglan (2016) Optimal solution Slow
Andelmin and Bartolini (2017) Set-partitioning model based exact algorithm Optimally solved instances with up to 110 customers Optimal solution Slow
Affi et al. (2018) Variable neighborhood search heuristic Produced the best solution values for 11 out of 12 large instances and was superior to the other existing heuristics in the literature Fast Near-optimal solution
Andelmin and Bartolini (2019) Multi-start local search heuristic Found 8 new best-known solutions and reached the best-known solution for the rest of the instances Fast Near-optimal solution
Tab.1  Solution approaches for GVRP
Reference Problem name Features Approaches
Zhang et al. (2018a) Capacitated GVRP Zero recharging time Two-phase heuristic and metaheuristic based on ant colony system
Granada-Echeverri et al. (2020) EVRP with backhaul Two types of customers Iterated local search heuristic
Felipe et al. (2014) GVRP with multiple technologies and partial recharges Multiple recharging technologies and partial recharging Constructive heuristic, variable neighborhood search heuristic, and simulated annealing algorithm
Lin et al. (2016) General EVRP Both delivering or collecting products, and vehicle load MIP model
Shao et al. (2018) EVRP Fixed charging time Hybrid genetic algorithm
Zhang et al. (2018b) EVRP Full recharging policy, and zero recharging time Ant colony algorithm and adaptive large neighborhood search heuristic
Li et al. (2019) Multi-depot GVRP CO2 emissions Improved ant colony optimization algorithm
Pelletier et al. (2019) EVRP with energy consumption uncertainty Energy consumption uncertainty Cutting-plane algorithm and two-phase heuristic based on large neighborhood search
Schneider et al. (2014) EVRPTW with recharging stations Time windows Hybrid heuristic that combines variable neighborhood search algorithm and tabu search heuristic
Desaulniers et al. (2016) EVRPTW Time windows Branch-and-price-and-cut algorithm
Keskin and Çatay (2016) EVRPTW Time windows and partial recharging policy Adaptive large neighborhood search heuristic
Hiermann et al. (2016) Electric fleet size and mix VRP with time windows and recharging stations Time windows and heterogeneous EVs Branch-and-price algorithm and adaptive large neighborhood search heuristic
Zhao and Lu (2019) Real-world EVRP Heterogeneous EVs, full recharging policy, and constant recharging time Heuristic based on adaptive large neighborhood search heuristic and set-partitioning model
Yu et al. (2019b) Heterogeneous fleet GVRPTW Heterogeneous EVs, full recharging policy, and carbon emissions Branch-and-price algorithm
Wen et al. (2016) EV scheduling problem Full recharging policy, multiple depot, and variable recharging time Adaptive large neighborhood search heuristic
Wang et al. (2019) Multi-depot GVRP with shared transportation resource Multiple depot, shared transportation resources, time-dependent speed, and piecewise penalty costs for violating time windows Hybrid heuristic
Keskin and Çatay (2018) EVRP with time windows and fast chargers Partial recharging and multiple types of rechargers Two-phase matheuristic approach
Verma (2018) EVRP with time windows, recharging stations, and battery swapping stations Both chargers and batteries for swapping Two-step heuristic
Kancharla and Ramadurai (2018) EVRP with load-dependent energy consumption Load-dependent energy consumption Adaptive large neighborhood search heuristic
Cortés-Murcia et al. (2019) EVRP with time windows, partial recharges, and satellite customers Partial recharges and satellite customers Hybrid heuristic consisting of iterated local search, variable neighborhood decent, and set-partitioning model
Montoya et al. (2017) EVRP with nonlinear charging function Nonlinear charging function Hybrid metaheuristic that combines an iterated local search algorithm and a concentration heuristic
Froger et al. (2019) EVRP with nonlinear charging function Nonlinear charging function Heuristic and exact labeling algorithm
Zuo et al. (2019) EVRPTW with concave nonlinear charging function Nonlinear charging function No tailored solution procedure
Tab.2  EVRP and its variants
Fig.8  Piecewise linear approximation of nonlinear charging function.
Reference Problem name Features Approaches
Goeke and Schneider (2015) EVRP with time windows and mixed fleet Energy consumption functions Adaptive large neighborhood search heuristic
Hiermann et al. (2019) Hybrid heterogeneous electric fleet routing problem with time windows and recharging stations Time windows, conventional, plug-in hybrid, and electric vehicles Metaheuristic consisting of a genetic algorithm, a local search procedure and a large neighborhood search procedure
Macrina et al. (2019a) Green mixed fleet VRP with partial battery recharging and time windows Partial battery recharging and time windows Iterated local search heuristic
Macrina et al. (2019b) Energy-efficient GVRP with mixed vehicle fleet, partial battery recharging, and time windows Partial battery recharging, time windows, energy consumption function, effects of acceleration and braking phases, and battery lifespan Large neighborhood search heuristic
Tab.3  MEVRP and its variants
Reference Problem name Features Approaches
Yang and Sun (2015) Battery swap station location-routing problem with capacitated electric vehicles Locations of battery-swapping stations and EV routes Two-phase heuristic that combines tabu search algorithm and modified Clarke–Wright saving algorithm and SIGALNS
Hof et al. (2017) Battery swap station location-routing problem with capacitated electric vehicles Locations of battery-swapping stations and EV routes Adaptive variable neighborhood search heuristic
Schiffer and Walther (2017) ELRP problem with time windows and partial recharging Locations of battery-swapping stations, EV routes, time windows, and partial recharging MIP model without tailored methods
Zhang et al. (2019) Location-routing problem in EV transportation with stochastic demands Locations of battery-swapping stations, EV routes, and stochastic demands Hybrid heuristic composed of a binary PSO algorithm and a variable neighborhood search heuristic
Koç et al. (2019) EVRP with shared charging stations Locations of battery-swapping stations, EV routes, multiple depots, and investment in charging stations Multi-start adaptive large neighborhood search heuristic
Tab.4  ELRP and its variants
Fig.9  Comparison between allowing and not allowing revisit in ELRP.
Fig.10  Example of E2EVRP transportation network.
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