Electric vehicle routing problem with recharging stations for minimizing energy consumption

Zhang, Shuai; Gajpal, Yuvraj; Appadoo, S. S.; Abdulkader, M.M.S.

doi:10.1016/j.ijpe.2018.07.016

Cited by 172 publications

(85 citation statements)

References 40 publications

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“…Objective functions can be complex with simultaneous minimization of vehicle number, total traveled distance [28], total travel times [29], total routing cost and planning horizon [11,27,30,31], GHG emission [32,33], energy consumption [34][35][36], etc. Total routing costs of BEVs usually consist of BEV acquisition cost, circulation tax, maintenance, costs related to the energy consumption (electric energy price), cost of battery pack renewal after its lifetime, labor costs, etc.…”

Section: Electric Vehicle Routing Problemmentioning

confidence: 99%

“…The authors concluded that using the realistic model is more efficient with 0.14% difference between realistic and constant energy consumption from the best-known solutions (BKS). To emphasize the importance of the energy consumption model and energy minimization, Zhang et al [36] compared the distance and energy minimization and concluded that the distance-minimizing objective consumes 16.44% more energy than the energyminimizing objective.…”

Section: Energy Consumptionmentioning

confidence: 99%

“…In most feasibility ensured cases when the energy constraint is violated, a CS is added to the route to make it energy feasible, and when the other constraints are violated such as vehicle load capacity or time windows, the new route is opened, and the procedure is repeated. Examples of adaptations to the E-VRP are push forward insertion heuristics (PIH) [41,145], constructive-k-PseudoGreedy [46], [28,30,31,33,36] modified insertion heuristics, and charging routing heuristic (CRH) [47][48][49], in which first the charging scheme at the depot is determined, and then the least-cost feasible customer insertions are performed.…”

Section: Routing Policiesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

163

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In order to ensure high-quality and on-time delivery in logistic distribution processes, it is necessary to efficiently manage the delivery fleet. Nowadays, due to the new policies and regulations related to greenhouse gas emission in the transport sector, logistic companies are paying higher penalties for each emission gram of CO2/km. With electric vehicle market penetration, many companies are evaluating the integration of electric vehicles in their fleet, as they do not have local greenhouse gas emissions, produce minimal noise, and are independent of the fluctuating oil price. The well-researched vehicle routing problem (VRP) is extended to the electric vehicle routing problem (E-VRP), which takes into account specific characteristics of electric vehicles. In this paper, a literature review on recent developments regarding the E-VRP is presented. The challenges that emerged with the integration of electric vehicles in the delivery processes are described, together with electric vehicle characteristics and recent energy consumption models. Several variants of the E-VRP and related problems are observed. To cope with the new routing challenges in E-VRP, efficient VRP heuristics and metaheuristics had to be adapted. An overview of the state-of-the-art procedures for solving the E-VRP and related problems is presented.

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Section: Electric Vehicle Routing Problemmentioning

confidence: 99%

Section: Energy Consumptionmentioning

confidence: 99%

Section: Routing Policiesmentioning

confidence: 99%

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A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

163

View full text Add to dashboard Cite

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“…Hof et al [20] designed an adaptive VNS algorithm to solve the battery swap station location-routing problem proposed by Yang and Sun [14]. Zhang et al [21] devised an ant colony algorithm to solve the EVRP with recharging stations to minimize energy consumption. Macrina et al [22] proposed an ILS heuristic to the mixed fleet vehicle routing problem with partial battery recharging and time windows in which the fleet is composed of electric and conventional vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

A Heuristic Approach for a Real-World Electric Vehicle Routing Problem

Zhao

2019

Algorithms

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To develop a non-polluting and sustainable city, urban administrators encourage logistics companies to use electric vehicles instead of conventional (i.e., fuel-based) vehicles for transportation services. However, electric energy-based limitations pose a new challenge in designing reasonable visiting routes that are essential for the daily operations of companies. Therefore, this paper investigates a real-world electric vehicle routing problem (VRP) raised by a logistics company. The problem combines the features of the capacitated VRP, the VRP with time windows, the heterogeneous fleet VRP, the multi-trip VRP, and the electric VRP with charging stations. To solve such a complicated problem, a heuristic approach based on the adaptive large neighborhood search (ALNS) and integer programming is proposed in this paper. Specifically, a charging station adjustment heuristic and a departure time adjustment heuristic are devised to decrease the total operational cost. Furthermore, the best solution obtained by the ALNS is improved by integer programming. Twenty instances generated from real-world data were used to validate the effectiveness of the proposed algorithm. The results demonstrate that using our algorithm can save 7.52% of operational cost.

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“…Zhang et al [37] considered an Electric Vehicle-Routing Problem (EVRP) to minimize the energy consumption of electric vehicles. The corresponding mathematical model is formulated.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Objective Vehicle-Routing Problem with Soft Time Windows and Multiple Depots to Minimize the Total Energy Consumption and Customer Dissatisfaction

Wang

Liu

et al. 2018

Sustainability

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In recent years, the impact of the energy crisis and environment pollution on quality of life has forced industry to actively participate in the development of a sustainable society. Simultaneously, customer satisfaction improvement has always been a goal of businesses. It is recognized that efficient technologies and advanced methods can help transportation companies find a better balance between progress in energy saving and customer satisfaction. This paper investigates a bi-objective vehicle-routing problem with soft time windows and multiple depots, which aims to simultaneously minimize total energy consumption and customer dissatisfaction. To address the problem, we first develop mixed-integer programming. Then, an augmented ϵ -constraint method is adopted to obtain the optimal Pareto front for small problems. It is very time consuming for the augmented ϵ -constraint method to precisely solve even medium-sized problems. For medium- and large-sized problems, two Non-dominated Sorting Genetic Algorithm-II (NSGA-II)-based heuristics with different rules for generating initial solutions and offspring are designed. The performance of the proposed methods is evaluated by 100 randomly generated instances. Computational results show that the second NSGA-II-based heuristic is highly effective in finding approximate non-dominated solutions for small-size and medium-size instances, and the first one is performs better for the large-size instances.

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Electric vehicle routing problem with recharging stations for minimizing energy consumption

Cited by 172 publications

References 40 publications

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

A Heuristic Approach for a Real-World Electric Vehicle Routing Problem

A Bi-Objective Vehicle-Routing Problem with Soft Time Windows and Multiple Depots to Minimize the Total Energy Consumption and Customer Dissatisfaction

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