A greedy search based evolutionary algorithm for electric vehicle routing problem

Hien, Vu Quoc; Dao, Tran Cong; Bình, Huỳnh Thị Thanh

doi:10.1007/s10489-022-03555-8

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…(8) The demand at each customer node must be met. (9) The vehicle runs at a constant speed. (10) The traffic resources on the road are not considered.…”

Section: Mathematical Modelmentioning

confidence: 99%

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A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

Xiao-Yun Jiang,

Wen-Chao Chen,

Yu-Tong Liu

2023

Int. j. eng. technol. innov.

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The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.

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“…(8) The demand at each customer node must be met. (9) The vehicle runs at a constant speed. (10) The traffic resources on the road are not considered.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Yao et al [8] extended the original VRPTW by introducing a high-dimensional spatio-temporal network flow model to enhance the realism of the model. Hien et al [9] proposed a genetic algorithm inspired by the greedy search for the largescale electric vehicle routing problem.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

Xiao-Yun Jiang,

Wen-Chao Chen,

Yu-Tong Liu

2023

Int. j. eng. technol. innov.

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Evolving routing policies for electric vehicles by means of genetic programming

Gil-Gala,

Đurasević,

Jakobović

2024

Appl Intell

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In recent years, the growing interest in environmental sustainability has led to Electric Vehicle Routing Problems (EVRPs) attracting more and more attention. EVRPs involve the use of electric vehicles, which have additional constraints, such as range and recharging time, compared to conventional Vehicle Routing Problems (VRPs). The complexity and dynamic nature of solving VRPs often lead to the introduction of Routing Policies (RPs), simple heuristics that incrementally build routes. However, manually designing efficient RPs proves to be a challenging and time-consuming task. Therefore, there is a pressing need to explore the application of hyper-heuristics, in particular Genetic Programming (GP), to automatically generate new RPs. Since this method has not yet been investigated in the literature in the context of EVRPs, this study explores the applicability of GP to automatically generate new RPs for EVRP. To this end, three RP variants (serial, semiparallel, and parallel) are introduced in this study, along with a set of domain-specific terminal nodes to optimise three criteria: the number of vehicles, energy consumption, and total tardiness. The experimental analysis shows that the serial variant performs best in terms of energy consumption and number of vehicles, while the parallel variant is most effective in minimising the total tardiness. A comprehensive analysis of the proposed method is conducted to determine its convergence properties and the impact of the proposed terminal nodes on performance and to describe several generated RPs. The results show that the automatically generated RPs perform commendably compared to traditional methods such as metaheuristics and exact methods, which usually require significantly more runtime. More specifically, depending on the scenario in which they are used, the generated RPs achieve results that are about 20%-37% worse compared to the best known results for the number of vehicles in almost negligible time, in just some milliseconds.

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Node depth Representation-based Evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks

Dao,

Tam,

Binh

2024

Engineering Applications of Artificial Intelligence

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A greedy search based evolutionary algorithm for electric vehicle routing problem

Cited by 7 publications

References 15 publications

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

Evolving routing policies for electric vehicles by means of genetic programming

Node depth Representation-based Evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks

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