Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading

Hao Qiang, Rui Ou, Yanchun Hu, Zhenyu Wu and Xiaohua Zhang ()
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Hao Qiang: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Rui Ou: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Yanchun Hu: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Zhenyu Wu: Changzhou Xingyu Automotive Lighting Systems Co., Ltd., Changzhou 213022, China
Xiaohua Zhang: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China

Sustainability, 2023, vol. 15, issue 22, 1-16

Abstract: As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution.

Keywords: green vehicle routing problem; carbon emission; green power trading; nearest-neighbor algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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