A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs

Under the background of the normalization of COVID-19 prevention and control and the rapid development of e-commerce, community group buying has occupied the market by providing low-priced, fast, and green consumer goods, but with it, the logistics and distribution volume of goods has also increased...

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Main Authors: Zhiqiang Liu, Yanqi Niu, Caiyun Guo, Shitong Jia
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/16/2/931
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author Zhiqiang Liu
Yanqi Niu
Caiyun Guo
Shitong Jia
author_facet Zhiqiang Liu
Yanqi Niu
Caiyun Guo
Shitong Jia
author_sort Zhiqiang Liu
collection DOAJ
description Under the background of the normalization of COVID-19 prevention and control and the rapid development of e-commerce, community group buying has occupied the market by providing low-priced, fast, and green consumer goods, but with it, the logistics and distribution volume of goods has also increased sharply. In order to reduce environmental pollution and the carbon emissions caused by transportation in the community group buying logistics distribution, it is necessary to investigate a suitable method to optimize vehicle distribution routes and reduce carbon emissions. Taking the lowest total costs of logistics and distribution and the smallest carbon emissions, this article introduces soft time window function and carbon emissions parameters, takes the delivery of goods from the community group buying distribution center in Wu’an Town, Hebei Province to customer points in 14 townships as an example, an optimization model for the distribution route of low carbon vehicles for community group buying based on improved genetic algorithm was constructed, AHP-EW fusion technology was used to calculate carbon emissions and cost weights, and compared with the traditional genetic algorithm and ant colony algorithm two typical heuristic algorithms, the feasibility of the proposed model and the advantages of the improved algorithm are verified, and the research results showed that it can reduce the costs and carbon emissions of vehicle distribution, provide decision-making reference for community group buying logistics enterprise distribution, and promote energy conservation and environmental sustainable development.
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spelling doaj.art-de60960527ac4c808878c32dc03be0dc2023-11-30T22:06:07ZengMDPI AGEnergies1996-10732023-01-0116293110.3390/en16020931A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution CostsZhiqiang Liu0Yanqi Niu1Caiyun Guo2Shitong Jia3School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, ChinaSchool of Management Engineering and Business, Hebei University of Engineering, Handan 056038, ChinaSchool of Management Engineering and Business, Hebei University of Engineering, Handan 056038, ChinaSchool of Management Engineering and Business, Hebei University of Engineering, Handan 056038, ChinaUnder the background of the normalization of COVID-19 prevention and control and the rapid development of e-commerce, community group buying has occupied the market by providing low-priced, fast, and green consumer goods, but with it, the logistics and distribution volume of goods has also increased sharply. In order to reduce environmental pollution and the carbon emissions caused by transportation in the community group buying logistics distribution, it is necessary to investigate a suitable method to optimize vehicle distribution routes and reduce carbon emissions. Taking the lowest total costs of logistics and distribution and the smallest carbon emissions, this article introduces soft time window function and carbon emissions parameters, takes the delivery of goods from the community group buying distribution center in Wu’an Town, Hebei Province to customer points in 14 townships as an example, an optimization model for the distribution route of low carbon vehicles for community group buying based on improved genetic algorithm was constructed, AHP-EW fusion technology was used to calculate carbon emissions and cost weights, and compared with the traditional genetic algorithm and ant colony algorithm two typical heuristic algorithms, the feasibility of the proposed model and the advantages of the improved algorithm are verified, and the research results showed that it can reduce the costs and carbon emissions of vehicle distribution, provide decision-making reference for community group buying logistics enterprise distribution, and promote energy conservation and environmental sustainable development.https://www.mdpi.com/1996-1073/16/2/931community group buyingvehicle routing optimizationcarbon emissionsgenetic algorithmsustainable development
spellingShingle Zhiqiang Liu
Yanqi Niu
Caiyun Guo
Shitong Jia
A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs
Energies
community group buying
vehicle routing optimization
carbon emissions
genetic algorithm
sustainable development
title A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs
title_full A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs
title_fullStr A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs
title_full_unstemmed A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs
title_short A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs
title_sort vehicle routing optimization model for community group buying considering carbon emissions and total distribution costs
topic community group buying
vehicle routing optimization
carbon emissions
genetic algorithm
sustainable development
url https://www.mdpi.com/1996-1073/16/2/931
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