Green vehicle routing using mixed fleets for cold chain distribution

Wanru Chen; Dehzi Zhang; Tom van Woensel; Guangming Xu; Jing Guo

doi:10.1016/j.eswa.2023.120979

Green vehicle routing using mixed fleets for cold chain distribution

Wanru Chen, Dehzi Zhang (Corresponding author), Tom van Woensel, Guangming Xu, Jing Guo

Research output: Contribution to journal › Article › Academic › peer-review

18 Citations (Scopus)

83 Downloads (Pure)

Abstract

Introducing clean energy into urban cold chain distribution to achieve a green transition is crucial for reducing transportation and logistics emissions and promoting sustainable urban development. This study focuses on the Cold Chain Green Multi-Depot Vehicle Routing Problem with Time Windows and Mixed Fleets (CC-GMD-VRPTW-MF) for city logistics distribution, utilizing both electric vehicles (EVs) and gasoline and diesel vehicles (GDVs). To accurately assess energy consumption, a realistic energy consumption model is employed. The CC-GMD-VRPTW-MF aims to minimize total costs (including fixed, energy consumption, damage, and environmental costs). To address the problem, we propose an improved Variable Neighborhood Search (VNS) algorithm that introduces a new balanced mechanism for perturbation and a new memory-based mechanism for local search to enhance computational performance. Numerical studies on newly designed CC-GMD-VRPTW-MF instances are conducted to investigate the effect of incorporating EVs for joint delivery, considering different carbon prices, and adjusting time windows. Furthermore, we evaluate the effectiveness of the proposed improvement strategies in VNS and demonstrate the algorithm’s performance on benchmarks of related problems.

Original language	English
Article number	120979
Number of pages	15
Journal	Expert Systems with Applications
Volume	233
DOIs	https://doi.org/10.1016/j.eswa.2023.120979
Publication status	Published - 15 Dec 2023

Funding

The work is supported by the China Scholarship Council (Grant No. 202206370132 ) and the High-end Think Tank Project of Central South University, China (Grant No. 2021znzk08 ). The work is supported in part by the Natural Science Foundation of Hunan Province of China (Grant No. 2021JJ30857 , and No. 2021JJ31167 ), in part by the Hunan Social Science Foundation, China (Grant No. 19YBA378 ).

Funders	Funder number
Central South University	2021znzk08
China Scholarship Council	202206370132

Keywords

Cold chain distribution
Electric vehicles
Mixed fleet
Multi-depot Vehicle Routing Problem
Variable Neighborhood Search

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.eswa.2023.120979

pdfFinal published version, 1.08 MBLicence: TAVERNE

Cite this

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title = "Green vehicle routing using mixed fleets for cold chain distribution",

abstract = "Introducing clean energy into urban cold chain distribution to achieve a green transition is crucial for reducing transportation and logistics emissions and promoting sustainable urban development. This study focuses on the Cold Chain Green Multi-Depot Vehicle Routing Problem with Time Windows and Mixed Fleets (CC-GMD-VRPTW-MF) for city logistics distribution, utilizing both electric vehicles (EVs) and gasoline and diesel vehicles (GDVs). To accurately assess energy consumption, a realistic energy consumption model is employed. The CC-GMD-VRPTW-MF aims to minimize total costs (including fixed, energy consumption, damage, and environmental costs). To address the problem, we propose an improved Variable Neighborhood Search (VNS) algorithm that introduces a new balanced mechanism for perturbation and a new memory-based mechanism for local search to enhance computational performance. Numerical studies on newly designed CC-GMD-VRPTW-MF instances are conducted to investigate the effect of incorporating EVs for joint delivery, considering different carbon prices, and adjusting time windows. Furthermore, we evaluate the effectiveness of the proposed improvement strategies in VNS and demonstrate the algorithm{\textquoteright}s performance on benchmarks of related problems.",

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author = "Wanru Chen and Dehzi Zhang and {van Woensel}, Tom and Guangming Xu and Jing Guo",

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AU - Chen, Wanru

AU - Zhang, Dehzi

AU - van Woensel, Tom

AU - Xu, Guangming

AU - Guo, Jing

PY - 2023/12/15

Y1 - 2023/12/15

N2 - Introducing clean energy into urban cold chain distribution to achieve a green transition is crucial for reducing transportation and logistics emissions and promoting sustainable urban development. This study focuses on the Cold Chain Green Multi-Depot Vehicle Routing Problem with Time Windows and Mixed Fleets (CC-GMD-VRPTW-MF) for city logistics distribution, utilizing both electric vehicles (EVs) and gasoline and diesel vehicles (GDVs). To accurately assess energy consumption, a realistic energy consumption model is employed. The CC-GMD-VRPTW-MF aims to minimize total costs (including fixed, energy consumption, damage, and environmental costs). To address the problem, we propose an improved Variable Neighborhood Search (VNS) algorithm that introduces a new balanced mechanism for perturbation and a new memory-based mechanism for local search to enhance computational performance. Numerical studies on newly designed CC-GMD-VRPTW-MF instances are conducted to investigate the effect of incorporating EVs for joint delivery, considering different carbon prices, and adjusting time windows. Furthermore, we evaluate the effectiveness of the proposed improvement strategies in VNS and demonstrate the algorithm’s performance on benchmarks of related problems.

AB - Introducing clean energy into urban cold chain distribution to achieve a green transition is crucial for reducing transportation and logistics emissions and promoting sustainable urban development. This study focuses on the Cold Chain Green Multi-Depot Vehicle Routing Problem with Time Windows and Mixed Fleets (CC-GMD-VRPTW-MF) for city logistics distribution, utilizing both electric vehicles (EVs) and gasoline and diesel vehicles (GDVs). To accurately assess energy consumption, a realistic energy consumption model is employed. The CC-GMD-VRPTW-MF aims to minimize total costs (including fixed, energy consumption, damage, and environmental costs). To address the problem, we propose an improved Variable Neighborhood Search (VNS) algorithm that introduces a new balanced mechanism for perturbation and a new memory-based mechanism for local search to enhance computational performance. Numerical studies on newly designed CC-GMD-VRPTW-MF instances are conducted to investigate the effect of incorporating EVs for joint delivery, considering different carbon prices, and adjusting time windows. Furthermore, we evaluate the effectiveness of the proposed improvement strategies in VNS and demonstrate the algorithm’s performance on benchmarks of related problems.

KW - Cold chain distribution

KW - Electric vehicles

KW - Mixed fleet

KW - Multi-depot Vehicle Routing Problem

KW - Variable Neighborhood Search

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Green vehicle routing using mixed fleets for cold chain distribution

Abstract

Funding

Keywords

UN SDGs

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