Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions
Given the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the obj...
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2021-01-01
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Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2021/8817991 |
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doaj-a9df196c0b1e4369b8755722f11f272c2021-04-05T00:00:52ZengHindawi-WileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/8817991Optimization of Urban Cold Chain Transport Routes under Time-Varying Network ConditionsShouchen Liu0Cheng Zhang1School of Transportation and LogisticsSchool of Transportation and LogisticsGiven the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the objective function as the minimum total cost is constructed under the premise of service reliability, and an artificial immune particle swarm optimization algorithm is designed to solve the model. For an empirical analysis of Xiamen’s cold chain transportation, a two-stage solution involving “static optimization and dynamic optimization” is used to verify the effectiveness of the model and the practical value of this research. Results show that the time-varying model can effectively reflect the situation of urban road transportation and satisfy the timeliness requirement of urban cold chain transportation.http://dx.doi.org/10.1155/2021/8817991 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shouchen Liu Cheng Zhang |
spellingShingle |
Shouchen Liu Cheng Zhang Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions Journal of Advanced Transportation |
author_facet |
Shouchen Liu Cheng Zhang |
author_sort |
Shouchen Liu |
title |
Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions |
title_short |
Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions |
title_full |
Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions |
title_fullStr |
Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions |
title_full_unstemmed |
Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions |
title_sort |
optimization of urban cold chain transport routes under time-varying network conditions |
publisher |
Hindawi-Wiley |
series |
Journal of Advanced Transportation |
issn |
2042-3195 |
publishDate |
2021-01-01 |
description |
Given the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the objective function as the minimum total cost is constructed under the premise of service reliability, and an artificial immune particle swarm optimization algorithm is designed to solve the model. For an empirical analysis of Xiamen’s cold chain transportation, a two-stage solution involving “static optimization and dynamic optimization” is used to verify the effectiveness of the model and the practical value of this research. Results show that the time-varying model can effectively reflect the situation of urban road transportation and satisfy the timeliness requirement of urban cold chain transportation. |
url |
http://dx.doi.org/10.1155/2021/8817991 |
work_keys_str_mv |
AT shouchenliu optimizationofurbancoldchaintransportroutesundertimevaryingnetworkconditions AT chengzhang optimizationofurbancoldchaintransportroutesundertimevaryingnetworkconditions |
_version_ |
1714694354479611904 |