Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem

Lijuan Zhuge, Lu Carol Tong, Hailong Wu, Yiheng Chen, Xuesong Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver's routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1355-1360
Number of pages6
ISBN (Electronic)9781538670248
DOIs
StatePublished - Oct 2019
Event2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand
Duration: Oct 27 2019Oct 30 2019

Publication series

Name2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
CountryNew Zealand
CityAuckland
Period10/27/1910/30/19

Fingerprint

Vehicle routing
schedules
delivery
vehicles
demand analysis
Logistics
commodities
distributing
optimization model
multiplier
logistics
path analysis
multipliers
Acoustic waves
commodity
driver
routes
costs
deviation
Costs

ASJC Scopus subject areas

  • Artificial Intelligence
  • Management Science and Operations Research
  • Instrumentation
  • Transportation

Cite this

Zhuge, L., Tong, L. C., Wu, H., Chen, Y., & Zhou, X. (2019). Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 1355-1360). [8916849] (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2019.8916849

Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem. / Zhuge, Lijuan; Tong, Lu Carol; Wu, Hailong; Chen, Yiheng; Zhou, Xuesong.

2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1355-1360 8916849 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhuge, L, Tong, LC, Wu, H, Chen, Y & Zhou, X 2019, Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 8916849, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1355-1360, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Auckland, New Zealand, 10/27/19. https://doi.org/10.1109/ITSC.2019.8916849
Zhuge L, Tong LC, Wu H, Chen Y, Zhou X. Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1355-1360. 8916849. (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). https://doi.org/10.1109/ITSC.2019.8916849
Zhuge, Lijuan ; Tong, Lu Carol ; Wu, Hailong ; Chen, Yiheng ; Zhou, Xuesong. / Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1355-1360 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).
@inproceedings{99d3a71114444f96964d1dba943c4e3d,
title = "Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem",
abstract = "Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver's routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method.",
author = "Lijuan Zhuge and Tong, {Lu Carol} and Hailong Wu and Yiheng Chen and Xuesong Zhou",
year = "2019",
month = "10",
doi = "10.1109/ITSC.2019.8916849",
language = "English (US)",
series = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1355--1360",
booktitle = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

}

TY - GEN

T1 - Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem

AU - Zhuge, Lijuan

AU - Tong, Lu Carol

AU - Wu, Hailong

AU - Chen, Yiheng

AU - Zhou, Xuesong

PY - 2019/10

Y1 - 2019/10

N2 - Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver's routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method.

AB - Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver's routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=85076809491&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85076809491&partnerID=8YFLogxK

U2 - 10.1109/ITSC.2019.8916849

DO - 10.1109/ITSC.2019.8916849

M3 - Conference contribution

AN - SCOPUS:85076809491

T3 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

SP - 1355

EP - 1360

BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -