Learning-Based Co-planning for Improved Container, Barge and Truck Routing

Rie B. Larsen; Bilge Atasoy; Rudy R. Negenborn

doi:10.1007/978-3-030-59747-4_31

Learning-Based Co-planning for Improved Container, Barge and Truck Routing

Rie B. Larsen^*, Bilge Atasoy, Rudy R. Negenborn

^*Corresponding author for this work

Transport Engineering and Logistics

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

47 Downloads (Pure)

Abstract

When barges are scheduled before the demand for container transport is known, the scheduled departures may match poorly with the realised demands’ due dates and with the truck utilization. Synchromodal transport enables simultaneous planning of container, truck and barge routes at the operational level. Often these decisions are taken by multiple stakeholders who wants cooperation, but are reluctant to share information. We propose a novel co-planning framework, called departure learning, where a barge operator learns what departure times perform better based on indications from the other operator. The framework is suitable for real time implementation and thus handles uncertainties by replanning. Simulated experiment results show that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.

Original language	English
Title of host publication	Computational Logistics
Subtitle of host publication	Proceedings of the 11th International Conference, ICCL 2020
Editors	Eduardo Lalla-Ruiz, Martijn Mes, Stefan Voß
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	476-491
ISBN (Electronic)	978-3-030-59747-4
ISBN (Print)	978-3-030-59746-7
DOIs	https://doi.org/10.1007/978-3-030-59747-4_31
Publication status	Published - 2020
Event	11th International Conference on Computational Logistics, ICCL 2020 - Enschede, Netherlands Duration: 28 Sept 2020 → 30 Sept 2020

Publication series

Name	Lecture Notes in Computer Science
Volume	12433
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Computational Logistics, ICCL 2020
Country/Territory	Netherlands
City	Enschede
Period	28/09/20 → 30/09/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Cooperative planning
Synchromodal transport
Vehicle utilization

UN SDGs

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

Access to Document

10.1007/978-3-030-59747-4_31

Larsen2020_Chapter_Learning-BasedCo-planningForImFinal published version, 1.53 MB

Cite this

Larsen, R. B., Atasoy, B., & Negenborn, R. R. (2020). Learning-Based Co-planning for Improved Container, Barge and Truck Routing. In E. Lalla-Ruiz, M. Mes, & S. Voß (Eds.), Computational Logistics : Proceedings of the 11th International Conference, ICCL 2020 (pp. 476-491). (Lecture Notes in Computer Science; Vol. 12433 ). Springer. https://doi.org/10.1007/978-3-030-59747-4_31

@inproceedings{233ddcd0b920461e9dec5273406a7f6e,

title = "Learning-Based Co-planning for Improved Container, Barge and Truck Routing",

abstract = "When barges are scheduled before the demand for container transport is known, the scheduled departures may match poorly with the realised demands{\textquoteright} due dates and with the truck utilization. Synchromodal transport enables simultaneous planning of container, truck and barge routes at the operational level. Often these decisions are taken by multiple stakeholders who wants cooperation, but are reluctant to share information. We propose a novel co-planning framework, called departure learning, where a barge operator learns what departure times perform better based on indications from the other operator. The framework is suitable for real time implementation and thus handles uncertainties by replanning. Simulated experiment results show that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.",

keywords = "Cooperative planning, Synchromodal transport, Vehicle utilization",

author = "Larsen, {Rie B.} and Bilge Atasoy and Negenborn, {Rudy R.}",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 11th International Conference on Computational Logistics, ICCL 2020 ; Conference date: 28-09-2020 Through 30-09-2020",

year = "2020",

doi = "10.1007/978-3-030-59747-4_31",

language = "English",

isbn = "978-3-030-59746-7",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "476--491",

editor = "Eduardo Lalla-Ruiz and Martijn Mes and Stefan Vo{\ss}",

booktitle = "Computational Logistics",

}

Larsen, RB , Atasoy, B & Negenborn, RR 2020, Learning-Based Co-planning for Improved Container, Barge and Truck Routing. in E Lalla-Ruiz, M Mes & S Voß (eds), Computational Logistics : Proceedings of the 11th International Conference, ICCL 2020. Lecture Notes in Computer Science, vol. 12433 , Springer, Cham, Switzerland, pp. 476-491, 11th International Conference on Computational Logistics, ICCL 2020, Enschede, Netherlands, 28/09/20. https://doi.org/10.1007/978-3-030-59747-4_31

Learning-Based Co-planning for Improved Container, Barge and Truck Routing. / Larsen, Rie B.; Atasoy, Bilge ; Negenborn, Rudy R.
Computational Logistics : Proceedings of the 11th International Conference, ICCL 2020. ed. / Eduardo Lalla-Ruiz; Martijn Mes; Stefan Voß. Cham, Switzerland: Springer, 2020. p. 476-491 (Lecture Notes in Computer Science; Vol. 12433 ).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Learning-Based Co-planning for Improved Container, Barge and Truck Routing

AU - Larsen, Rie B.

AU - Atasoy, Bilge

AU - Negenborn, Rudy R.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2020

Y1 - 2020

N2 - When barges are scheduled before the demand for container transport is known, the scheduled departures may match poorly with the realised demands’ due dates and with the truck utilization. Synchromodal transport enables simultaneous planning of container, truck and barge routes at the operational level. Often these decisions are taken by multiple stakeholders who wants cooperation, but are reluctant to share information. We propose a novel co-planning framework, called departure learning, where a barge operator learns what departure times perform better based on indications from the other operator. The framework is suitable for real time implementation and thus handles uncertainties by replanning. Simulated experiment results show that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.

AB - When barges are scheduled before the demand for container transport is known, the scheduled departures may match poorly with the realised demands’ due dates and with the truck utilization. Synchromodal transport enables simultaneous planning of container, truck and barge routes at the operational level. Often these decisions are taken by multiple stakeholders who wants cooperation, but are reluctant to share information. We propose a novel co-planning framework, called departure learning, where a barge operator learns what departure times perform better based on indications from the other operator. The framework is suitable for real time implementation and thus handles uncertainties by replanning. Simulated experiment results show that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.

KW - Cooperative planning

KW - Synchromodal transport

KW - Vehicle utilization

UR - http://www.scopus.com/inward/record.url?scp=85092220703&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-59747-4_31

DO - 10.1007/978-3-030-59747-4_31

M3 - Conference contribution

AN - SCOPUS:85092220703

SN - 978-3-030-59746-7

T3 - Lecture Notes in Computer Science

SP - 476

EP - 491

BT - Computational Logistics

A2 - Lalla-Ruiz, Eduardo

A2 - Mes, Martijn

A2 - Voß, Stefan

PB - Springer

CY - Cham, Switzerland

T2 - 11th International Conference on Computational Logistics, ICCL 2020

Y2 - 28 September 2020 through 30 September 2020

ER -

Learning-Based Co-planning for Improved Container, Barge and Truck Routing

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this