Dealing with demand uncertainty in service network and load plan design

Ahmad Baubaid; Natashia Boland; Martin Savelsbergh

doi:10.1007/978-3-319-93031-2_5

Dealing with demand uncertainty in service network and load plan design

Ahmad Baubaid^*, Natashia Boland, Martin Savelsbergh

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Less-than-Truckload (LTL) transportation carriers plan for their next operating season by deciding: (1) a load plan, which specifies how shipments are routed through the terminal network from origins to destinations, and (2) how many trailers to operate between each pair of terminals in the network. Most carriers also require that the load plan is such that shipments at an intermediate terminal and having the same ultimate destination are loaded onto trailers headed to a unique next terminal regardless of their origins. In practice, daily variations in demand are handled by relaxing this requirement and possibly loading shipments to an alternative next terminal. We introduce the p-alt model, which integrates routing and capacity decisions, and which allows p choices for the next terminal for shipments with a particular ultimate destination. We further introduce and computationally test three solution methods for the stochastic p-alt model, which shows that much can be gained from using the p-alt model and explicitly considering demand uncertainty.

Original language	English
Title of host publication	Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings
Editors	Willem -Jan van Hoeve
Publisher	Springer Verlag
Pages	63-71
Number of pages	9
ISBN (Print)	9783319930305
DOIs	https://doi.org/10.1007/978-3-319-93031-2_5
State	Published - 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10848 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-93031-2_5

Cite this

Baubaid, A., Boland, N., & Savelsbergh, M. (2018). Dealing with demand uncertainty in service network and load plan design. In W. .-J. van Hoeve (Ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings (pp. 63-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10848 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93031-2_5

Baubaid, Ahmad ; Boland, Natashia ; Savelsbergh, Martin. / Dealing with demand uncertainty in service network and load plan design. Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings. editor / Willem -Jan van Hoeve. Springer Verlag, 2018. pp. 63-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Baubaid, A, Boland, N & Savelsbergh, M 2018, Dealing with demand uncertainty in service network and load plan design. in W-J van Hoeve (ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10848 LNCS, Springer Verlag, pp. 63-71. https://doi.org/10.1007/978-3-319-93031-2_5

Dealing with demand uncertainty in service network and load plan design. / Baubaid, Ahmad; Boland, Natashia; Savelsbergh, Martin.
Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings. ed. / Willem -Jan van Hoeve. Springer Verlag, 2018. p. 63-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10848 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Savelsbergh, Martin

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AB - Less-than-Truckload (LTL) transportation carriers plan for their next operating season by deciding: (1) a load plan, which specifies how shipments are routed through the terminal network from origins to destinations, and (2) how many trailers to operate between each pair of terminals in the network. Most carriers also require that the load plan is such that shipments at an intermediate terminal and having the same ultimate destination are loaded onto trailers headed to a unique next terminal regardless of their origins. In practice, daily variations in demand are handled by relaxing this requirement and possibly loading shipments to an alternative next terminal. We introduce the p-alt model, which integrates routing and capacity decisions, and which allows p choices for the next terminal for shipments with a particular ultimate destination. We further introduce and computationally test three solution methods for the stochastic p-alt model, which shows that much can be gained from using the p-alt model and explicitly considering demand uncertainty.

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Baubaid A, Boland N, Savelsbergh M. Dealing with demand uncertainty in service network and load plan design. In van Hoeve WJ, editor, Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings. Springer Verlag. 2018. p. 63-71. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93031-2_5

Dealing with demand uncertainty in service network and load plan design

Abstract

Publication series

Bibliographical note

ASJC Scopus subject areas

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Cite this