A Decomposed Fourier-Motzkin Elimination Framework to Derive Vessel Capacity Models

Mai Lise Ajspur; Rune Møller Jensen; Kent Høj Andersen

doi:10.1007/978-3-030-31140-7_6

A Decomposed Fourier-Motzkin Elimination Framework to Derive Vessel Capacity Models

Mai Lise Ajspur, Rune Møller Jensen, Kent Høj Andersen

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

Accurate Vessel Capacity Models (VCMs) expressing the
trade-off between different container types that can be stowed on container
vessels are required in core liner shipping functions such as uptake-,
capacity-, and network management. Today, simple models based on volume,
weight, and refrigerated container capacity are used for these tasks,
which causes overestimations that hamper decision making. Though previous
work on stowage planning optimization in principle provide finegrained
linear Vessel Stowage Models (VSMs), these are too complex
to be used in the mentioned functions. As an alternative, this paper
contributes a novel framework based on Fourier-Motzkin Elimination
that automatically derives VCMs from VSMs by projecting unneeded
variables. Our results show that the projected VCMs are reduced by
an order of magnitude and can be solved 20–34 times faster than their
corresponding VSMs with only a negligible loss in accuracy. Our framework
is applicable to LP models in general, but are particularly effective
on block-angular structured problems such as VSMs. We show similar
results for a multi-commodity flow problem.

Original language	English
Title of host publication	Proceedings of the 10th International Conference on Computational Logistics (ICCL19)
Number of pages	16
Publisher	Springer
Publication date	30 Sept 2019
Pages	85-100
ISBN (Print)	978-3-030-31139-1
DOIs	https://doi.org/10.1007/978-3-030-31140-7_6
Publication status	Published - 30 Sept 2019
Event	10th International Conference on Computational Logistics - Barranquilla, Colombia Duration: 30 Sept 2019 → 2 Oct 2019 Conference number: 10

Conference

Conference	10th International Conference on Computational Logistics
Number	10
Country/Territory	Colombia
City	Barranquilla
Period	30/09/2019 → 02/10/2019

Series	Lecture Notes in Computer Science
Volume	11756
ISSN	0302-9743

Keywords

Vessel Capacity Models
Stowage Planning Optimization
Fourier-Motzkin Elimination
Container Shipping
Block-Angular Structured Problems

Access to Document

10.1007/978-3-030-31140-7_6

rmj2Final published version, 3.42 MB

Cite this

@inproceedings{dce07c5948554dfb8293d0f4cb45d8c8,

title = "A Decomposed Fourier-Motzkin Elimination Framework to Derive Vessel Capacity Models",

abstract = "Accurate Vessel Capacity Models (VCMs) expressing thetrade-off between different container types that can be stowed on containervessels are required in core liner shipping functions such as uptake-,capacity-, and network management. Today, simple models based on volume,weight, and refrigerated container capacity are used for these tasks,which causes overestimations that hamper decision making. Though previouswork on stowage planning optimization in principle provide finegrainedlinear Vessel Stowage Models (VSMs), these are too complexto be used in the mentioned functions. As an alternative, this papercontributes a novel framework based on Fourier-Motzkin Eliminationthat automatically derives VCMs from VSMs by projecting unneededvariables. Our results show that the projected VCMs are reduced byan order of magnitude and can be solved 20–34 times faster than theircorresponding VSMs with only a negligible loss in accuracy. Our frameworkis applicable to LP models in general, but are particularly effectiveon block-angular structured problems such as VSMs. We show similarresults for a multi-commodity flow problem.",

keywords = "Vessel Capacity Models, Stowage Planning Optimization, Fourier-Motzkin Elimination, Container Shipping, Block-Angular Structured Problems",

author = "Ajspur, {Mai Lise} and Jensen, {Rune M{\o}ller} and Andersen, {Kent H{\o}j}",

year = "2019",

month = sep,

day = "30",

doi = "10.1007/978-3-030-31140-7_6",

language = "English",

isbn = "978-3-030-31139-1",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "85--100",

booktitle = "Proceedings of the 10th International Conference on Computational Logistics (ICCL19)",

address = "Germany",

note = "10th International Conference on Computational Logistics, ICCL19 ; Conference date: 30-09-2019 Through 02-10-2019",

}

Ajspur, ML, Jensen, RM & Andersen, KH 2019, A Decomposed Fourier-Motzkin Elimination Framework to Derive Vessel Capacity Models. in Proceedings of the 10th International Conference on Computational Logistics (ICCL19). Springer, Lecture Notes in Computer Science, vol. 11756, pp. 85-100, 10th International Conference on Computational Logistics, Barranquilla, Colombia, 30/09/2019. https://doi.org/10.1007/978-3-030-31140-7_6

A Decomposed Fourier-Motzkin Elimination Framework to Derive Vessel Capacity Models. / Ajspur, Mai Lise; Jensen, Rune Møller; Andersen, Kent Høj.
Proceedings of the 10th International Conference on Computational Logistics (ICCL19). Springer, 2019. p. 85-100 (Lecture Notes in Computer Science, Vol. 11756).

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

TY - GEN

T1 - A Decomposed Fourier-Motzkin Elimination Framework to Derive Vessel Capacity Models

AU - Ajspur, Mai Lise

AU - Jensen, Rune Møller

AU - Andersen, Kent Høj

N1 - Conference code: 10

PY - 2019/9/30

Y1 - 2019/9/30

N2 - Accurate Vessel Capacity Models (VCMs) expressing thetrade-off between different container types that can be stowed on containervessels are required in core liner shipping functions such as uptake-,capacity-, and network management. Today, simple models based on volume,weight, and refrigerated container capacity are used for these tasks,which causes overestimations that hamper decision making. Though previouswork on stowage planning optimization in principle provide finegrainedlinear Vessel Stowage Models (VSMs), these are too complexto be used in the mentioned functions. As an alternative, this papercontributes a novel framework based on Fourier-Motzkin Eliminationthat automatically derives VCMs from VSMs by projecting unneededvariables. Our results show that the projected VCMs are reduced byan order of magnitude and can be solved 20–34 times faster than theircorresponding VSMs with only a negligible loss in accuracy. Our frameworkis applicable to LP models in general, but are particularly effectiveon block-angular structured problems such as VSMs. We show similarresults for a multi-commodity flow problem.

AB - Accurate Vessel Capacity Models (VCMs) expressing thetrade-off between different container types that can be stowed on containervessels are required in core liner shipping functions such as uptake-,capacity-, and network management. Today, simple models based on volume,weight, and refrigerated container capacity are used for these tasks,which causes overestimations that hamper decision making. Though previouswork on stowage planning optimization in principle provide finegrainedlinear Vessel Stowage Models (VSMs), these are too complexto be used in the mentioned functions. As an alternative, this papercontributes a novel framework based on Fourier-Motzkin Eliminationthat automatically derives VCMs from VSMs by projecting unneededvariables. Our results show that the projected VCMs are reduced byan order of magnitude and can be solved 20–34 times faster than theircorresponding VSMs with only a negligible loss in accuracy. Our frameworkis applicable to LP models in general, but are particularly effectiveon block-angular structured problems such as VSMs. We show similarresults for a multi-commodity flow problem.

KW - Vessel Capacity Models

KW - Stowage Planning Optimization

KW - Fourier-Motzkin Elimination

KW - Container Shipping

KW - Block-Angular Structured Problems

U2 - 10.1007/978-3-030-31140-7_6

DO - 10.1007/978-3-030-31140-7_6

M3 - Article in proceedings

SN - 978-3-030-31139-1

T3 - Lecture Notes in Computer Science

SP - 85

EP - 100

BT - Proceedings of the 10th International Conference on Computational Logistics (ICCL19)

PB - Springer

T2 - 10th International Conference on Computational Logistics

Y2 - 30 September 2019 through 2 October 2019

ER -

A Decomposed Fourier-Motzkin Elimination Framework to Derive Vessel Capacity Models

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this