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    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 259, 2, 2017 DOI: 10.1016/j.ejor.2016.10.034

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Line planning with user-optimal route choice

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Line planning with user-optimal route choice. / Goerigk, Marc; Schmidt, Marie.
In: European Journal of Operational Research, Vol. 259, No. 2, 01.06.2017, p. 424-436.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Goerigk, M & Schmidt, M 2017, 'Line planning with user-optimal route choice', European Journal of Operational Research, vol. 259, no. 2, pp. 424-436. https://doi.org/10.1016/j.ejor.2016.10.034

APA

Goerigk, M., & Schmidt, M. (2017). Line planning with user-optimal route choice. European Journal of Operational Research, 259(2), 424-436. https://doi.org/10.1016/j.ejor.2016.10.034

Vancouver

Goerigk M, Schmidt M. Line planning with user-optimal route choice. European Journal of Operational Research. 2017 Jun 1;259(2):424-436. Epub 2016 Oct 22. doi: 10.1016/j.ejor.2016.10.034

Author

Goerigk, Marc ; Schmidt, Marie. / Line planning with user-optimal route choice. In: European Journal of Operational Research. 2017 ; Vol. 259, No. 2. pp. 424-436.

Bibtex

@article{82aaa483f2374ebf977778488cad8f11,
title = "Line planning with user-optimal route choice",
abstract = "We consider the problem of designing lines in a public transport system, where we include user-optimal route choice. The model we develop ensures that there is enough capacity present for every passenger to travel on a shortest route. We present two different integer programming formulations for this problem, and discuss exact solution approaches. To solve large-scale line planning instances, we also implemented a genetic solution algorithms. We test our algorithms in computational experiments using randomly generated instances along realistic data, as well as a realistic instance modeling the German long-distance network. We examine the advantages and disadvantages of using such user-optimal solutions, and show that our algorithms sufficiently scale with instance size to be used for practical purposes.",
keywords = "Transportation, Line planning, Public transport capacity, Equilibrium constraints, Railway optimization",
author = "Marc Goerigk and Marie Schmidt",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 259, 2, 2017 DOI: 10.1016/j.ejor.2016.10.034",
year = "2017",
month = jun,
day = "1",
doi = "10.1016/j.ejor.2016.10.034",
language = "English",
volume = "259",
pages = "424--436",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "2",

}

RIS

TY - JOUR

T1 - Line planning with user-optimal route choice

AU - Goerigk, Marc

AU - Schmidt, Marie

N1 - This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 259, 2, 2017 DOI: 10.1016/j.ejor.2016.10.034

PY - 2017/6/1

Y1 - 2017/6/1

N2 - We consider the problem of designing lines in a public transport system, where we include user-optimal route choice. The model we develop ensures that there is enough capacity present for every passenger to travel on a shortest route. We present two different integer programming formulations for this problem, and discuss exact solution approaches. To solve large-scale line planning instances, we also implemented a genetic solution algorithms. We test our algorithms in computational experiments using randomly generated instances along realistic data, as well as a realistic instance modeling the German long-distance network. We examine the advantages and disadvantages of using such user-optimal solutions, and show that our algorithms sufficiently scale with instance size to be used for practical purposes.

AB - We consider the problem of designing lines in a public transport system, where we include user-optimal route choice. The model we develop ensures that there is enough capacity present for every passenger to travel on a shortest route. We present two different integer programming formulations for this problem, and discuss exact solution approaches. To solve large-scale line planning instances, we also implemented a genetic solution algorithms. We test our algorithms in computational experiments using randomly generated instances along realistic data, as well as a realistic instance modeling the German long-distance network. We examine the advantages and disadvantages of using such user-optimal solutions, and show that our algorithms sufficiently scale with instance size to be used for practical purposes.

KW - Transportation

KW - Line planning

KW - Public transport capacity

KW - Equilibrium constraints

KW - Railway optimization

U2 - 10.1016/j.ejor.2016.10.034

DO - 10.1016/j.ejor.2016.10.034

M3 - Journal article

VL - 259

SP - 424

EP - 436

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 2

ER -