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Metaheuristics approach for solving personalized crew rostering problem in public bus transit

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Metaheuristics approach for solving personalized crew rostering problem in public bus transit. / Xie, Lin; Merschformann, Marius; Kliewer, Natalia et al.
In: Journal of Heuristics, Vol. 23, No. 5, 10.2017, p. 321-347.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Xie, L, Merschformann, M, Kliewer, N & Suhl, L 2017, 'Metaheuristics approach for solving personalized crew rostering problem in public bus transit', Journal of Heuristics, vol. 23, no. 5, pp. 321-347. https://doi.org/10.1007/s10732-017-9348-7

APA

Xie, L., Merschformann, M., Kliewer, N., & Suhl, L. (2017). Metaheuristics approach for solving personalized crew rostering problem in public bus transit. Journal of Heuristics, 23(5), 321-347. https://doi.org/10.1007/s10732-017-9348-7

Vancouver

Xie L, Merschformann M, Kliewer N, Suhl L. Metaheuristics approach for solving personalized crew rostering problem in public bus transit. Journal of Heuristics. 2017 Oct;23(5):321-347. Epub 2017 Jun 30. doi: 10.1007/s10732-017-9348-7

Bibtex

@article{9a83ad81dbcf4f6e96070be0c921a44b,
title = "Metaheuristics approach for solving personalized crew rostering problem in public bus transit",
abstract = "The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.",
keywords = "Business informatics, Ant colony optimization , Simulated annealing , Tabu search , Crew rostering problem , Personalized/non-cyclic rostering , Public transport ",
author = "Lin Xie and Marius Merschformann and Natalia Kliewer and Leena Suhl",
year = "2017",
month = oct,
doi = "10.1007/s10732-017-9348-7",
language = "English",
volume = "23",
pages = "321--347",
journal = "Journal of Heuristics",
issn = "1381-1231",
publisher = "Springer Netherlands",
number = "5",

}

RIS

TY - JOUR

T1 - Metaheuristics approach for solving personalized crew rostering problem in public bus transit

AU - Xie, Lin

AU - Merschformann, Marius

AU - Kliewer, Natalia

AU - Suhl, Leena

PY - 2017/10

Y1 - 2017/10

N2 - The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.

AB - The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.

KW - Business informatics

KW - Ant colony optimization

KW - Simulated annealing

KW - Tabu search

KW - Crew rostering problem

KW - Personalized/non-cyclic rostering

KW - Public transport

UR - http://www.scopus.com/inward/record.url?scp=85021732871&partnerID=8YFLogxK

U2 - 10.1007/s10732-017-9348-7

DO - 10.1007/s10732-017-9348-7

M3 - Journal articles

VL - 23

SP - 321

EP - 347

JO - Journal of Heuristics

JF - Journal of Heuristics

SN - 1381-1231

IS - 5

ER -

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Comparing two hybrid neural network models to predict real-world bus travel time

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Research output: Journal contributions › Conference article in journal › Research

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Ge, L., Kliewer, N., Nourmohammadzadeh, A., Voß, S. & Xie, L., 10.2024, In: Public Transport. 16, 3, p. 775-801 27 p.

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Otten, S., Krenzler, R., Xie, L., Daduna, H. & Kruse, K., 06.2022, In: OR Spectrum. 44, 2, p. 603-648 46 p.

Research output: Journal contributions › Journal articles › Research › peer-review

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Xie, L., Li, H. & Luttmann, L., 01.06.2023, In: European Journal of Operational Research . 307, 2, p. 713-730 18 p.

Research output: Journal contributions › Journal articles › Research › peer-review

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