Data‐driven maintenance planning and scheduling based on predicted railway track condition
Abstract Timely planning and scheduling of railway infrastructure maintenance interventions are crucial for increased safety, improved availability, and reduced cost. We propose a data‐driven decision‐support framework integrating track condition predictions with tactical maintenance planning and op...
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Online Access: | http://dx.doi.org/10.1002/qre.3166 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qre.3166 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qre.3166 |
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crwiley:10.1002/qre.3166 2024-06-23T07:55:36+00:00 Data‐driven maintenance planning and scheduling based on predicted railway track condition Sedghi, Mahdieh Bergquist, Bjarne Vanhatalo, Erik Migdalas, Athanasios 2022 http://dx.doi.org/10.1002/qre.3166 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qre.3166 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qre.3166 en eng Wiley http://creativecommons.org/licenses/by-nc-nd/4.0/ Quality and Reliability Engineering International volume 38, issue 7, page 3689-3709 ISSN 0748-8017 1099-1638 journal-article 2022 crwiley https://doi.org/10.1002/qre.3166 2024-06-11T04:43:42Z Abstract Timely planning and scheduling of railway infrastructure maintenance interventions are crucial for increased safety, improved availability, and reduced cost. We propose a data‐driven decision‐support framework integrating track condition predictions with tactical maintenance planning and operational scheduling. The framework acknowledges prediction uncertainties by using a Wiener process‐based prediction model at the tactical level. We also develop planning and scheduling algorithms at the operational level. One algorithm focuses on cost‐optimisation, and one algorithm considers the multi‐component characteristics of the railway track by grouping track segments near each other for one maintenance activity. The proposed framework's performance is evaluated using track geometry measurement data from a 34 km railway section in northern Sweden, focusing on the tamping maintenance action. We analyse maintenance costs and demonstrate potential efficiency increases by applying the decision‐support framework. Article in Journal/Newspaper Northern Sweden Wiley Online Library Quality and Reliability Engineering International 38 7 3689 3709 |
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English |
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Abstract Timely planning and scheduling of railway infrastructure maintenance interventions are crucial for increased safety, improved availability, and reduced cost. We propose a data‐driven decision‐support framework integrating track condition predictions with tactical maintenance planning and operational scheduling. The framework acknowledges prediction uncertainties by using a Wiener process‐based prediction model at the tactical level. We also develop planning and scheduling algorithms at the operational level. One algorithm focuses on cost‐optimisation, and one algorithm considers the multi‐component characteristics of the railway track by grouping track segments near each other for one maintenance activity. The proposed framework's performance is evaluated using track geometry measurement data from a 34 km railway section in northern Sweden, focusing on the tamping maintenance action. We analyse maintenance costs and demonstrate potential efficiency increases by applying the decision‐support framework. |
format |
Article in Journal/Newspaper |
author |
Sedghi, Mahdieh Bergquist, Bjarne Vanhatalo, Erik Migdalas, Athanasios |
spellingShingle |
Sedghi, Mahdieh Bergquist, Bjarne Vanhatalo, Erik Migdalas, Athanasios Data‐driven maintenance planning and scheduling based on predicted railway track condition |
author_facet |
Sedghi, Mahdieh Bergquist, Bjarne Vanhatalo, Erik Migdalas, Athanasios |
author_sort |
Sedghi, Mahdieh |
title |
Data‐driven maintenance planning and scheduling based on predicted railway track condition |
title_short |
Data‐driven maintenance planning and scheduling based on predicted railway track condition |
title_full |
Data‐driven maintenance planning and scheduling based on predicted railway track condition |
title_fullStr |
Data‐driven maintenance planning and scheduling based on predicted railway track condition |
title_full_unstemmed |
Data‐driven maintenance planning and scheduling based on predicted railway track condition |
title_sort |
data‐driven maintenance planning and scheduling based on predicted railway track condition |
publisher |
Wiley |
publishDate |
2022 |
url |
http://dx.doi.org/10.1002/qre.3166 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qre.3166 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qre.3166 |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
Quality and Reliability Engineering International volume 38, issue 7, page 3689-3709 ISSN 0748-8017 1099-1638 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.1002/qre.3166 |
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Quality and Reliability Engineering International |
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38 |
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7 |
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3689 |
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3709 |
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1802648262778290176 |