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Data‐driven maintenance planning and scheduling based on predicted railway track condition
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering.ORCID iD: 0000-0001-9681-3804
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-3911-8009
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-1473-3670
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering.ORCID iD: 0000-0001-8473-3663
2022 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 38, no 7, p. 3689-3709Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2022. Vol. 38, no 7, p. 3689-3709
Keywords [en]
decision-making framework, multi-component system, planning and scheduling, predictive maintenance, railway track, Wiener process
National Category
Computer Engineering Production Engineering, Human Work Science and Ergonomics Other Mechanical Engineering
Research subject
Quality technology and logistics
Identifiers
URN: urn:nbn:se:ltu:diva-92182DOI: 10.1002/qre.3166ISI: 000826112600001Scopus ID: 2-s2.0-85134349665OAI: oai:DiVA.org:ltu-92182DiVA, id: diva2:1683702
Funder
Swedish Transport Administration
Note

Validerad;2022;Nivå 2;2022-11-28 (joosat);

Funder: Swedish Strategic Innovation Programme InfraSweden2030 (2016–04757); Luleå Railway Research Centre (JVTC); Predge AB

Available from: 2022-07-18 Created: 2022-07-18 Last updated: 2023-09-05Bibliographically approved

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Sedghi, MahdiehBergquist, BjarneVanhatalo, ErikMigdalas, Athanasios

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