An autonomous system for maintenance scheduling data-rich complex infrastructure:Fusing the railways’ condition, planning and cost


National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.

Divisions: College of Engineering & Physical Sciences
Additional Information: Crown Copyright © 2018 Published by Elsevier Ltd. This is an open access article under the CC BY license (
Publication ISSN: 0968-090X
Last Modified: 29 Nov 2023 11:54
Date Deposited: 16 Apr 2018 08:35
Full Text Link: 10.1016/j.trc.2018.02.010
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 2055?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2018-02-22
Accepted Date: 2018-02-15
Authors: Durazo-Cardenas, Isidro
Starr, Andrew
Turner, Christopher J.
Tiwari, Ashutosh
Kirkwood, Leigh
Bevilacqua, Maurizio
Tsourdos, Antonios
Shehab, Essam
Baguley, Paul
Xu, Yuchun
Emmanouilidis, Christos



Version: Published Version

License: Creative Commons Attribution

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