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

•Decision-making in data-rich, large and complex intervention scenarios.•Integration of railway technical and business drivers for optimised interventions.•Optimum scheduling by sequencing heuristic and genetic algorithms.•Comprehensive task cost breakdown modelling for added-value autonomy.•Proof o...

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Bibliographic Details
Published in:	Transportation research. Part C, Emerging technologies Vol. 89; pp. 234 - 253
Main 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
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01-04-2018
Subjects:	Condition-based maintenance Cost engineering Data fusion Data-driven asset management of rail infrastructure Intelligent maintenance Planning and scheduling Systems design and implementation Systems integration Condition-based maintenance Intelligent maintenance Systems design and implementation Data fusion Data-driven asset management of rail infrastructure Systems integration Planning and scheduling Cost engineering
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Summary:	•Decision-making in data-rich, large and complex intervention scenarios.•Integration of railway technical and business drivers for optimised interventions.•Optimum scheduling by sequencing heuristic and genetic algorithms.•Comprehensive task cost breakdown modelling for added-value autonomy.•Proof of principle validated by British rail network stakeholders. 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.
ISSN:	0968-090X 1879-2359
DOI:	10.1016/j.trc.2018.02.010