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Approximating the RSRP with Predictive Maintenance

Please always quote using this URN: urn:nbn:de:0297-zib-89531
  • We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational results for two instances derived from real world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions.

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Author:Felix PrauseORCiD, Ralf BorndörferORCiD, Boris Grimm, Alexander Tesch
Document Type:ZIB-Report
Tag:Heuristic; Integer Linear Programming; Lower Bound; Predictive Maintenance; Rolling Stock Rotation Planning
Date of first Publication:2023/01/20
Series (Serial Number):ZIB-Report (23-04)
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