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Robust Algorithms for Sorting Railway Cars

Please always quote using this URN:urn:nbn:de:0296-matheon-10267
  • We consider a sorting problem from railway optimization called train classi� fication: incoming trains are split up into their single cars and reassembled to form new outgoing trains. Trains are subject to delay, which may turn a prepared sorting schedule infeasible for the disturbed situation. The classi� fication methods applied today deal with this issue by completely disregarding the input order of cars, which provides robustness against any amount of disturbance but also wastes the potential contained in the a priori knowledge about the input. We introduce a new method that provides a feasible sorting schedule for the expected input and allows to flexibly insert additional sorting steps if the schedule has become infeasible after revealing the disturbed input. By excluding disruptions that almost never occur from our consideration, we obtain a classi� fication process that is quicker than the current railway practice but still provides robustness against realistic delays. In fact, our algorithm allows flexibly trading o� ff fast classi� fication against high degrees of robustness depending on the respective need. We further explore this flexibility in experiments on real-world traffic data, underlining our algorithm improves on the methods currently applied in practice.

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Metadaten
Author:Christina Büsing, Jens Maue
URN:urn:nbn:de:0296-matheon-10267
Referee:Rolf H. Möhring
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2012/01/25
Release Date:2012/01/25
Tag:Recovery; Robust Optimization; Train Classification
Institute:Technische Universität Berlin
MSC-Classification:05-XX COMBINATORICS (For finite fields, see 11Txx) / 05-06 Proceedings, conferences, collections, etc.
Preprint Number:876
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