The Concept of Recoverable Robustness, Linear Programming Recovery, and Railway Applications
Robust and Online Large-Scale Optimization
Recoverable Robustness in Shunting and Timetabling
Robust and Online Large-Scale Optimization
Shunting for Dummies: An Introductory Algorithmic Survey
Robust and Online Large-Scale Optimization
Experimental evaluation of approximation and heuristic algorithms for sorting railway cars
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
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We consider a sorting problem from railway optimization called train classification: 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 classification 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 classification process that is quicker than the current railway practice but still provides robustness against realistic delays. In fact, our algorithm allows flexibly trading off fast classification 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.