Title
Fast Approaches to Improve the Robustness of a Railway Timetable
Abstract
The train timetabling problem (TTP) consists of finding a train schedule on a railway network that satisfies some operational constraints and maximizes some profit function that accounts for the efficiency of the infrastructure usage. In practical cases, however, the maximization of the objective function is not enough, and one calls for a robust solution that is capable of absorbing, as much as possible, delays/disturbances on the network. In this paper we propose and computationally analyze four different methods to improve the robustness of a given TTP solution for the aperiodic (noncyclic) case. The approaches combine linear programming (LP) and ad hoc stochastic programming/robust optimization techniques. We computationally compare the effectiveness and practical applicability of the four techniques under investigation on real-world test cases from the Italian railway company Trenitalia. The outcome is that two of the proposed techniques are very fast and provide robust solutions of comparable quality with respect to the standard (but very time consuming) stochastic programming approach.
Year
DOI
Venue
2009
10.1287/trsc.1090.0264
Transportation Science
Keywords
Field
DocType
robust solution,linear programming,robust optimization.,stochastic programming,objective function,profit function,practical applicability,ttp solution,italian railway company,stochastic programming approach,timetabling,practical case,fast approaches,integer programming,robustness,railway timetable,robust optimization technique,linear program,robust optimization
Mathematical optimization,Robust optimization,Robustness (computer science),Integer programming,Schedule,Test case,Linear programming,Stochastic programming,Operations management,Maximization,Mathematics
Journal
Volume
Issue
ISSN
43
3
0041-1655
Citations 
PageRank 
References 
43
2.04
10
Authors
3
Name
Order
Citations
PageRank
Matteo Fischetti12505260.53
Domenico Salvagnin228921.05
Arrigo Zanette31177.24