Fast Approaches to Improve the Robustness of a Railway Timetable

• Published in: Transportation science Vol. 43; no. 3; pp. 321 - 335
• Main Authors: Fischetti, Matteo, Salvagnin, Domenico, Zanette, Arrigo
• Format: Journal Article
• Language: English
• Published: Linthicum, MD INFORMS 01.08.2009; Transportation Science & Logistic Society of the Institute for Operations Research and Management Sciences; Institute for Operations Research and the Management Sciences
• Subjects: Analysis; Applied sciences; Approximation; Delay lines; Efficiency; Efficiency loss; Exact sciences and technology; Geometric lines; Ground, air and sea transportation, marine construction; Integer programming; Italy; Linear programming; Management; Mathematical analysis; Mathematical models; Mathematical optimization; Mathematical robustness; Networks; Objective functions; Optimization techniques; Programming; Rail lines; Railroad trains; Railroads; Railway engineering; Railway networks; Railway transport; Railway transportation and traffic; Railways; robust optimization; robustness; Scheduling; Services; Stochastic models; stochastic programming; Studies; Time budgets; timetabling; Trains; Transport economics; Transportation; Transportation planning, management and economics; Transportation schedules; Trenitalia; United Kingdom; Italy; Integer programming; Validation; Railway; timetabling; robust optimization; Timetabling problem; Robustness; Rail transportation; Modeling; Comparative study; Optimization; Stochastic programming
• ISSN: 0041-1655; 1526-5447
• DOI: 10.1287/trsc.1090.0264

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.