The New Dutch Timetable: The OR Revolution

Leo Kroon (), Dennis Huisman (), Erwin Abbink (), Pieter-Jan Fioole (), Matteo Fischetti (), Gábor Maróti (), Alexander Schrijver (), Adri Steenbeek () and Roelof Ybema ()
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Leo Kroon: Department of Logistics, Netherlands Railways (NS), NL-3500 HA Utrecht, and Rotterdam School of Management, Erasmus University Rotterdam, NL-3000 DR Rotterdam, The Netherlands
Dennis Huisman: Department of Logistics, Netherlands Railways (NS), NL-3500 HA Utrecht, and Econometric Institute, Erasmus University Rotterdam, NL-3000 DR Rotterdam, The Netherlands
Erwin Abbink: Department of Logistics, Netherlands Railways (NS), NL-3500 HA Utrecht, The Netherlands
Pieter-Jan Fioole: Department of Logistics, Netherlands Railways (NS), NL-3500 HA Utrecht, The Netherlands
Matteo Fischetti: Department of Information Engineering, University of Padova, and Double-Click sas, I-35131 Padova, Italy
Gábor Maróti: Rotterdam School of Management, Erasmus University Rotterdam, NL-3000 DR Rotterdam, The Netherlands
Alexander Schrijver: Center for Mathematics and Computer Science (CWI), Amsterdam, and University of Amsterdam, NL-1098 SJ Amsterdam, The Netherlands
Adri Steenbeek: Safiro Software Solutions, NL-1303 AJ Almere, The Netherlands
Roelof Ybema: Department of Logistics, Netherlands Railways (NS), NL-3500 HA Utrecht, The Netherlands

Interfaces, 2009, vol. 39, issue 1, 6-17

Abstract: In December 2006, Netherlands Railways introduced a completely new timetable. Its objective was to facilitate the growth of passenger and freight transport on a highly utilized railway network and improve the robustness of the timetable, thus resulting in fewer operational train delays. Modifications to the existing timetable, which was constructed in 1970, were not an option; additional growth would require significant investments in the rail infrastructure. Constructing a railway timetable from scratch for about 5,500 daily trains was a complex problem. To support this process, we generated several timetables using sophisticated operations research techniques. Furthermore, because rolling-stock and crew costs are principal components of the costs of a passenger railway operator, we used innovative operations research tools to devise efficient schedules for these two resources. The new resource schedules and the increased number of passengers resulted in an additional annual profit of €40 million ($60 million); the additional revenues generated approximately €10 million of this profit. We expect this profit to increase to €70 million ($105 million) annually in the coming years. However, the benefits of the new timetable for the Dutch society as a whole are much greater: more trains are transporting more passengers on the same railway infrastructure, and these trains are arriving and departing on schedule more than they ever have in the past. In addition, the rail transport system will be able to handle future transportation demand growth and thus allow cities to remain accessible to more people. Therefore, we expect that many will switch from car transport to rail transport, thus reducing the emission of greenhouse gases.

Keywords: transportation; rail; scheduling; vehicles; crew; programming; large-scale systems; integer; network graphs; multicommodity (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (46)

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