Algorithmic Decision Support for the Construction of Periodic Railway Timetables
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Research Collection Doctoral Thesis Algorithmic decision support for the construction of periodic railway timetables Author(s): Herrigel-Wiedersheim, Sabrina Publication Date: 2015 Permanent Link: https://doi.org/10.3929/ethz-a-010412035 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library DISS. ETH NO. 22548 ALGORITHMIC DECISION SUPPORT FOR THE CONSTRUCTION OF PERIODIC RAILWAY TIMETABLES A thesis submitted to attain the degree of DOCTOR OF SCIENCES OF ETH ZURICH (DR. SC. ETH ZURICH) presented by SABRINA HERRIGEL-WIEDERSHEIM MSc in Mathematics, ETH Zurich born on 31.08.1985 citizen of Zurich accepted on the recommendation of Prof. Dr. Ulrich Alois Weidmann, examiner Prof. Dr. rer. nat. habil. Karl Nachtigall, co-examiner Prof. em. Dr. Hans-Jakob L¨uthi, co-examiner PD. Dr. Marco Laumanns, co-examiner 2015 Acknowledgments As a combination of operations research, railway systems and computer science the suggested topic for this PhD thesis could not have fitted to my interests better. I would like to thank Prof. Ulrich Weidmann and Prof. Hans-Jakob L¨uthi for the opportunity to write this thesis at their institutes. During my first 1.5 years at the Institute for Operations Research, I was able to extend my knowledge about operations research, precise argumentation and writing. In the following years, I was introduced to the applied world of railway planning and operations at the Institute for Transport Planning and Systems. I enjoyed the projects and collaborations close to practice a lot. A special thank goes to Prof. Dr. Ulrich Weidmann, as the main advisor of this thesis, who brought a lot of practical experience and industrial connections into the work. I greatly appreciated his continuous, kind and encouraging support during the past few years. Although he was contributing in many different projects and PhD theses, he always had time for discussions and advises. Besides railway systems, he thought me a lot about efficient working and collaborating with practice. Leading my thesis the first 1.5 years at the Institute for Operations Research, Prof. em. Dr. Hans-Jakob L¨uthi played another main role for the successful progress of my thesis. I am very grateful to Prof. em. Dr. Hans-Jakob L¨uthi for willing to employ me, to referee this thesis and for his patient advise and fruitful discussions. I am also very grateful to Prof. Dr. rer. nat. habil. Karl Nachtigall from TU Dresden willing to supervise this thesis, as the external referee. I greatly appreciated the visit at his research group and all interesting discussions concerning PESP and its application in practice. At this place, I would also like to thank Dr. Jens Opitz, Peter Grossmann and further members of his group for all exchanges and the presentation of the software TAKT. As a continuance supervisor, third co-supervisor and as a main impulse for the progress of this thesis, I am very much indebted to PD. Dr. Marco Laumanns. Several times he motivated me to publications and contributions at conferences and offered me a lot of support for writing papers, presentations and the whole thesis. Although he was involved in a lot of different projects, he had always time for fruitful discussions, listening to my doubts and giving me encouraging support. His contribution for the successful outcome of the work was essential. At this point, I further like to thank Dr. Jacint Szabo, who works at the same research group as PD. Dr. Marco Laumanns. He also contributed a lot, especially to Chapter 6. An applied PhD thesis is not really applied without connections to industry. At this place I would like to thank the Swiss Federal Railways (SBB) and especially Dr. Helga ii | Labermeier, M´edard Fischer and Dr. Peter Grossenbacher for all fruitful discussions and the supplied data. I really enjoyed this insight into the more practical life. In addition to SBB, I would also like to thank TrafIT Solutions, especially Dr. Dan Burkolter and Burkhard Franke for offering me to use the software OnTime to evaluate my models and timetables. I would further like to thank Prof. Leo Kroon, Dick Middelkoop and Dr. Birgit Hey- denreich organizing a visit at ProRail, including a lot of very fruitful and interesting discussions on the automation of timetabling, their software DONS and all mathematical challenges in connection to PESP. I want to thank all my former colleagues of both institutes also collaborating to sim- ilar railway projects, especially to Dr. Martin Fuchsberger, Dr. Kaspar Sch¨upbach, Dr. Gabrio Caimi and Dr. Dan Burkolter for their introduction and a lot of addi- tional support. I’m also grateful to Prof. Dr. Rico Zenklusen, Dr. David Adjiashvili and Dr. Christian Wagner for their ideas and discussions to Chapter 5. Furthermore, I want to thank to Betty Fahrni and Dr. Christian Eichenberger for proofreading my thesis. I want to address an additional thank to all my present and former colleagues at both research institutes and further international colleagues I had the opportunity to get to know at conferences during the last years. You all made the last years a very pleasant and interesting time. A special thank goes at this point to my office colleagues Dr. Martin Fuchsberger, Dr. Olga Fink and Christian Marti. They were always there for discussions, new ideas, support, or simply a good laugh. Ein ganz herzlicher Dank geht an alle meine engsten Freunde und die Familie, insbeson- dere meine Eltern, mein Mann Roger und mein Sohn Levin, die w¨ahrend allen Jahren immer hinter mir standen, mich unterst¨utzten und f¨ur mich da waren. Abstract This thesis addresses the problem of solving large railway timetabling problems using algorithmic methods. With the increasing demand for better and more frequent services, for higher capacity utilization and for improved reliability, railway timetabling problems become more and more complex. Algorithmic decision support is one promising way to cope with this increasing complexity, and the continuous progress of operations research methods offers a significant potential for the railway industry. The approach of this thesis concentrates on algorithms for the construction of periodic railway timetables during long- and mid-term planning. As an input, functional require- ments and restrictions of the infrastructure have to be described at a model granularity suitable for the given planning stage. The algorithmic approach then creates a periodic timetable with the same model granularity and optimizes the timetable according to a given objective. Compared to a manual timetable construction, the algorithmic approach allows to compare different timetable scenarios in a shorter time. Possible modifications of the infrastructure or the service intention and their influence on the over- all timetable can therefore be tested and evaluated more efficiently and also in more detail. The algorithms studied in this thesis are based on the so called Periodic Event Scheduling Problem (PESP), a mathematical model which proved to be suitable to automate the construction of periodic railway timetables already several times in research and practice. To solve the model there exist different mathematical methods. This thesis summarizes them and shows advantages of a method based on Mixed Integer Linear Programming (MILP). Compared to other solution methods it allows a direct optimization and with this several further advantages concerning a more efficient automation and a better solution quality. However there exists no practical experience of the method for larger timetabling problems and there even can be found the conjecture that the solution method is not suitable for large scale problems. The algorithms developed in this thesis are based on the so-called Periodic Event Scheduling Problem (PESP), a mathematical model which has become the standard approach for algorithmic construction of periodic railway timetables and has been used successfully already several times in research and practice. Different mathematical meth- ods have been proposed to solve timetabling problems formulated as a PESP. This thesis summarizes the different solution approaches and shows advantages of a method based on Mixed Integer Linear Programming (MILP). Compared to other solution methods, the MILP approach allows a direct optimization, and with this several further advantages concerning a more efficient automation and a better solution quality. However, there exists no practical experience of the method for larger timetabling problems and it has iv | been an open question so far whether and to what extent it is suitable for large-scale problems. In this thesis this gap is filled by providing the missing experience of applying the MILP solution approach for a set of increasingly large timetabling problems for parts of the Swiss railway network. To profit from the method’s advantages also for large-scale prob- lems, an adaptation of the solution method is introduced. It allows to reduce computa- tional complexity considerably, but still ensures good solution quality. To this end, the thesis provides three main contributions: • Implementation of the defined algorithms including an automated model construc- tion out of real data provided by Swiss Federal Railways (SBB). • Extension of the models until a limit of computation time for the used algorithms is reached. • Development of new methods for the acceleration of the given algorithms to solve and optimize also larger models. To ensure operational feasibility for the given model granularity and a certain degree of timetable quality for our models, the resulting timetables are evaluated by the software OnTime. This way it can be ensured that the level of detail of our models is rich enough to represent realistic timetable scenarios. However, the models in this thesis are not constructed to study and evaluate a concrete timetable scenario but rather to represent different model sizes to study the computational performance of the proposed algorithms.