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Beschreibung:
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 requirements 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 overall 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 ...