Rail track operators have traditionally used manual methods to construct train timetables. Creating a timetable can take several weeks, and so the process usually stops once the first feasible timetable has been found. It is suspected that this timetable is often far from optimal. Existing methods schedule track maintenance once the best train timetable has been determined and allow little or no adjustments to the timetable. This approach almost certainly produces suboptimal integrated solutions since the track maintenance schedule is developed with the imposition of the previously constructed train timetable. The research in this thesis considers operationally feasible methods to produce integrated train timetables and track maintenance schedules so that, when evaluated according to key performance criteria, the overall schedule is the best possible. This research was carried out as part of the Cooperative Research Centre for Railway Engineering and Technologies. We developed a method that uses a local search meta-heuristic called 'problem space search'. A fast dispatch heuristic repeatedly selects and moves a track possessor (train or maintenance task) through the network; this results in a single integrated schedule. This technique generates a collection of alternative feasible schedules by applying the dispatch heuristic to different sets of randomly perturbed data. The quality of the schedules is then evaluated. Thousands of feasible solutions can be found within minutes. We also formulated an integer programming model that selects a path for each train and maintenance task from a set of alternatives. If all possible paths are considered, then the best schedule found is guaranteed to be optimal. To reduce the size of the model, we explored a reduction technique called 'branch and price'. The method works on small example problems where paths are selected from a predetermined set, but the computation time and memory requirements mean that the method is not suitable for realistic problems. The main advantages of the problem space search method are generality and speed. We are able to model the operations of a variety of rail networks due to the representation of the problem. The generated schedules can be ranked with a user-defined objective measure. The speed at which we produce a range of feasible integrated schedules allows the method to be used in an operational setting, both to create schedules and to test different scenarios. A comparison with simulated current practice on a range of test data sets reveals improvements in total delay of up to 22%.
| Identifer | oai:union.ndltd.org:ADTP/284031 |
| Date | January 2009 |
| Creators | Albrecht, Amie |
| Source Sets | Australiasian Digital Theses Program |
| Language | EN-AUS |
| Detected Language | English |
| Rights | Copyright Amie Albrecht 2009 |
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