TAILIEUCHUNG - Decision Support for the Rolling Stock Dispatcher

So far, economic fluctuations have been predicted almost exclusively through the aggregate infor- mation conveyed either by i) macro variables (labor market conditions, money, credit, lagged growth), ii) financial indicators (aggregate stock market returns and variances, slope of the yield curve, credit spreads) or iii) confidence (households or business) indicators. Focusing on models including aggre- gate financial variables, which are also the focus of the present paper, a broad conclusion reached by analyses carried out so far is that their predictive power is broadly unstable over time and also that the set of indicators which are key to improve the forecast of business cycle developments tends. | Decision Support for the Rolling Stock Dispatcher Julie Jespersen Groth Kongens Lyngby 2008 IMM-PHD-2008-212 Technical University of Denmark Informatics and Mathematical Modelling Building 321 DK-2800 Kongens Lyngby Denmark Phone 45 45253351 Fax 45 45882673 reception@ IMM-PHD ISSN 0909-3192 Summary Real-time recovery is receiving a fast growing interest in an increasingly competitive railway operation market. This thesis considers the area of rolling stock dispatching which is one of the typical real-time railway dispatching problems. All work of the thesis is based on the network and planning processes of the railway operator DSB S-tog a s. In the thesis the problems existing in the railway planning process from the strategic to real-time level are briefly sketched. Network planning line planning timetabling crew and rolling stock planning is outlined and relevant references are given. Specifically the thesis references the operation research studies based on the railway operation of DSB S-tog a s. Subsequently the process of dispatching is outlined with a specific emphasis on rolling stock. The rolling stock recovery problem is the problem of assigning train units to train departures in a disrupted rolling stock schedule so that operation returns quickly to the originally planned schedule. Different network structures and mathematical formulations for the problem are discussed. Based on prior work on network structures a decomposed approach for the rolling stock recovery problem is put forward. The main contributions of the thesis are contained in four papers included as appendices. The papers deal with respectively an analysis of robustness in timetables the mathematical model behind a decision support tool for reinsertion of a train line a survey on the dispatching problems of passenger railway transportation and the decomposed solution process of the rolling stock recovery .

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