@inproceedings{BorndoerferMehrgardtReutheretal., author = {Bornd{\"o}rfer, Ralf and Mehrgardt, Julika and Reuther, Markus and Schlechte, Thomas and Waas, Kerstin}, title = {Re-Optimization of Rolling Stock Rotations}, edition = {Operations Research Proceedings 2013}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-319-07001-8_8}, pages = {49 -- 55}, abstract = {The Rolling Stock Rotation Problem is to schedule rail vehicles in order to cover timetabled trips by a cost optimal set of vehicle rotations. The problem integrates several facets of railway optimization, such as vehicle composition, maintenance constraints, and regularity aspects. In industrial applications existing vehicle rotations often have to be re-optimized to deal with timetable changes or construction sites. We present an integrated modeling and algorithmic approach to this task as well as computational results for industrial problem instances of DB Fernverkehr AG.}, language = {en} } @article{BorndoerferReutherSchlechteetal., author = {Bornd{\"o}rfer, Ralf and Reuther, Markus and Schlechte, Thomas and Waas, Kerstin and Weider, Steffen}, title = {Integrated Optimization of Rolling Stock Rotations for Intercity Railways}, series = {Transportation Science}, volume = {50}, journal = {Transportation Science}, number = {3}, doi = {10.1287/trsc.2015.0633}, pages = {863 -- 877}, abstract = {This paper proposes a highly integrated solution approach for rolling stock planning problems in the context of long distance passenger traffic between cities. The main contributions are a generic hypergraph-based mixed-integer programming model for the considered rolling stock rotation problem and an integrated algorithm for its solution. The newly developed algorithm is able to handle a large spectrum of industrial railway requirements, such as vehicle composition, maintenance constraints, infrastructure capacities, and regularity aspects. We show that our approach has the power to produce rolling stock rotations that can be implemented in practice. In this way, the rolling stock rotations at the largest German long distance operator Deutsche Bahn Fernverkehr AG could be optimized by an automated system utilizing advanced mathematical programming techniques.}, language = {en} } @misc{BorndoerferMehrgardtReutheretal., author = {Bornd{\"o}rfer, Ralf and Mehrgardt, Julika and Reuther, Markus and Schlechte, Thomas and Waas, Kerstin}, title = {Re-optimization of Rolling Stock Rotations}, issn = {1438-0064}, doi = {10.1007/978-3-319-07001-8_8}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42569}, abstract = {The Rolling Stock Rotation Problem is to schedule rail vehicles in order to cover timetabled trips by a cost optimal set of vehicle rotations. The problem integrates several facets of railway optimization, i.e., vehicle composition, maintenance constraints, and regularity aspects. In industrial applications existing schedules often have to be re-optimized to integrate timetable changes or construction sites. We present an integrated modeling and algorithmic approach for this task as well as computational results for industrial problem instances of DB Fernverkehr AG.}, language = {en} } @article{BorndoerferEsserFrankenbergeretal., author = {Bornd{\"o}rfer, Ralf and Eßer, Thomas and Frankenberger, Patrick and Huck, Andreas and Jobmann, Christoph and Krostitz, Boris and Kuchenbecker, Karsten and Moorhagen, Kai and Nagl, Philipp and Peterson, Michael and Reuther, Markus and Schang, Thilo and Schoch, Michael and Sch{\"u}lldorf, Hanno and Sch{\"u}tz, Peter and Therolf, Tobias and Waas, Kerstin and Weider, Steffen}, title = {Deutsche Bahn Schedules Train Rotations Using Hypergraph Optimization}, series = {Informs Journal on Applied Analytics}, volume = {51}, journal = {Informs Journal on Applied Analytics}, number = {1}, doi = {10.1287/inte.2020.1069}, pages = {42 -- 62}, abstract = {Deutsche Bahn (DB) operates a large fleet of rolling stock (locomotives, wagons, and train sets) that must be combined into trains to perform rolling stock rotations. This train composition is a special characteristic of railway operations that distinguishes rolling stock rotation planning from the vehicle scheduling problems prevalent in other industries. DB models train compositions using hyperarcs. The resulting hypergraph models are ad-dressed using a novel coarse-to-fine method that implements a hierarchical column genera-tion over three levels of detail. This algorithm is the mathematical core of DB's fleet em-ployment optimization (FEO) system for rolling stock rotation planning. FEO's impact within DB's planning departments has been revolutionary. DB has used it to support the company's procurements of its newest high-speed passenger train fleet and its intermodal cargo locomotive fleet for cross-border operations. FEO is the key to successful tendering in regional transport and to construction site management in daily operations. DB's plan-ning departments appreciate FEO's high-quality results, ability to reoptimize (quickly), and ease of use. Both employees and customers benefit from the increased regularity of operations. DB attributes annual savings of 74 million euro, an annual reduction of 34,000 tons of CO2 emissions, and the elimination of 600 coupling operations in cross-border operations to the implementation of FEO.}, language = {en} }