One of the last elements of the planning process of a passenger railway operator is operational shunt planning. It focuses on the logistics within a station and its surroundings. Since demand for transportation fluctuates over a day, a railway operator typically has a surplus of rolling stock outside the rush hours, and especially during the night. In general, the idle rolling stock is parked at a shunt yard, thereby keeping the main railway infrastructure available for other train services. Besides parking of rolling stock, matching of arriving to departing rolling stock, routing over local railway infrastructure, cleaning of rolling stock, and crew planning are part of shunt planning. "Algorithmic Decision Support for Shunt Planning" introduces relevant aspects of shunting and provides a first step for quantitative models and algorithms to support shunt planning. The algorithms for solving the models contain algorithms that resemble the current practice of shunt planners as well as algorithms that are somewhat farther away from current practice. Computational tests on real-life data show that high-quality solutions are typically found within minutes of computation time. In addition, these algorithms are designed to interact with shunt planners. They provide a firm basis for an advanced planning system to support shunt planners.

Additional Metadata
Keywords Shunt planning, algorithms, decision support, railway planning
JEL Optimization Techniques; Programming Models; Dynamic Analysis (jel C61), Railroads and Other Surface Transportation: Autos, Buses, Trucks, and Water Carriers; Ports (jel L92), Business Administration and Business Economics; Marketing; Accounting (jel M), Transportation Systems (jel R4)
Promotor L.G. Kroon (Leo)
Publisher Erasmus University Rotterdam , Erasmus Research Institute of Management
Sponsor Bertrand, J.W.M., Kroon, L.G., Nunen, J.A.E.E. van, Schrijver, A., Wagelmans, A.P.M.
ISBN 978-90-5892-104-8
Persistent URL
Series ERIM Ph.D. Series Research in Management
Lentink, R.M. (2006, February 10). Algorithmic Decision Support for Shunt Planning (No. 73). ERIM Ph.D. Series Research in Management. Erasmus Research Institute of Management. Retrieved from