Optimization of a railway freight yard in real time

Résumé : The efficiency of rail good transportation is dependent on the processes implemented in shunting yards. Specifically, from the network, trains arrive and depart through the receiving and the departure bowl, respectively. The railcars of the inbound trains are recombined to form the desired outbound trains with the use of a hump from where they slide downhill to the classification bowl. If necessary, railcars may have to be pulled back and made slide again to achieve specific sortings. It is this going back and forth between the hump and the classification bowl that the optimization is most critical. Today, the recombination of the freight trains in yards can represent up to 50% of total transportation time. This large value can be explained by the weak automation and the lack of optimization. As part of the European project, OptiYard we will present an optimization tool designed to fit shunting yards in Europe. For assessing this tool, we will exploit microscopic simulation in a closed-loop framework. In this work, we realize a state of the art on the optimization of yard processes. We study different types of train sorting: simultaneous sorting, triangular sorting or geometric sorting. Recently quite complex methods have been published, many of them being based on integer programming. However, rather few papers exist on the real-time optimization of shunting yards, which could explain why, today, problems are solved "by hand" with empirically designed methods.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-02262515
Contributor : Ifsttar Cadic <>
Submitted on : Friday, August 2, 2019 - 4:21:18 PM
Last modification on : Saturday, August 3, 2019 - 1:15:53 AM

Identifiers

  • HAL Id : hal-02262515, version 1

Collections

Citation

Samuel Deleplanque, Paola Pellegrini, Joaquin Rodriguez. Optimization of a railway freight yard in real time. EURO 2018, 29th European Conference On Operational Research, Jul 2018, Valence, Spain. 1p. ⟨hal-02262515⟩

Share

Metrics

Record views

11