Deutsche Bahn Schedules Train Rotations Using Hypergraph Optimization

Ralf Borndörfer,Thilo Schang,Steffen Weider,Karsten Kuchenbecker,Thomas Eßer,Michael Peterson,Kai Mohrhagen,Andreas Huck,Hanno Schülldorf,Tobias Therolf,Kerstin Waas,Philipp Nagl,Patrick Frankenberger,Christoph Jobmann,Peter Schütz,Boris Krostitz,Michael Schoch,Markus Reuther

doi:10.1287/inte.2020.1069

Ralf Borndörfer, Thilo Schang + Show 16 more

Open Access

https://doi.org/10.1287/inte.2020.1069

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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 addressed using a novel coarse-to-fine method that implements a hierarchical column generation over three levels of detail. This algorithm is the mathematical core of DB’s fleet employment 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 crossborder operations. FEO is the key to successful tendering in regional transport and to construction site management in daily operations. DB’s planning 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 crossborder operations to the implementation of FEO.

Highlights

The Western European railway network is among the densest in the world and is of major importance for passenger and freight transport in the European Union (Figure 1)
For example, cars are assembled into blocks in step 1, trains are routed in step 2, and locomotives are scheduled in step 3 (Ireland et al 2004, Ahuja et al 2005), often using network flow techniques
Rotations and train compositions must be synchronized to ensure a railroad can cover all timetabled trips at the minimum cost—this is the rolling stock rotation planning problem

Summary

Introduction

The Western European railway network is among the densest in the world and is of major importance for passenger and freight transport in the European Union (Figure 1). This algorithm is the mathematical core of DB’s fleet employment optimization (FEO) system for rolling stock rotation planning. Scheduling railway rolling stock requires handling the composition of vehicles into trains.

Results

Conclusion