Title
Deutsche Bahn Schedules Train Rotations Using Hypergraph Optimization
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.
Year
DOI
Venue
2021
10.1287/inte.2020.1069
INFORMS JOURNAL ON APPLIED ANALYTICS
Keywords
DocType
Volume
transportation, rail, scheduling, vehicles, large-scale systems, integer programming, graphs, hypergraphs, Edelman Award
Journal
51
Issue
ISSN
Citations 
1
2644-0865
0
PageRank 
References 
Authors
0.34
0
18