Optimizing Railway Crew Scheduling at DB Schenker

Jütte, Silke; Albers, Marc; Thonemann, Ulrich W.; Haase, Knut

doi:10.1287/inte.1100.0549

Cited by 35 publications

(13 citation statements)

References 21 publications

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“…In the field of freight railway, Jütte et al [2011] describe the modelling and implementation of a crew scheduling system for the DB Schenker German freight network. Robustness is not a focus of the work, but some robustness considerations are included.…”

Section: Robustness In Crew Planningmentioning

confidence: 99%

A survey on robustness in railway planning

Lusby

Larsen

Bull³

2018

European Journal of Operational Research

107

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Section: Robustness In Crew Planningmentioning

confidence: 99%

A survey on robustness in railway planning

Lusby

Larsen

Bull³

2018

European Journal of Operational Research

107

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show abstract

“…Jütte and Thonemann (2012) divide the CSP into multiple regions and price the assignment of trips to these regions. This procedure is implemented by Jütte et al (2011) for a real-world instance from DB Schenker. The authors show that large-scale instances can be solved in a reasonable computation time.…”

Section: Literaturementioning

confidence: 99%

“…The crew considered work in Sweden, Denmark, or Germany. Papers considering train working regulations related to these countries include Rezanova and Ryan (2010) for a Danish, and Jütte et al (2011) for a German setting.…”

Section: Literaturementioning

confidence: 99%

Integrating Timetabling and Crew Scheduling at a Freight Railway Operator

Bach

Dollevoet

Huisman

2016

Transportation Science

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We investigate to what degree we can integrate a Train Timetabling / Engine Scheduling Problem with a Crew Scheduling Problem. In the Timetabling / Engine Scheduling Problem we determine for each demand a specific time within its time window when the demand should be serviced. Furthermore, we generate engine duties for the demands. In our solution approach for the overall problem, we first obtain an optimal solution for the Timetabling / Engine Scheduling Problem. When solving the Crew Scheduling Problem, we then exploit the fact that numerous optimal, and near optimal solutions exist for the previous problem. We consider all these solutions that can be obtained from the optimal engine schedule by shifting the demands in time, while keeping the order of demands in the engine duties intact. In particular, in the crew scheduling stage it is allowed to re-time the service of demands if the additional cost is outweighed by the crew savings. This information is implemented in a mathematical model for the Crew Scheduling Problem. The model is solved using a column generation scheme. We perform computational experiments based on a case at a freight railway operator, DB Schenker Rail Scandinavia, and show that significant cost savings can be achieved.

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“…Abbink et al (2007) experiment with chronological, graphical, and train line decomposition of the set of trips at Netherlands Railways. Jütte et al (2011) and Jütte and Thonemann (2012) investigate the effects of decomposing the set of trips of a major European railway freight carrier by business units and by geography and departure times. Several of the above studies showed that for certain fields of application, some logical decomposition strategies are more appropriate than others, and choosing the wrong strategy can result in schedules of low solution quality (e.g., Albers 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Despite the use of label filtering and variable fixing as heuristic components, the generated crew schedules of the duty generation algorithm are very close to optimal: for all our real-world test instances, the cost of the generated integer solution schedule exceeded the cost of an optimal integer solution by less than 1 % (see the description of the LB gap below for information on how to calculate a lower bound on the optimal integer objective function value). Details on the crew scheduling solution algorithm that we implemented can be found in Jütte et al (2011). Table 1 presents details on the four instances.…”

Section: Crew Scheduling Solution Phasementioning

confidence: 99%

A graph partitioning strategy for solving large-scale crew scheduling problems

Jütte

Thonemann

2014

OR Spectrum

Self Cite

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Railway crew scheduling deals with generating driver duties for a given train timetable such that all work regulations are met and the resulting schedule has minimal cost. Typical problem instances in the freight railway industry require the generation of duties for thousands of drivers operating tens of thousands of trains per week. Due to short runtime requirements, common solution approaches decompose the optimization problem into smaller subproblems that are solved separately. Several studies have shown that the way of decomposing the problem significantly affects the solution quality. An overall best decomposition strategy for a freight railway crew scheduling problem, though, is not known. In this paper, we present general considerations on when to assign two scheduled train movements to separate subproblems (and when to rather assign them to the same subproblem) and deduct a graph partitioning based decomposition algorithm with several variations. Using a set of real-world problem instances from a major European railway freight carrier, we evaluate our strategy and benchmark the performance of the decomposition algorithm both against a common non-decomposition algorithm and a lower bound on the optimal solution schedule. The test runs show that our decomposition algorithm is capable of producing highquality solution schedules while significantly cutting runtimes compared to the nondecomposition solution algorithm. We are following a "greenfield" approach, where no information on previous schedules is needed. Hence, our approach is applicable to any railway crew scheduling setting, including network enlargement, integration of new customers, etc.

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Optimizing Railway Crew Scheduling at DB Schenker

Cited by 35 publications

References 21 publications

A survey on robustness in railway planning

A survey on robustness in railway planning

Integrating Timetabling and Crew Scheduling at a Freight Railway Operator

A graph partitioning strategy for solving large-scale crew scheduling problems

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