This project aims at designing fast mathematical methods that create high quality drafts of timetables for SBB Cargo.
Cargo transportation by railway has significant advantages, first of all economically and ecologically. Furthermore, transport volumes for railway cargo are forecasted to increase by 45% until 2040. In contrast, railway cargo carriers have a long history of financial deficits and dependence on subsidies. To remain competitive and be prepared for the volume growth, SBB Cargo (SBBC), the biggest Swiss carrier, must increase its efficiency and reduce operational costs.
Roughly two thirds of SBBC’s business is in national wagonload transports. This means the transport of groups of only few wagons with different origins and destinations. Since the efficiency on rails is greatest for long distances with long trains, the wagons need to be shunted and bundled together. The timetable is the central element that determines where and when this happens.
This timetable is currently managed manually. Even minor local improvements are very time-consuming and manual global optimization of operational costs stays impossible. This project tackles this problem by designing innovative, fast mathematical methods that create high quality drafts of the timetable. Using these models as automated support tools has the potential to reduce planning efforts by a factor of three. The full potential of a global optimization is estimated at around 5-10% of production costs.
This four-year, four-month project is led by professor Friedrich Eisenbrand, head of the Chair of Discrete Optimization (DISOPT), in collaboration with Carsten Moldenhauer, head of Operations Research & Analytics at SBB Cargo. It is sponsored by Innosuisse.
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Principal investigator |
Prof. Friedrich Eisenbrand |
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Project manager |
SBB Cargo |
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Sponsor |
Innosuisse |
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Period |
2020-2024 |
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Laboratory |
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Partner |
SBB Cargo |