Green Locomotive Assignment Problem with Data-driven Energy Consumption

Aktivität: VorträgeVortragScience to Science


Since rail freight generates significantly less greenhouse gases per tonne kilometer than road freight, it is a promising alternative. We propose the Green Locomotive Assignment Problem, which minimizes the total energy consumption of a heterogenous locomotive fleet over the entire planning horizon when assigning them to fixed scheduled trains. In the model, energy consumption can be reduced by allowing locomotives to be attached to scheduled trains as passive rolling stock elements and by allowing multiple locomotives to run as a consist where one active locomotive pulls the remaining locomotives. In this way, locomotives can be moved to the station where they are needed next with by using less energy. The energy consumption in the objective function is derived from the Davis equation, which is commonly used to calculate energy consumption in rail transport, since the amount of energy required to move a train is proportional to the resistance acting on the train. The model takes into account the different energy consumption profiles of the different types of locomotives and the energy intensity of the track sections due to the gradient profile. In addition, the objective accounts for the regenerative braking energy generated during each braking operation, assuming that the different locomotive types have different capacities to generate this energy. Using information from actual train schedules, locomotive energy consumption, and actual rail line distance and gradient data, multiple linear regression models are used to predict new resistance coefficients of the Davis equation for each locomotive type. We will demonstrate the potential of our model using a case study for the Austrian Railway network on problem instances generated from real-world data.
Zeitraum28 Aug. 2023
EreignistitelOR 2023
OrtHamburg, Deutschland, HamburgAuf Karte anzeigen