Abstract

EEEIC 2016 - 17th IEEE International Conference on Environment and Electrical Engineering , Florence, Italie, 06-/06/2016 - 08/06/2016; The estimation of running times is essential for the railway timetable definition. The trains travel duration must respect some constraints, which depend on the railway network. Usually, the running times minimize the trains travel duration on the railway network. They are typically calculated without considering the energy consumption. The green transportation has recently gained significant importance. In this paper, the energy consumption in the running times estimation is taken into account by defining an optimization problem to calculate the running times for the railway timetable definition. This optimization problem finds the running times which minimize the energy consumption. Precisely, an algorithm to handle the optimization problem with the integration platform for multiobjective and multi-disciplinary optimization modeFRONTIER is defined. The algorithm is tested on a case study and the results are validated with the OpenTrack Railway Simulation Tool.

Abstract

"jats:p"The article proposes a method for determining the rational motion intensity of specific train traffic flows on railway transport corridors with account for balance of expenses on traction resources and cargo owners. A mathematical model based on stochastic optimization is developed, which allows to optimize, in the conditions of risks, the interval between trailing trains on the railway lines taking into account the limited resources of the traction rolling stock, the capacity of the stations and freight fronts at the cargo destination point. Solving this mathematical model allows to find a balance between the expenses for movement of train traffic flows from different railway lines to their terminal reference station and the expenses of a consignee, subject to the limitations of the technological logistics chain in cargo transportation. For the solution of this mathematical model, a Real-coded Genetic Algorithm (RGA) was used. Document type: Article