Towards eco‐aware timetabling: evolutionary approach and cascading initialisation strategy for the bi‐objective optimisation of train running times

In railway planning, the timetabling step needs, as input, the train running times, which are calculated from a train dynamic model. Usually, this model determines the most energy‐efficient train trajectory for a predefined time. However, this time may not correspond to the timetable‐makers’ needs. They should have the choice among a set of solutions, more or less energy‐consuming. This study proposes a method capable of producing a set of alternative running times with the associated mechanical energy required. To this end, the authors’ contribution is to set up an efficient evolutionary multi‐objective algorithm builds a set of well‐spread and diversified solutions which approximate a Pareto front. The solutions are all compromises between running time and energy‐consumption, the two minimisation objectives concurrently optimised. Given that an evolutionary algorithm is strongly dependent on the initialisation phase, the efficiency of the algorithm is improved through a specific and original mechanism connecting multiple initialisations in cascade in order to accelerate the convergence towards the best solutions. A set of results obtained on randomly‐generated instances is analysed and discussed.