The Norway Railway Directorate (JDir) considers that the possibility to automatically generate sets of timetables complying with given technical and operational constraints would provide significant time savings when performing strategic timetabling. This would help the planners to better understand which are the several best ways to exploit available capacity.
Efficient automatic timetable generation is one of the current frontiers in Operation Research applied to the railway sector. At TRENOlab, we tackled this problem with our own original approach, developed together with the researchers of Gustave Eiffel University (France). An innovative Multi Objective Ant Colony Optimization (MO-ACO) algorithm has been designed to produce Pareto-optimal sets of timetables optimized with regard to relevant KPIs, as travel times, energy consumptions, stability, etc. This algorithm is embedded into a standalone tool which communicates with a dedicated TRENOplus module. The latter allows the user to effectively define the relevant input dataset and to navigate and analyse the produced timetables.