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RailBeijing 2021, 9th International Conference on Railway Operations Modelling and Analysis (ICROMA), Pékin, CHINE, 03-/11/2021 - 07/11/2021; This professional paper presents the algorithmic framework for automatic timetable generation which is actually under development within the research project « Tools for mathematical optimization of strategic railway timetable models » funded by the Norwegian Railway Directorate. This framework is composed by two main components, which tackle the Train Timetabling Problem in a macroscopic model of infrastructure and operations. The first stage is a Multi-Objective Ant Colony Optimization (MOACO) algorithm which produces a Pareto Optimal Set of solutions. These are timetables optimized with respect to four main objectives, namely: the total travel time, the total energy consumption, the timetable robustness and the total number of scheduled trains. The MOACO treats conflict constraints as soft ones and produces strictly periodic traffic patterns. The second stage is a MILP formulation tackled with a commercial solver. It refines timetables produced by MOACO, looking for improvements in the neighbourhood of the solutions provided by the latter. MILP solutions are conflict-free timetables and can profit of given tolerances on trains periodicity. The MILP stage is used as a final refinement of the results as well as an intermediate local search during MOACO iterations.

IRSA 2019 : Tagungsband, Proceedings : 2nd International Railway Symposium Aachen, Aachen, Germany, 26-28 November 2019 / Herausgeber Univ.-Prof. Dr. ir. Dr. h. c. De Doncker, Rik W. (Institut für Stromrichtertechnik und elektrische Antriebe (ISEA), Aachen), Univ.-Prof. Dr.-Ing. Nießen, Nils (Verkehrswissenschaftliches Institut (VIA), Aachen), Univ.-Prof. Dr.-Ing. Schindler, Christian (Institut für Schienenfahrzeuge und Transportsysteme (IFS)/Institute of Rail Vehicles and Transport Systems 2nd International Railway Symposium Aachen, IRSA 2019, Aachen, Germany, 26 Nov 2019 - 28 Nov 2019; Aachen : [RWTH Aachen] 261-287 (2020)., Published by [RWTH Aachen], Aachen