The train timetabling problem in liberalized railway markets represents a
challenge to the coordination between infrastructure managers and railway
undertakings. Efficient scheduling is critical in maximizing infrastructure
capacity and utilization while adhering as closely as possible to the requests
of railway undertakings. These objectives ultimately contribute to maximizing
the infrastructure manager’s revenues. This paper sets out a modular simulation
framework to reproduce the dynamics of deregulated railway systems. Ten
metaheuristic algorithms using the MEALPY Python library are then evaluated in
order to optimize train schedules in the liberalized Spanish railway market.
The results show that the Genetic Algorithm outperforms others in revenue
optimization, convergence speed, and schedule adherence. Alternatives, such as
Particle Swarm Optimization and Ant Colony Optimization Continuous, show slower
convergence and higher variability. The results emphasize the trade-off between
scheduling more trains and adhering to requested times, providing insights into
solving complex scheduling problems in deregulated railway systems.

Cet article explore les excursions dans le temps et leurs implications.

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