Train Traffic Simulation with Optimization

The aim of this thesis was to create a train traffic simulation system with optimization. The created system can serve to find an optimal schedule for trains in a fixed railway network. An input of the presented system consists of a railway network, passangers' requirements, tariff rate zones, and a schedule. The output of the simulation is a profit. The profit depending on the schedule defines the objective function whose maximum we are tying to find. Two different algorithms were used to find the maximum profit - hill climbing and differential evolution method. We show an experimental comparison of both algorithms in this thesis