Simulation-Based Replacement Line and Headway Optimization

We present a study on simulation-based replacement line and headway optimization for the Viennese public transportation system. The discussed problem focuses on scheduled closures of subway lines. A genetic algorithm is proposed to design replacement lines and potentially adjust the headways of all lines in the network. Candidate networks are simulated to evaluate their solution quality. The underlying discrete event simulation model has several stochastic elements (e.g., vehicle travel and turning maneuver times). Passenger creation is a Poisson process which uses daily origin destination matrices based on anonymous mobile phone and count data. Vehicles are subject to capacity restrictions. Computational insights are gained from three real-world based test instance. Our problem-specific genetic algorithm creates not only good but also robust solutions by taking stochastic elements into account.