Simulation-based Headway Optimization for a Subway Network: A Performance Comparison of Population-based Algorithms

We present a study on simulation-based optimization for the Viennese subway system. The underlying discrete event simulation model has several stochastic elements such as: time-dependent demand, passenger transfer and travel times as well as vehicle travel and crossover maneuver times. Passenger creation is a Poisson process which uses hourly origin-destination-matrices based on mobile phone data. The number of waiting passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms is considered. There are trade-offs between the customer’s view (e.g. waiting time) and the public transportation provider’s view (e.g. fleet mileage). This bi-objective optimization problem is transformed into a single-objective one by normalization and scalarization. The goal is to find optimal time-dependent headways. Computational experience is gained from 48 test instances which are based on real-world data. Several population-based evolutionary algorithms were applied. The covariance matrix adaptation evolution strategy (CMA-ES) performed best.