Multi-objective simulation optimization for complex urban mass rapid transit systems

David Schmaranzer (), Roland Braune () and Karl F. Doerner ()
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David Schmaranzer: University of Vienna
Roland Braune: University of Vienna
Karl F. Doerner: University of Vienna

Annals of Operations Research, 2021, vol. 305, issue 1, No 19, 449-486

Abstract: Abstract In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We propose a two-phase algorithm that combines the single-objective covariance matrix adaptation evolution strategy with a problem-specific multi-directional local search. With a computational study, we compare our proposed method against both a multi-objective covariance matrix adaptation evolution strategy and a non-dominated sorting genetic algorithm. The integrated discrete event simulation model has several stochastic elements. Fluctuating demand (i.e., creation of passengers) is driven by hourly origin-destination-matrices based on mobile phone and infrared count data. We also consider the passenger distribution along waiting platforms and within vehicles. Our two-phase optimization scheme outperforms the comparative approaches, in terms of both spread and the accuracy of the resulting Pareto front approximation.

Keywords: Multi-objective optimization; Simulation optimization; Headway optimization; Transit network frequencies setting problem; Population-based algorithms; Multi-directional local search; Public transportation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10479-019-03378-w

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