Distributed optimization for real-time railway traffic management

Journal article (2018)

Authors

X. Luan Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
B.H.K. De Schutter Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
A.J.J. van den Boom Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
F. Corman ETH Zürich
G. Lodewijks University of New South Wales
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 X. Luan, B.H.K. De Schutter, A.J.J. van den Boom, F. Corman, G. Lodewijks
DOI
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https://doi.org/10.1016/j.ifacol.2018.07.018
Clustering Decomposition Distributed optimization Alternating direction method of multipliers (ADMM) algorithm Mixed-integer linear programming (MILP) Real-time railway traffic management
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Published Date

2018

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

We introduce a distributed optimization method for improving the computational efficiency of real-time traffic management approaches for large-scale railway networks. We first decompose the whole network into a pre-defined number of regions by using an integer linear optimization approach. For each resulting region, a mixed-integer linear programming approach is used to address the traffic management problem, with micro details of the network and incorporated with the train control problem. For handling the interactions among regions, an alternating direction method of multipliers (ADMM) algorithm based solution approach is developed to solve the subproblem of each region through coordination with the other regions in an iterative manner. A priority rule based solution approach is proposed to generate feasible suboptimal solutions, in case of lack of convergence. Numerical experiments are conducted based on the Dutch railway network to show the performance of the proposed solution approaches, in terms of effectiveness and efficiency. We also show the trade-off between solution quality and computational efficiency.