Abstract
This paper introduces decomposition and distributed optimization approaches for the
real-time railway traffic management problem considering microscopic infrastructure
characteristics, aiming at an improved computational efficiency when tackling
large-scale railway networks.
Based on the nature of the railway traffic management problem, we consider three
decomposition methods, namely a geography-based (GEO) decomposition, a train-based (TRA)
decomposition, and a time-interval-based (TIN) decomposition, in order to partition the
large railway traffic management optimization problem into several subproblems. In
particular, an integer linear programming (ILP) model is developed to generate the
optimal GEO solution, with the objectives of minimizing the number of interconnections
among regions and of balancing the size of regions. The decomposition creates couplings
among the subproblems, in terms of either capacity usage or transit time consistency;
therefore the whole problem gets a non-separable structure. To handle the couplings, we
introduce three distributed optimization approaches, namely an Alternating Direction
Method of Multipliers (ADMM) algorithm, a priority-rule-based (PR) algorithm, and a
Cooperative Distributed Robust Safe But Knowledgeable (CDRSBK) algorithm, which operate
iteratively.
We test all combinations of the three decomposition methods and the three distributed
optimization algorithms on a large-scale railway network in the South-East of the
Netherlands, in terms of feasibility, computational efficiency, and optimality. Overall
the CDRSBK algorithm with the TRA decomposition performs best, where high-quality
(optimal or near-optimal) solutions can be found within 10 s of computation time.
Keywords
Real-time traffic management, Decomposition, Distributed
optimization, Large-scale railway network
Highlights
• We address the real-time railway traffic management for large-scale networks
microscopically.
• We propose three decomposition methods, based on geography, trains, and time
intervals,
to partition a large-scale problem into some small-scale subproblems.
• We present three distributed optimization approaches, namely ADMM, PR, and CDRSBK, for
achieving the coordination of subproblems.
• The proposed CDRSBK algorithm in the case of the train-based decomposition can find an
optimal or near-optimal solution within 10 s of computation time.
原文传递： https:/www.sciencedirect.com/science/article/pii/S0191261520303908
