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Decomposition and distributed optimization of real-time traffic management for large-scale railway networks

机译：大型铁路网络实时交通管理分解和分布式优化

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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. (C) 2020 Elsevier Ltd. All rights reserved.
机译：本文介绍了考虑微观基础设施特征的实时铁路交通管理问题的分解和分布式优化方法，旨在提高计算大规模铁路网络的计算效率。基于铁路交通管理问题的性质，我们认为三个分解方法，即基于地理的（Geo）分解，基于列车（TRA）分解，以及基于时间间隔（TIN）分解，以便将大型铁路交通管理优化问题分配给几个子问题。特别地，开发了一个整数线性编程（ILP）模型以产生最佳地理解决方案，其目标最小化区域之间的互连数量和平衡区域大小。分解在子问题之间创建耦合，就容量使用或传输时间一致性;因此，整个问题得到了不可分离的结构。为了处理耦合，我们介绍了三种分布式优化方法，即乘法器（ADMM）算法的交替方向方法，基于优先级规则的（PR）算法，以及操作的合作分布式鲁棒安全但知识迭代地。在可行性，计算效率和最优性方面，测试三种分解方法的所有组合和三个分解方法和三个分布式优化算法的大规模铁路网络。总的来说，具有TRA分解的CDRSBK算法最佳，其中高质量（最佳或接近最佳）解决方案可以在10秒内找到。 （c）2020 elestvier有限公司保留所有权利。

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