基于深度强化学习求解单线列车调度问题
Solving the single-track train scheduling problem via Deep Reinforcement Learning
AI总结：
针对铁路扰动下调度员决策认知有限的问题，提出两种深度Q学习方法求解单线列车调度问题，性能优于经典线性Q学习，集中式方法对比MILP也有良好表现。
摘要：
铁路网络和车队每天都会遭遇扰动与中断，影响铁路运营的稳定性。由此引发的延误在网络中传播，导致货运和客运的供需不匹配，进而造成服务质量下降。在这类情况下，人工调度员（即行车调度人员）有责任尽最大努力降低对交通的影响。然而，调度员对自身决策的连锁反应的认知深度不可避免地存在局限，尤其是对于其直接管控范围之外的网络区域所受的影响更是如此。近年来，决策科学领域的大量研究致力于开发可自动求解该问题的方法，为调度员应对这项挑战性任务提供支持。本文研究了基于机器学习的问题求解方法，提出了两种不同的深度Q学习方法（分布式与集中式）。数值结果表明，相较于基于矩阵的经典线性Q学习，这些技术性能更优。此外，研究还将集中式方法与混合整数线性规划（MILP）模型进行了对比，取得了值得关注的结果。实验基于美国一级铁路公司提供的数据设计。
英文摘要：
Every day, railways experience disturbances and disruptions, both on the network and the fleet side, that affect the stability of rail traffic. Induced delays propagate through the network, which leads to a mismatch in demand and offer for goods and passengers, and, in turn, to a loss in service quality. In these cases, it is the duty of human traffic controllers, the so-called dispatchers, to do their best to minimize the impact on traffic. However, dispatchers inevitably have a limited depth of perception of the knock-on effect of their decisions, particularly how they affect areas of the network that are outside their direct control. In recent years, much work in Decision Science has been devoted to developing methods to solve the problem automatically and support the dispatchers in this challenging task. This paper investigates Machine Learning-based methods for tackling this problem, proposing two different Deep Q-Learning methods(Decentralized and Centralized). Numerical results show the superiority of these techniques with respect to the classical linear Q-Learning based on matrices. Moreover, the Centralized approach is compared with a MILP formulation showing interesting results. The experiments are inspired by data provided by a U.S. Class 1 railroad.
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