Transit agencies world-wide face tightening budgets. To maintain quality of
service while cutting costs, efficient transit network design is essential. But
planning a network of public transit routes is a challenging optimization
problem. The most successful approaches to date use metaheuristic algorithms to
search through the space of solutions by applying low-level heuristics that
randomly alter routes in a network. The design of these low-level heuristics
has a major impact on the quality of the result. In this paper we use deep
reinforcement learning with graph neural nets to learn low-level heuristics for
an evolutionary algorithm, instead of designing them manually. These learned
heuristics improve the algorithm's results on benchmark synthetic cities with
70 nodes or more, and obtain state-of-the-art results when optimizing operating
costs. They also improve upon a simulation of the real transit network in the
city of Laval, Canada, by as much as 54% and 18% on two key metrics, and offer
cost savings of up to 12% over the city's existing transit network.

利用深度强化学习和图神经网络学习低级启发式算法可有效优化公共交通网络设计，在合成和现实城市场景中均取得最佳结果并降低成本。

使用深度强化学习学习公共交通网络设计和改进的启发式方法

Learning Heuristics for Transit Network Design and Improvement with Deep  Reinforcement Learning

In the digital transformation era, integrating digital technology into every
aspect of banking operations improves process automation, cost efficiency, and
service level improvement. Although logistics for ATM cash is a crucial task
that impacts operating costs and consumer satisfaction, there has been little
effort to enhance it. Specifically, in Vietnam, with a market of more than
20,000 ATMs nationally, research and technological solutions that can resolve
this issue remain scarce. In this paper, we generalized the vehicle routing
problem for ATM cash replenishment, suggested a mathematical model and then
offered a tool to evaluate various situations. When being evaluated on the
simulated dataset, our proposed model and method produced encouraging results
with the benefits of cutting ATM cash operating costs.

在数字化转型时代，将数字技术整合到银行业务的各个方面可以提高流程自动化、成本效率和服务水平。本文针对 ATM 现金补充的物流问题，提出了广义车辆路径问题的数学模型，并提供了评估各种情况的工具。在模拟数据集上评估时，我们提出的模型和方法取得了令人鼓舞的结果，可以削减 ATM 现金运营成本。