论文标题
SUPAID：一种基于规则挖掘的方法，用于为车队管理系统中的主管自动推出决策援助
SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems
论文作者
论文摘要
推出车辆的决定对于车队管理公司至关重要，因为错误的决定可能会导致旅途中的额外维护和失败成本。随着大量数据的可用性和机器学习技术的进步，主管的推出决策可以有效地自动化，并且在主管做出的决策中所掌握的错误。在本文中，我们提出了一种新颖的学习算法supaid，该算法在主管上的自然“单向效率”假设下，使用规则挖掘方法根据其推出的可行性来对车辆进行排名，从而帮助防止主管做出甲虫决定。我们对来自美国城市公共交通机构的真实数据的实验结果表明，拟议的方法可以节省大量成本。
The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey. With the availability of large amount of data and advancement of machine learning techniques, the rollout decisions of a supervisor can be effectively automated and the mistakes in decisions made by the supervisor learnt. In this paper, we propose a novel learning algorithm SUPAID which under a natural 'one-way efficiency' assumption on the supervisor, uses a rule mining approach to rank the vehicles based on their roll-out feasibility thus helping prevent the supervisor from makingerroneous decisions. Our experimental results on real data from a public transit agency from a city in U.S show that the proposed method SUPAID can result in significant cost savings.
