SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems
This addresses cost reduction for fleet management companies by automating supervisor decisions, but it is incremental as it builds on existing rule mining and learning techniques.
The paper tackles the problem of automating vehicle rollout decisions in fleet management to reduce maintenance and failure costs, proposing the SUPAID algorithm which uses rule mining to rank vehicles and shows significant cost savings on real transit data.
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.