Speaker:Ye Tian

Location: WebEx

Password: 25gRcQWtVd7

MATE: A Memory-Augmented Time-Expansion Approach for Optimal Trip-Vehicle Matching and Routing in Ride-Sharing

Ride-sharing systems have attracted a great amount of attention in recent years. The heart of most ride-sharing systems is the problem of joint trip-vehicle matching and routing optimization, which is highly challenging and results in this area remain rather limited. In this work, based on our new analytical framework that jointly considers trip-vehicle matching and optimal routing, we develop a memory-augmented time-expansion (MATE) approach for solving large-sized problem instances, which leverages the special problem structure to facilitate approximate (or even exact) algorithm designs. Our results advance the state-of-the-art of intelligent ride-sharing and contribute to the field of sharing economy.

Committee: Jia Liu (major professor), Hridesh Rajan (major professor), Pavan Aduri, Qi Li, and Wensheng Zhang