In this paper we develop methods for maximizing the throughput of a mobility-on-demand urban transportation system. We consider a finite group of shared vehicles, located at a set of stations. Users arrive at the stations, pick-up vehicles, and drive (or are driven) to their destination station where they drop-off the vehicle. When some origins and destinations are more popular than others, the...

This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not le...

We consider binary search trees, where rebalancing transformations need not be connected with updates but may be delayed. For standard AVL tree rebalancing, we prove that even though the rebalancing operations are un-coupled from updates, their total number is bounded by O(M log(M +N)), where M is the number of updates to an AVL tree of initial size N. Hence, relaxed balancing of AVL trees come...

In this paper we present queueing-theoretical methods for the modeling, analysis, and control of autonomous mobilityon-demand (MOD) systems wherein robotic, self-driving vehicles transport customers within an urban environment and rebalance themselves to ensure acceptable quality of service throughout the network. We first cast an autonomous MOD system within a closed Jackson network model with...

Portfolio management starts with asset allocation. There is a consensus that asset allocation plays an important role in determining portfolio performance (Arshanapalli, Coggin & Nelson, 2001). Active portfolio management implies the rebalancing of the existing portfolio by buying and selling assets. The aim of rebalancing is to improve the performance of the managed portfolio by adjusting it t...

Robust balancing is a technique for maintaining generalized B-trees with a cumulative rebalancing cost that is asymptotically linear. It is especially significant in conjunction with fingers, which can make cumulative search cost linear. We define a new family of robust balancing algorithms which includes algorithms with sublinear rebalancing cost, extending previous work that has shown linear ...