Over 400 cities around the world have deployed or have plans to deploy a bike sharing system. However, the factors that drive their usage and the amount of rebalancing they require are not known precisely. A knowledge of these factors would allow cities to design or modify their systems to increase usage while lowering rebalancing costs. We collect station-level occupancy data from two cities a...

States are shifting Medicaid spending on long-term services and supports from institutional to home and community-based services, a process known as rebalancing. Using fifteen years of state expenditure data, a statistical model was developed to assess the effect of rebalancing on overall spending for long-term services and supports. The model indicates that spending is affected by the way reba...

Multi-period guarantees are often embedded in life insurance contracts. In this paper we consider the problem of hedging these multiperiod guarantees in the presence of transaction costs. We derive the hedging strategies for the cheapest hedge portfolio for a multi-period guarantee that with certainty makes the insurance company able to meet the obligations from the insurance policies it has is...

Institutional fund managers generally rebalance using ad hoc methods such as calendar basis or tolerance band triggers. We propose a different framework that quantifies the cost of a rebalancing strategy in terms of risk-adjusted returns net of transaction costs. We then develop an optimal rebalancing strategy that actively seeks to minimize that cost. We use certainty equivalents and the trans...

Relaxed balancing means that, in a dictionary stored as a balanced tree, the necessary rebalancing after updates may be delayed. This is in contrast to strict balancing meaning that rebalancing is performed immediately after the update. Relaxed balancing is important for efficiency in highly dynamic applications where updates can occur in bursts. The rebalancing tasks can be performed gradually...

As bike-share systems expand in urban areas, the wealth of publicly available data has drawn researchers to the novel operational challenges these systems face. The key challenge is to meet user demand for available bikes and docks by rebalancing the system. This paper addresses the optimization problems that arise from these efforts. In particular, we provide new models to guide truck routing ...

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...

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...