Operations & Supply Chain Management

This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers.

This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers.

We study a seller that starts with an initial inventory of goods, has a target horizon over which to sell the goods, and is subject to a set of financial milestone constraints on the revenues and sales that need to be achieved at different time points along the sales horizon. We characterize the revenue maximizing dynamic pricing policy for the seller and highlight the effect of revenue and sales milestones on its structure.

Since its inception in 2006, the New York City (NYC) Task Force for Patients with Burns has continued to develop a city-wide and regional response plan that addressed the triage, treatment, transportation of 50/million (400) adult and pediatric victims for 3 to 5 days after a large-scale burn disaster within NYC until such time that a burn center bed and transportation could be secured. The following presents updated recommendations on these planning efforts. Previously published literature, project deliverables, and meeting documents for the period of 2009–2010 were reviewed.

Many mobile applications retrieve content from remote servers via user generated queries. Processing these queries is often needed before the desired content can be identified. Processing the request on the mobile devices can quickly sap the limited battery resources. Conversely, processing user queries at remote servers can have slow response times due communication latency incurred during transmission of the potentially large query. We evaluate a network-assisted mobile computing scenario where mid-network nodes with “leasing” capabilities are deployed by a service provider.

Based on a sample of highly leveraged Austrian ski hotels undergoing debt restructurings, we show that reducing a debt overhang leads to a significant improvement in operating performance. Changes in leverage in the debt restructurings are instrumented with Unexpected Snow, which captures the extent to which a ski hotel experienced unusually good or bad snow conditions prior to the debt restructuring.

The game of golf involves many different types of shots: long tee shots (typically hit with a driver), approach shots to greens, shots from the sand, putts on the green, and others.  While it is easy to determine the winner in a golf tournament by counting strokes, it is not easy to assess which factors most contributed to the victory.  In this paper we apply an analysis based on strokes gained (previously termed shot value) to assess the performance of golfers in different parts of the game of golf.  Strokes gained is a simple and intuitive measure of the contribution 

We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high- dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural “projection” of a well-studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go function.

We propose a method for estimating substitute and lost demand when only sales and product availability data are observable, not all products are displayed in all periods (e.g., due to stock-outs or availability controls), and the seller knows its aggregate market share. The model combines a multinomial logit (MNL) choice model with a non-homogeneous Poisson model of arrivals over multiple periods. Our key idea is to view the problem in terms of primary (or first-choice) demand; that is, the demand that would have been observed if all products had been available in all periods.