Operations & Supply Chain Management

The development of predictive models in healthcare settings has been growing; one such area is the prediction of patient arrivals to the Emergency Department (ED). The general premise behind these works is that such models may be used to help manage an ED which consistently faces high congestion. In this work, we propose a class of proactive policies which utilizes future information of potential patient arrivals to effectively manage admissions into an ED while reducing waiting times for patients who are eventually treated.

We consider a non-stationary variant of a sequential stochastic optimization problem, where the underlying cost functions may change along the horizon. We propose a measure, termed variation budget, that controls the extent of said change, and study how restrictions on this budget impact achievable performance. We identify sharp conditions under which it is possible to achieve long- run-average optimality and more refined performance measures such as rate optimality that fully characterize the complexity of such problems.

Researchers typically invoke theoretical assumptions to estimate mark-ups. Instead, we directly obtain mark-ups by surveying Pakistani soccer-ball producers.

This work examines the process of admission to a hospital’s intensive care unit (ICU). ICUs currently lack systematic admission criteria, largely because the impact of ICU admission on patient outcomes has not been well quantified. This makes evaluating the performance of candidate admission strategies difficult. Using a large patient-level data set of more than 190,000 hospitalizations across 15 hospitals, we first quantify the cost of denied ICU admission for a number of patient outcomes.

This work examines the process of admission to a hospital’s intensive care unit (ICU). ICUs currently lack systematic admission criteria, largely because the impact of ICU admission on patient outcomes has not been well quantified. This makes evaluating the performance of candidate admission strategies difficult. Using a large patient-level data set of more than 190,000 hospitalizations across 15 hospitals, we first quantify the cost of denied ICU admission for a number of patient outcomes.

In a number of service systems, there can be substantial latitude to vary service rates. However, although speeding up service rate during periods of congestion may address a present congestion issue, it may actually exacerbate the problem by increasing the need for rework. We introduce a state-dependent queuing network where service times and return probabilities depend on the “overloaded” and “underloaded” state of the system.

Many factors introduce the prospect of changes for the demand environment that a firm faces, with the specifics of such changes not necessarily known in advance. If and when realized, such changes affect the delicate balance between demand and supply and thus should be anticipated to the extent possible. We study the dynamic pricing problem of a retailer facing the prospect of a change in the demand function during a finite selling season with no inventory replenishment opportunity.

We propose a simple approach to dynamic multi-period portfolio choice with quadratic transaction costs. The approach is tractable in settings with a large number of securities, realistic return dynamics with multiple risk factors, many predictor variables, and stochastic volatility. We obtain a closed-form solution for a trading rule that is optimal if the problem is restricted to a broad class of strategies we define as "linearity generating strategies" (LGS).