Operations & Supply Chain Management

We consider a system of parallel queues where arriving service tasks are buffered, according to type. Available service resources are dynamically configured and allocated to the queues to process the tasks. At each point in time, a scheduler chooses a service configuration across the queues, in response to queue backlogs. Switching from one service configuration to another incurs a setup time, during which idling occurs and service bandwidth is lost. Such setup times are inherent in manufacturing and computer systems.

The role of assortment planning and pricing in shaping sales and profits of retailers is well documented and studied in monopolistic settings. However, such a role remains relatively unexplored in competitive environments. In this paper, we study equilibrium behavior of competing retailers in two settings: i.) when prices are exogenously fixed, and retailers compete in assortments only; and ii.) when retailers compete jointly in assortment and prices.

A new class of online services allows internet media sites to direct users from articles they are currently reading to other content they may be interested in. This process creates a "browsing path'' along which there is potential for repeated interaction between the user and the provider, giving rise to a dynamic optimization problem. A key metric that often underlies this recommendation process is the click-through rate (CTR) of candidate articles.

This paper examines the role of a hospital Step-Down Unit (SDU) on patient flows and patient outcomes. An SDU provides an intermediate level of care for semi-critically ill patients who are not sick enough to require intensive care but not stable enough to be treated in the general medical/surgical ward (ward). Using data from 10 hospitals from a single hospital network, we use an instrumental variable approach to estimate the impact on patient outcomes of routing patients to the SDU following Intensive Care Unit (ICU) discharge.

Objectives: To employ automated bed data to examine whether ICU occupancy influences ICU admission decisions and patient outcomes.

Design: Retrospective study using an instrumental variable to remove biases from unobserved differences in illness severity for patients admitted to ICU.

Setting: Fifteen hospitals in an integrated healthcare delivery system in California.

Patients: Seventy thousand one hundred thirty-three episodes involving patients admitted via emergency departments to a medical service over a 1-year period between 2008 and 2009.

In hospitals, Step Down Units (SDUs) provide an intermediate level of care between the Intensive Care Units (ICUs) and the general medical-surgical wards. Because SDUs are less richly staffed than ICUs, they are less costly to operate; however, they also are unable to provide the level of care required by the sickest patients. There is an ongoing debate in the medical community as to whether and how SDUs should be used. On one hand, an SDU alleviates ICU congestion by providing a safe environment for post-ICU patients before they are stable enough to be transferred to the general wards.

Ad Exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real-time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a pre-specified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in Ad Exchanges and the implications for publishers' decisions.

We consider a multi-period single product pricing problem with an unknown demand curve. The seller's objective is to adjust prices in each period so as to maximize cumulative expected revenues over a given finite time horizon; in so doing, the seller needs to resolve the tension between learning the unknown demand curve, and earning revenues by solving the dynamic optimization problem.

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.