Knowledge that Transforms

In this research, we apply robust optimization (RO) to the problem of locating facilities in a network facing uncertain demand over multiple periods. We consider a multi‐period fixed‐charge network location problem for which we find (1) the number of facilities, their location and capacities, (2) the production in each period, and (3) allocation of demand to facilities. Using the RO approach we formulate the problem to include alternate levels of uncertainty over the periods. We consider two models of demand uncertainty: demand within a bounded and symmetric multi‐dimensional box, and demand within a multi‐dimensional ellipsoid. We evaluate the potential benefits of applying the RO approach in our setting using an extensive numerical study. We show that the alternate models of uncertainty lead to very different solution network topologies, with the model with box uncertainty set opening fewer, larger facilities. Through sample path testing, we show that both the box and ellipsoidal uncertainty cases can provide small but significant improvements over the solution to the problem when demand is deterministic and set at its nominal value. For changes in several environmental parameters, we explore the effects on the solution performance.

This paper considers pricing and remanufacturing strategy of a firm that decides to offer both new and remanufactured versions of its product in the market and is concerned with demand cannibalization. We present a model of demand cannibalization and a behavioral study that estimates a key modeling parameter: a fraction of consumers who switch from new to remanufactured product. As we show, this fraction has an inverted‐U shape, and, thus, the underlying consumer behavior cannot be modeled using the standard methodologies that rely on consumers' willingness to pay (WTP). We find that by incorporating the inverted‐U‐shaped consumer behavior, the firm remanufactures under broader conditions, charges a much lower price, and typically remanufactures more units—leading to an increase of profits from remanufacturing by up to a factor of two as compared with making decisions based on the WTP only. Lastly, we find that the behavior of the low‐price market segment plays an important role because the firm reacts to it differently than the WTP‐based logic would suggest.

This paper reviews the general problem of surgical scheduling. We organize the literature based on the time frame or planning horizon of the schedule into six categories: capacity planning, process reengineering/redesign, the surgical services portfolio, procedure duration estimation, schedule construction, and schedule execution, monitoring, and control. We survey past work and suggest topics for potential future research in each of those areas.

We consider the distribution planning problem in the motion picture industry. This problem involves forecasting theater‐level box office revenues for a given movie and using these forecasts to choose the best locations to screen a movie. We first develop a method that predicts theater‐level box office revenues over time for a given movie as a function of movie attributes and theater characteristics. These estimates are then used by the distributor to choose where to screen the movie. The distributor's location selection problem is modeled as an integer programming‐based optimization model that chooses the location of theaters in order to optimize profits. We tested our methods on realistic box office data and show that it has the potential to significantly improve the distributor's profits. We also develop some insights into why our methods outperform existing practice, which are crucial to their successful practical implementation.

This paper examines the effect of the common practice of reserving slots for urgent patients in a primary health care practice on two service quality measures: the average number of urgent patients that are not handled during normal hours (either handled as overtime, referred to other physicians, or referred to the emergency room) and the average queue of non‐urgent or routine patients. We formulate a stochastic model of appointment scheduling in a primary care practice. We conduct numerical experiments to optimize the performance of this system accounting for revenue and these two service quality measures as a function of the number of reserved slots for urgent patients. We compare traditional methods with the advanced‐access system advocated by some physicians, in which urgent slots are not reserved, and evaluate the conditions under which alternative appointment scheduling mechanisms are optimal. Finally, we demonstrate the importance of patient arrival dynamics to their relative performance finding that encouraging routine patients to call for same‐day appointments is a key ingredient for the success of advanced‐access.