This paper deals with a basic issue: How does one approach the problem of designing the "right" objective for a given resource allocation problem? The notion of what is right can be fairly nebulous; we consider two issues that we see as key: efficiency and fairness. We approach the problem of...

We study the interplay between financial covenants and the operational decisions of a retailer that obtains financing through a secured, inventory-based lending contract. We characterize how leverage affects dynamic inventory decisions, and find that it can lead to surprising non-threshold...

Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the "right" model of choice to use. Because models of choice are inherently high-dimensional...

We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the "price") of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as...

We develop a new model for studying the phenomenon of congestion in a transient environment, focusing on the problem of aircraft landings at a busy "hub" airport. Our model is based on a Markov/semi-Markov treatment of changes in the weather, the principal source of uncertainty governing service...

We address the performance optimization problem in a single-station multiclass queueing network with changeover times by means of the achievable region approach. This approach seeks to obtain performance bounds and scheduling policies from the solution of a mathematical program over a relaxation...

We address the problem of scheduling a multi-station multiclass queueing network (MQNET) with server changeover times to minimize steady-state mean job holding costs. We present new lower bounds on the best achievable cost that emerge as the values of mathematical programming problems (linear,...

We develop a mathematical programming approach for the classical PSPACE - hard restless bandit problem in stochastic optimization. We introduce a hierarchy of n (where n is the number of bandits) increasingly stronger linear programming relaxations, the last of which is exact and corresponds to...

Includes bibliographical references (p. 35-36).