We deal with problems where we have to make decisions over time as new information is arriving. Most of our problems involve complex vectors of decisions (managing fleets of vehicles, allocating research dollars to projects, planning energy investments or the use of current energy resources) in the presence of different sources of uncertainty. We begin with a desire to understand how to control these systems, and then use this information to develop robust designs.

Our research can be divided between fundamental research into new methods, and the application of these methods to a range of applications. Our methodological research all falls under the broad umbrella of stochastic optimization, with a special focus on the modeling and algorithmic framework of approximate dynamic programming. Recently, we became involved in a closely related area that we are calling optimal learning, which addresses the problem of efficiently collecting information. This research was recently applied to a problem in drug discovery which recently received recognition in the new "Doing Good with Good OR" competition (click here for more information). Our applied and computational research is supported by a strong program of theoretical research.

Our application areas are varied. We have a long history of working in transportation and logistics, which introduced us to the challenge of modeling complex operations, and solving resource allocation problems under uncertainty. These problems often involve either modeling the operations of large control centers, or providing tools to help people within control centers, such as the control center at Netjets to the right. We recently won the 2009 Daniel H. Wagner prize for our use of approximate dynamic programming at Schneider National (click here for more information).

We are also heavily involved in research in energy, with problems spanning energy policy models, optimization of energy R&D, planning the placement of wind farms, and optimizing the control and design of storage portfolios.

Another area of application is in health, where our research has spanned the study of policies to handle HIV/AIDS and drug-resistent tuberculosis, the management of vaccines and the design of testing policies for cardiovascular disease. We are currently involved in an exciting project which involves the application of our optimal learning research to drug discovery to assist cancer research.

For a perspective on doing academic research with industry, see the article in the August, 2009 issue of OR/MS Today, which is available here.

I hope you find the material interesting, and perhaps useful. If you have any questions, please contact me.

Warren B. Powell

powell@princeton.edu

(c) Warren B. Powell, 1997-2009