Public Abstract: "Objective Operational Learning and Applications"
Andrew Lim and J. George Shanthikumar
The research objective of this award is to study a general approach called "Objective Operational Learning" for estimating an objective function and optimizing a stochastic system. The essential feature of this approach is that it is a hybrid between classical model-based approaches to stochastic optimization and purely non-parametric data driven methods that require minimal assumptions. Its advantage is that it allows the decision maker to incorporate structural knowledge of the system (which may only be partially correct) into the initial model, but to become increasingly data-driven and less dependent on these initial assumptions as the data set increases in size. The goal of the research is to show how objective operational learning can be applied to problems of interest in operations research and management science, and to establish theoretical properties such as small sample performance and asymptotic convergence of this approach. If successful, the research will lead to the development of new data driven methods for optimizing stochastic systems that perform well when the size of the data set is small but with attractive large sample properties, and insight into how these methods can be used through a number of case studies involving applied problems of interest to industry. Applications of interest include (but are not restricted to) staff and patient scheduling applications in healthcare and service systems, pricing and revenue management problems, and inventory control. Outcomes of research will be disseminated through journal and conference publications and research presentations, undergraduate research opportunities, and advanced undergraduate and graduate level courses and seminars.
