Robust Optimization With Applications In Healthcare Operations Management

Abstract

The combination of an increasingly complex world, the vast proliferation of data, and the pressing need to stay one step ahead of competition has sharpened focus on using analytics and optimization for decision making. There is also a need to computationally exploit the wealth of data available in optimization problems by providing a exible framework for modeling uncertainty that incorporates distributional information, while preserving the computational tractability for practical implementation. As motivated by the importance of such a decision making process, I investigate this procedure under robust optimization and extend the ndings into real applications in health care operations management.