How can data be transformed into better decisions for an uncertain future? Using mathematical optimization, this book shows how data can inform practical decision-making when resources are limited and outcomes are uncertain. It develops deterministic and stochastic optimization models for resource allocation, with particular emphasis on fairness, stochastic programming, and chance constraints. Applications in healthcare and energy address facility location, resource allocation, intervention timing, renewable energy integration, and unit commitment. The book also examines computational methods for large-scale problems, including stronger formulations, Lagrangian methods, and regularization techniques. Throughout, practical problems motivate the theory rather than the reverse. The central message is simple: good data alone do not lead to good decisions. Objectives, constraints, uncertainty, trade-offs, and consequences must also be represented clearly.
"With a unique perspective on data-driven decision making, Dr. Singh takes the reader on a tour through the many real-world applications he worked on..." (Professor Guzin Bayraksan, Integrated Systems Engineering Department, The Ohio State University)
"Singh systematically develops this philosophy, pairing formal chance-constrained methodologies with concrete applications in public health resource allocation and renewable energy dispatch..." (Professor Yinyu Ye, K. T. Li Professor of Engineering (Emeritus), Stanford University)
