This book deals with decision making in environments of significant data uncertainty, with particular emphasis on operations and production management applications. For such environments, we suggest the use of the robustness approach to decision making, which assumes inadequate knowledge of the decision maker about the random state of nature and develops a decision that hedges against the worst contingency that may arise. Robust Discrete Optimization is a comprehensive mathematical programming framework for robust decision making. This book takes a giant first step in presenting decision support tools and solution methods for generating robust decisions in a variety of interesting application environments such as: linear programming, assignment problems, shortest paths, minimum spanning trees, knapsack problems, resource allocation, scheduling, production planning, location, inventory, layout planning, network design, and international sourcing. Beyond theoretical results, the book provides many suggestions and useful advice to the practitioners of the robustness approach. Emphasis is placed upon the assessment of the decision environment for applicability of the approach, structuring of data uncertainty and the scenario generation process, choice of appropriate robustness criteria, and formulation and solution of robust decision problems. Audience: The book will be of interest to researchers, practitioners and graduate students working in the fields of operations research, management science, industrial and systems engineering, computer science, decision analysis and applied mathematics.