Robust Discrete Optimization and Its Applications

This text deals with decision-making in environments of significant data uncertainty, with particular emphasis on operations and production management applications. For such environments, this text suggests 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 arize. The book provides a comprehensive mathematical programming framework for robust decision making. It also presents 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.