| Robust Discrete Optimization and Its Applications 1997 Edition Contributor(s): Kouvelis, Panos (Author), Gang Yu (Author) |
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ISBN: 0792342917 ISBN-13: 9780792342915 Publisher: Springer OUR PRICE: $313.49 Product Type: Hardcover Published: November 1996 Annotation: 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. Beyond theoretical results, the book provides many suggestions and useful advice to the practitioner 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. 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Applied - Technology & Engineering | Industrial Engineering - Mathematics | Optimization |
| Dewey: 003.56 |
| LCCN: 96043291 |
| Series: Nonconvex Optimization and Its Applications |
| Physical Information: 1.04" H x 6.36" W x 9.62" (1.48 lbs) 358 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: This book deals with decision making in environments of significant data un- certainty, with particular emphasis on operations and production management applications. For such environments, we suggest the use of the robustness ap- proach 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. The main motivating factors for a decision maker to use the robustness approach are: - It does not ignore uncertainty and takes a proactive step in response to the fact that forecasted values of uncertain parameters will not occur in most environments; - It applies to decisions of unique, non-repetitive nature, which are common in many fast and dynamically changing environments; - It accounts for the risk averse nature of decision makers; and - It recognizes that even though decision environments are fraught with data uncertainties, decisions are evaluated ex post with the realized data. For all of the above reasons, robust decisions are dear to the heart of opera- tional decision makers. 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. Robust Discrete Optimization is a comprehensive mathematical programming framework for robust decision making. |