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＜電子ブック＞
Robust Discrete Optimization and Its Applications / by Panos Kouvelis, Gang Yu
(Nonconvex Optimization and Its Applications ; 14)

版	1st ed. 1997.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	1997
本文言語	英語
大きさ	XVI, 358 p : online resource
著者標目	*Kouvelis, Panos author Gang Yu author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Operations research LCSH:Production management LCSH:Algorithms FREE:Optimization FREE:Operations Research and Decision Theory FREE:Operations Management FREE:Algorithms
一般注記	1 Approaches for Handling Uncertainty in Decision Making -- 2 A Robust Discrete Optimization Framework -- 3 Computational Complexity Results of Robust Discrete Optimization Problems -- 4 Easily Solvable Cases of Robust Discrete Optimization Problems -- 5 Algorithmic Developments for Difficult Robust Discrete Optimization Problems -- 6 Robust 1-Median Location Problems: Dynamic Aspects and Uncertainty -- 7 Robust Scheduling Problems -- 8 Robust Uncapacitated Network Design and International Sourcing Problems -- 9 Robust Discrete Optimization: Past Successes and Future Challenges 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 HTTP:URL=https://doi.org/10.1007/978-1-4757-2620-6

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