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Titlebook: DLP and Extensions; An Optimization Mode John Lawrence Nazareth Book 2001 Springer-Verlag Berlin Heidelberg 2001 Decision Support.Dynamic P

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書目名稱DLP and Extensions
副標題An Optimization Mode
編輯John Lawrence Nazareth
視頻videohttp://file.papertrans.cn/261/260098/260098.mp4
概述The book develops a new approach to computational decision support and contains a demonstration program
圖書封面Titlebook: DLP and Extensions; An Optimization Mode John Lawrence Nazareth Book 2001 Springer-Verlag Berlin Heidelberg 2001 Decision Support.Dynamic P
描述DLP denotes a dynamic-linear modeling and optimization approach to computational decision support for resource planning problems that arise, typically, within the natural resource sciences and the disciplines of operations research and operational engineering. It integrates techniques of dynamic programming (DP) and linear programming (LP) and can be realized in an immediate, practical and usable way. Simultaneously DLP connotes a broad and very general modeling/ algorithmic concept that has numerous areas of application and possibilities for extension. Two motivating examples provide a linking thread through the main chapters, and an appendix provides a demonstration program, executable on a PC, for hands-on experience with the DLP approach.
出版日期Book 2001
關鍵詞Decision Support; Dynamic Programming; Entscheidungsunterstützung; Linear Programming; Modeling; Modellie
版次1
doihttps://doi.org/10.1007/978-3-642-56761-2
isbn_softcover978-3-642-62502-2
isbn_ebook978-3-642-56761-2
copyrightSpringer-Verlag Berlin Heidelberg 2001
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Motivating ExamplesThis introductory chapter describes two very simple resource decision problems. Its purpose is to provide
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Modeling Issues and ApplicationsModeling issues raised in Chapters 1 and 2 are now explored in more detail within the context of resource decision problems from several application areas, in particular:
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Stochastic and Other ExtensionsIn this concluding chapter, we extend the ... model/algorithm and associated decision support system in order to incorporate uncertainty of data. Motivation for ... optimization under certainty (stochastic extension of the model) can be found in Chapter 6, Subsection 6.2.2.
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