Risk management in stochastic integer programming : with application to dispersed power generation
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The work Risk management in stochastic integer programming : with application to dispersed power generation represents a distinct intellectual or artistic creation found in University of Missouri Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Risk management in stochastic integer programming : with application to dispersed power generation
Resource Information
The work Risk management in stochastic integer programming : with application to dispersed power generation represents a distinct intellectual or artistic creation found in University of Missouri Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Risk management in stochastic integer programming : with application to dispersed power generation
 Title remainder
 with application to dispersed power generation
 Statement of responsibility
 Frederike Neise
 Subject

 Academic theses
 Ganzzahlige Optimierung
 Integer programming
 Integer programming
 Integer programming
 Integer programming
 Mathematics
 Mathematics
 Mathematics
 Mathematics
 Risikomanagement
 Risk management  Mathematical models
 Risk management  Mathematical models
 Risk management  Mathematical models
 Risk management  Mathematical models
 Stochastic programming
 Stochastic programming
 Stochastic programming
 Stochastic programming
 Academic theses
 Academic theses
 Language
 eng
 Summary
 Twostage stochastic optimization is a useful tool for making optimal decisions under uncertainty. Frederike Neise describes two concepts to handle the classic linear mixedinteger twostage stochastic optimization problem: The wellknown meanrisk modeling, which aims at finding a best solution in terms of expected costs and risk measures, and stochastic programming with first order dominance constraints that heads towards a decision dominating a given cost benchmark and optimizing an additional objective. For this new class of stochastic optimization problems results on structure and stability are proven. Moreover, the author develops equivalent deterministic formulations of the problem, which are efficiently solved by the presented dual decomposition method based on Lagrangian relaxation and branchandbound techniques. Finally, both approaches  meanrisk optimization and dominance constrained programming  are applied to find an optimal operation schedule for a dispersed generation system, a problem from energy industry that is substantially influenced by uncertainty
 Cataloging source
 GW5XE
 Dewey number
 519.77
 Dissertation note
 Zugl.: Duisburg, Essen, University, Diss., 2008.
 Illustrations
 illustrations
 Index
 no index present
 LC call number
 QA402.5
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 theses
 Series statement

 Wissenschaft
 Vieweg+Teubner research
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