Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation /

Two-stage stochastic optimization is a useful tool for making optimal decisions under uncertainty. Frederike Neise describes two concepts to handle the classic linear mixed-integer two-stage stochastic optimization problem: The well-known mean-risk modeling, which aims at finding a best solution in...

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Principais autores: Neise, Frederike., SpringerLink (Online service)
Formato: Digital
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Endereço do item:http://dx.doi.org/10.1007/978-3-8348-9536-3
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spelling oai:localhost:123456789-1298522023-07-17T15:13:22Z Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation / Neise, Frederike. SpringerLink (Online service) Matemática. Two-stage stochastic optimization is a useful tool for making optimal decisions under uncertainty. Frederike Neise describes two concepts to handle the classic linear mixed-integer two-stage stochastic optimization problem: The well-known mean-risk 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 branch-and-bound techniques. Finally, both approaches mean-risk 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. 0 2022-10-06T07:52:28Z 2022-10-06T07:52:28Z 2008. Digital 51 N416r 9783834895363 197940 http://dx.doi.org/10.1007/978-3-8348-9536-3 http://dx.doi.org/10.1007/978-3-8348-9536-3
institution Acervo SISBI
collection SIGAA
topic Matemática.
spellingShingle Matemática.
Neise, Frederike.
SpringerLink (Online service)
Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation /
description Two-stage stochastic optimization is a useful tool for making optimal decisions under uncertainty. Frederike Neise describes two concepts to handle the classic linear mixed-integer two-stage stochastic optimization problem: The well-known mean-risk 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 branch-and-bound techniques. Finally, both approaches mean-risk 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.
format Digital
author Neise, Frederike.
SpringerLink (Online service)
author_facet Neise, Frederike.
SpringerLink (Online service)
author_sort Neise, Frederike.
title Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation /
title_short Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation /
title_full Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation /
title_fullStr Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation /
title_full_unstemmed Risk Management in Stochastic Integer Programming With Application to Dispersed Power Generation /
title_sort risk management in stochastic integer programming with application to dispersed power generation /
publishDate 2022
url http://dx.doi.org/10.1007/978-3-8348-9536-3
work_keys_str_mv AT neisefrederike riskmanagementinstochasticintegerprogrammingwithapplicationtodispersedpowergeneration
AT springerlinkonlineservice riskmanagementinstochasticintegerprogrammingwithapplicationtodispersedpowergeneration
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