000			02762nam a22004455i 4500
001			978-3-8348-9536-3
003			DE-He213
005			20161121231217.0
007			cr nn 008mamaa
008			100301s2008 gw | s |||| 0|eng d
020			_a9783834895363 _9978-3-8348-9536-3
024	7		_a10.1007/978-3-8348-9536-3 _2doi
050		4	_aQA273.A1-274.9
050		4	_aQA274-274.9
072		7	_aPBT _2bicssc
072		7	_aPBWL _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.2 _223
100	1		_aNeise, Frederike. _eauthor.
245	1	0	_aRisk Management in Stochastic Integer Programming _h[electronic resource] : _bWith Application to Dispersed Power Generation / _cby Frederike Neise.
264		1	_aWiesbaden : _bVieweg+Teubner, _c2008.
300			_aVIII, 107 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aRisk Measures in Two-Stage Stochastic Programs -- Stochastic Dominance Constraints induced by Mixed-Integer Linear Recourse -- Application: Optimal Operation of a Dispersed Generation System -- Conclusion and Perspective.
520			_aTwo-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.
650		0	_aMathematics.
650		0	_aProbabilities.
650	1	4	_aMathematics.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aMathematics, general.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783834805478
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-8348-9536-3
912			_aZDB-2-SMA
950			_aMathematics and Statistics (Springer-11649)
999			_c510189 _d510189