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001			309349
003			MX-SnUAN
005			20160429160258.0
007			cr nn 008mamaa
008			150903s2008 gw | o |||| 0|eng d
020			_a9783834895363 _99783834895363
024	7		_a10.1007/9783834895363 _2doi
035			_avtls000363340
039		9	_a201509031023 _bVLOAD _c201405070342 _dVLOAD _y201402211144 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA1-939
100	1		_aNeise, Frederike. _eautor _9349561
245	1	0	_aRisk Management in Stochastic Integer Programming : _bWith Application to Dispersed Power Generation / _cby Frederike Neise.
264		1	_aWiesbaden : _bVieweg+Teubner, _c2008.
300			_aviii, 107 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783834805478
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-8348-9536-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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