000 03883nam a22005775i 4500
001 978-1-4939-2282-6
003 DE-He213
005 20161006171600.0
007 cr nn 008mamaa
008 150305s2015 xxu| s |||| 0|eng d
020 _a9781493922826
_9978-1-4939-2282-6
024 7 _a10.1007/978-1-4939-2282-6
_2doi
050 4 _aQA402-402.37
050 4 _aT57.6-57.97
072 7 _aKJT
_2bicssc
072 7 _aKJM
_2bicssc
072 7 _aBUS049000
_2bisacsh
072 7 _aBUS042000
_2bisacsh
082 0 4 _a519.6
_223
100 1 _aDiwekar, Urmila.
_eauthor.
245 1 0 _aBONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems
_h[electronic resource] /
_cby Urmila Diwekar, Amy David.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2015.
300 _aXVIII, 146 p. 57 illus., 19 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Optimization,
_x2190-8354
505 0 _a1. Introduction -- 2. Uncertainty Analysis and Sampling Techniques -- 3. Probability Density Functions and Kernel Density Estimation -- 4. The BONUS Algorithm -- 5. Water Management under Weather Uncertainty -- 6. Real Time Optimization for Water Management -- 7. Sensor Placement under Uncertainty for Power Plants -- 8. The L-Shaped BONUS Algorithm -- 9. The Environmental Trading Problem -- 10. Water Security Networks -- References -- Index.
520 _aThis book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.
650 0 _aMathematics.
650 0 _aDynamics.
650 0 _aErgodic theory.
650 0 _aSystem theory.
650 0 _aAlgorithms.
650 0 _aOperations research.
650 0 _aManagement science.
650 1 4 _aMathematics.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aSystems Theory, Control.
650 2 4 _aDynamical Systems and Ergodic Theory.
650 2 4 _aAlgorithms.
700 1 _aDavid, Amy.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781493922819
830 0 _aSpringerBriefs in Optimization,
_x2190-8354
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4939-2282-6
912 _aZDB-2-SMA
999 _c226894
_d226894