LEADER 04772nam 22007815 450 001 9910299774103321 005 20200630020655.0 010 $a1-4939-2282-3 024 7 $a10.1007/978-1-4939-2282-6 035 $a(CKB)3710000000371888 035 $a(EBL)1998227 035 $a(OCoLC)904547657 035 $a(SSID)ssj0001465386 035 $a(PQKBManifestationID)11861816 035 $a(PQKBTitleCode)TC0001465386 035 $a(PQKBWorkID)11472301 035 $a(PQKB)10134107 035 $a(DE-He213)978-1-4939-2282-6 035 $a(MiAaPQ)EBC1998227 035 $a(PPN)184890888 035 $a(EXLCZ)993710000000371888 100 $a20150305d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems /$fby Urmila Diwekar, Amy David 205 $a1st ed. 2015. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2015. 215 $a1 online resource (154 p.) 225 1 $aSpringerBriefs in Optimization,$x2190-8354 300 $aDescription based upon print version of record. 311 $a1-4939-2281-5 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world. 410 0$aSpringerBriefs in Optimization,$x2190-8354 606 $aOperations research 606 $aManagement science 606 $aSystem theory 606 $aDynamics 606 $aErgodic theory 606 $aAlgorithms 606 $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024 606 $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070 606 $aDynamical Systems and Ergodic Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M1204X 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 615 0$aOperations research. 615 0$aManagement science. 615 0$aSystem theory. 615 0$aDynamics. 615 0$aErgodic theory. 615 0$aAlgorithms. 615 14$aOperations Research, Management Science. 615 24$aSystems Theory, Control. 615 24$aDynamical Systems and Ergodic Theory. 615 24$aAlgorithms. 676 $a519.7 700 $aDiwekar$b Urmila$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755489 702 $aDavid$b Amy$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299774103321 996 $aBONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems$92540388 997 $aUNINA