Auswahl der wissenschaftlichen Literatur zum Thema „Multistage optimization“
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Zeitschriftenartikel zum Thema "Multistage optimization"
Nusinovich, G. S., B. Levush und O. Dumbrajs. „Optimization of multistage harmonic gyrodevices“. Physics of Plasmas 3, Nr. 8 (August 1996): 3133–44. http://dx.doi.org/10.1063/1.871589.
Der volle Inhalt der QuelleAleksandrov, V. Yu, und K. K. Klimovskii. „Optimization of multistage gas ejectors“. Thermal Engineering 56, Nr. 9 (September 2009): 790–94. http://dx.doi.org/10.1134/s0040601509090146.
Der volle Inhalt der QuelleRao, S. S., und H. R. Eslampour. „Multistage Multiobjective Optimization of Gearboxes“. Journal of Mechanisms, Transmissions, and Automation in Design 108, Nr. 4 (01.12.1986): 461–68. http://dx.doi.org/10.1115/1.3258755.
Der volle Inhalt der QuelleGhosh, T. K., und R. G. Carter. „Optimization of Multistage Depressed Collectors“. IEEE Transactions on Electron Devices 54, Nr. 8 (August 2007): 2031–39. http://dx.doi.org/10.1109/ted.2007.900003.
Der volle Inhalt der QuelleRubchinsky, Alexander. „Choice functions in multistage optimization“. Journal of Multi-Criteria Decision Analysis 3, Nr. 2 (August 1994): 105–17. http://dx.doi.org/10.1002/mcda.4020030205.
Der volle Inhalt der QuelleWaleed Hammad, Azhar, und Faiz Faig Showkat. „Multistage Ant System Optimization Algorithm“. Engineering and Technology Journal 29, Nr. 10 (01.07.2011): 1893–901. http://dx.doi.org/10.30684/etj.29.10.3.
Der volle Inhalt der QuelleMoslemi, Amir, und Mirmehdi Seyyed-Esfahani. „Robust optimization of multistage process: response surface and multi-response optimization approaches“. International Journal of Nonlinear Sciences and Numerical Simulation 23, Nr. 2 (26.11.2021): 163–75. http://dx.doi.org/10.1515/ijnsns-2017-0003.
Der volle Inhalt der QuelleBistrickas, V. J., und N. Šimelienė. „Discrete Multistage Optimization and Hierarchical Market“. Nonlinear Analysis: Modelling and Control 11, Nr. 2 (18.05.2006): 149–56. http://dx.doi.org/10.15388/na.2006.11.2.14755.
Der volle Inhalt der QuelleBertsimas, Dimitris, und Constantine Caramanis. „Finite Adaptability in Multistage Linear Optimization“. IEEE Transactions on Automatic Control 55, Nr. 12 (Dezember 2010): 2751–66. http://dx.doi.org/10.1109/tac.2010.2049764.
Der volle Inhalt der QuelleWang, Hao, Xiaohui Lei, Soon-Thiam Khu und Lixiang Song. „Optimization of Pump Start-Up Depth in Drainage Pumping Station Based on SWMM and PSO“. Water 11, Nr. 5 (13.05.2019): 1002. http://dx.doi.org/10.3390/w11051002.
Der volle Inhalt der QuelleDissertationen zum Thema "Multistage optimization"
Rosmarin, Jonathan. „An evolutionary approach to multistage portfolio optimization“. Thesis, Imperial College London, 2007. http://hdl.handle.net/10044/1/7280.
Der volle Inhalt der QuelleDunatunga, Manimelwadu Samson. „Optimization of multistage systems with nondifferentiable objective functions“. Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185050.
Der volle Inhalt der QuelleCuadrado, Guevara Marlyn Dayana. „Multistage scenario trees generation for renewable energy systems optimization“. Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/670251.
Der volle Inhalt der Quellea presencia de energías renovables en la optimización de sistemas energéticos hagenerado un alto nivel de incertidumbre en los datos, lo que ha llevado a la necesidad de aplicar técnicas de optimización estocástica para modelar problemas con estas características. El método empleado en esta tesis es programación estocástica multietapa (MSP, por sus siglas en inglés). La idea central de MSP es representar la incertidumbre (que en este caso es modelada mediante un proceso estocástico), mediante un árbol de escenarios. En esta tesis, desarrollamos una metodología que parte de una data histórica, la cual está disponible; generamos un conjunto de escenarios por cada variable aleatoria del modelo MSP; definimos escenarios individuales, que luego serán usados para construir el proceso estocástico inicial (como un fan o un árbol de escenario inicial); y, por último, construimos el árbol de escenario final, el cual es la aproximación del proceso estocástico. La metodología propuesta consta de dos fases. En la primera fase, desarrollamos un procedimiento similar a Muñoz et al. (2013), con la diferencia de que para las predicciones del próximo día para cada variable aleatoria del modelo MSP usamos modelos VAR. En la segunda fase construimos árboles de escenarios mediante el "Forward Tree Construction Algorithm (FTCA)", desarrollado por Heitsch and Römisch (2009a); y una versión adaptada del "Dynamic Tree Generation with a Flexible Bushiness Algorithm (DTGFBA)", desarrolado por Pflug and Pichler (2014, 2015). Esta metodología fue usada para generar árboles de escenarios para dos modelos MSP. El primer modelo fue el "Multistage Stochastic Wind Battery Virtual Power Plant model (modelo MSWBVPP)", y el segundo modelo es el "Multistage Stochastic Optimal Operation of Distribution Networks model (MSOODN model)". Para el modelo MSWBVPP desarrollamos extensivos experimentos computacionales y generamos árboles de escenarios a partir de datos realesde precios MIBEL y generación eólica de una granja eólica llamada Espina, ubicada en España. Para el modelo MSOODN obtuvimos árboles de escenarios basados en datos reales de carga, provistos por FEEC-UNICAMP y de generación fotovoltaica de una red de distribución localizada en Brasil. Los resultados muestran que la metodología de generación de árboles de escenarios propuesta en esta tesis, permite obtener árboles de escenarios adecuados para cada modelo MSP. Adicionalmente, obtuvimos resultados para los modelos MSP usando como datos de entrada los árboles de escenarios. En el caso del modelo MSWBVPP, resolvimos tres casos de estudio correspondiente a tres hipótesis basadas en la participación de una VPP en los mercados de energía. En el caso del modelo MSOODN, dos casos de prueba fueron resueltos, mostrando que la EDN satisface los límites impuestos para cada caso de prueba, y además, que el caso con BESS da mejores resultados cuando se toma en cuenta el valor la incertidumbre en el modelo. Finalmente, el modelo MSWBVPP fue usado para estudiar el desempeño relativo de los árboles de escenarios FTCA y DTGFBA, específicamente, analizando el valor de la solución estocástica para los 366 problemas de oferta óptima. Para tal fin, una variación del clásico VSS (denominado "Forecasted Value of the Stochastic Solution", FVSS) fue definido y usado junto al clásico VSS.
Kuhn, Daniel. „Generalized bounds for convex multistage stochastic programs /“. Berlin [u.a.] : Springer, 2005. http://www.loc.gov/catdir/enhancements/fy0818/2004109705-d.html.
Der volle Inhalt der QuelleKuznia, Ludwig Charlemagne. „Extensions of Multistage Stochastic Optimization with Applications in Energy and Healthcare“. Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4114.
Der volle Inhalt der QuelleGolari, Mehdi. „Multistage Stochastic Programming and Its Applications in Energy Systems Modeling and Optimization“. Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556438.
Der volle Inhalt der QuelleChagas, Guido Marcelo Borma. „Long-term asset allocation based on stochastic multistage multi-objective portfolio optimization“. reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/17044.
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Multi-Period Stochastic Programming (MSP) offers an appealing approach to identity optimal portfolios, particularly over longer investment horizons, because it is inherently suited to handle uncertainty. Moreover, it provides flexibility to accommodate coherent risk measures, market frictions, and most importantly, major stylized facts as volatility clustering, heavy tails, leverage effects and tail co-dependence. However, to achieve satisfactory results a MSP model relies on representative and arbitrage-free scenarios of the pertaining multivariate financial series. Only after we have constructed such scenarios, we can exploit it using suitable risk measures to achieve robust portfolio allocations. In this thesis, we discuss a comprehensive framework to accomplish that. First, we construct joint scenarios based on a combined GJR-GARCH + EVT-GPD + t-Copula approach. Then, we reduce the original scenario tree and remove arbitrage opportunities using a method based on Optimal Discretization and Process Distances. Lastly, using the approximated scenario tree we perform a multi-period Mean-Variance-CVaR optimization taking into account market frictions such as transaction costs and regulatory restrictions. The proposed framework is particularly valuable to real applications because it handles various key features of real markets that are often dismissed by more common optimization approaches.
Programação Estocástica Multi-Período (MSP) oferece uma abordagem conveniente para identificar carteiras ótimas, particularmente para horizontes de investimento mais longos, pois incorpora adequadamente a incerteza no processo de otimização. Adicionalmente, ela proporciona flexibilidade para acomodar medidas coerentes de risco, fricções de mercado e fatos estilizados relevantes como agrupamento de volatilidade, caudas pesadas, efeitos de alavancagem e co-dependência nas caudas. No entanto, para alcançar resultados satisfatórios, um modelo MSP depende de cenários representativos e livres de arbitragem. Somente após construídos esses cenários, podemos explorá-los usando medidas de risco adequadas para alcançar alocações ótimas. Nessa tese, discutimos uma metodologia completa para alcançar esse objetivo. Em primeiro lugar, construímos cenários conjuntos baseados numa abordagem conjunta GJR-GARCH + EVT-GPD + t-Copula. Posteriormente, reduzimos a árvore original de cenários e removemos oportunidades de arbitragem utilizando um método de discretização ótima baseado nas distâncias de processos estocásticos. Por último, usando a árvore aproximada de cenários, realizamos uma otimização multi-período de média-variância-CVaR considerando fricções de mercado, custos de transação e restrições regulamentares. A metodologia proposta é particularmente útil para aplicações reais, porque considera várias características relevantes dos mercados reais que muitas vezes são ignorados por abordagens mais simples de otimização.
Zhou, Zhihong. „Multistage Stochastic Decomposition and its Applications“. Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/222892.
Der volle Inhalt der QuelleKüchler, Christian. „Stability, approximation, and decomposition in two- and multistage stochastic programming“. Wiesbaden : Vieweg + Teubner, 2009. http://d-nb.info/995018979/04.
Der volle Inhalt der QuelleYeo, In-Young. „Multistage hierarchical optimization for land use allocation to control nonpoint source water pollution“. Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1127156412.
Der volle Inhalt der QuelleTitle from first page of PDF file. Document formatted into pages; contains xvii, 180 p.; also includes graphics (some col.). Includes bibliographical references (p. 156-171). Available online via OhioLINK's ETD Center
Bücher zum Thema "Multistage optimization"
Pflug, Georg Ch, und Alois Pichler. Multistage Stochastic Optimization. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3.
Der volle Inhalt der QuelleKuhn, Daniel. Generalized bounds for convex multistage stochastic programs. Berlin: Springer, 2005.
Den vollen Inhalt der Quelle findenCatalano, L. A. Two dimensional optimization of smoothing properties of multistage schemes applied to hyperbolic equations. Rhode Saint Genese, Belgium: von Karman Institute for Fluid Dynamics, 1990.
Den vollen Inhalt der Quelle findenPflug, Georg, und Alois Pichler. Multistage Stochastic Optimization. Springer International Publishing AG, 2014.
Den vollen Inhalt der Quelle findenPflug, Georg Ch, und Alois Pichler. Multistage Stochastic Optimization. Springer, 2014.
Den vollen Inhalt der Quelle findenPflug, Georg Ch, und Alois Pichler. Multistage Stochastic Optimization. Springer International Publishing AG, 2016.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Multistage optimization"
Pflug, Georg Ch, und Alois Pichler. „Introduction“. In Multistage Stochastic Optimization, 1–39. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_1.
Der volle Inhalt der QuellePflug, Georg Ch, und Alois Pichler. „The Nested Distance“. In Multistage Stochastic Optimization, 41–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_2.
Der volle Inhalt der QuellePflug, Georg Ch, und Alois Pichler. „Risk and Utility Functionals“. In Multistage Stochastic Optimization, 95–123. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_3.
Der volle Inhalt der QuellePflug, Georg Ch, und Alois Pichler. „From Data to Models“. In Multistage Stochastic Optimization, 125–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_4.
Der volle Inhalt der QuellePflug, Georg Ch, und Alois Pichler. „Time Consistency“. In Multistage Stochastic Optimization, 175–208. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_5.
Der volle Inhalt der QuellePflug, Georg Ch, und Alois Pichler. „Approximations and Bounds“. In Multistage Stochastic Optimization, 209–28. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_6.
Der volle Inhalt der QuellePflug, Georg Ch, und Alois Pichler. „The Problem of Ambiguity in Stochastic Optimization“. In Multistage Stochastic Optimization, 229–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_7.
Der volle Inhalt der QuellePflug, Georg Ch, und Alois Pichler. „Examples“. In Multistage Stochastic Optimization, 257–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_8.
Der volle Inhalt der QuelleGulpinar, Nalan, Berc Rustem und Reuben Settergren. „Multistage Stochastic Programming in Computational Finance“. In Applied Optimization, 35–47. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3613-7_3.
Der volle Inhalt der QuelleSteinbach, Marc C. „Hierarchical Sparsity in Multistage Stochastic Programs“. In Applied Optimization, 385–410. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-6594-6_16.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Multistage optimization"
Li, Huayi, Dominic Liao-McPherson, Ilya Kolmanovsky, Shinhoon Kim und Ken Butts. „Analysis of Multistage Hybrid Powertrains Using Multistage Mixed-Integer Trajectory Optimization“. In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2020. http://dx.doi.org/10.4271/2020-01-0274.
Der volle Inhalt der QuelleMatveev, Valety N., Grigorii M. Popov, Oleg V. Baturin, Evgenii S. Goryachkin und Daria A. Kolmakova. „Workflow Optimization of Multistage Axial Turbine“. In 51st AIAA/SAE/ASEE Joint Propulsion Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-4129.
Der volle Inhalt der QuellePotanin, Vladislav Y., und Elena E. Potanina. „Analytical Optimization of a Multistage Amplifiers“. In 2006 49th IEEE International Midwest Symposium on Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/mwscas.2006.382038.
Der volle Inhalt der QuelleSozio, Ernesto, Tom Verstraete und Guillermo Paniagua. „Design-Optimization Approach to Multistage Axial Contra-Rotating Turbines“. In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94762.
Der volle Inhalt der QuelleMoroz, Leonid, und Yuri Govorushenko. „Multidisciplinary Optimization of Multistage Axial Turbine Flow Path“. In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4569.
Der volle Inhalt der QuelleJaros, Jiri. „Evolutionary optimization of multistage interconnection networks performance“. In the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570107.
Der volle Inhalt der QuellePopov, Grigorii M., Oleg Baturin, Evgenii Goriachkin, Daria A. Kolmakova, Andrei Volkov und Igor Egorov. „Optimization Algorithm for Axial Multistage Compressor Workflow“. In AIAA Propulsion and Energy 2020 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-3683.
Der volle Inhalt der QuelleOverin, Alexander, Michael Samoilov, Semen Kudrya und Konstantin Baidyukov. „Multistage Stimulation: Fracturing Optimization at Samotlorskoe Field“. In SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers, 2019. http://dx.doi.org/10.2118/196961-ms.
Der volle Inhalt der QuelleKim, Harrison, und I. Jessica Hidalgo. „System of Systems Optimization by Pseudo-Hierarchical Multistage Model“. In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-6921.
Der volle Inhalt der QuelleYoung, Gin-Shu, und Bi-Chu Wu. „Optimization of sequentially coupled elements in multistage multidisciplinary system design“. In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4037.
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