Relevant bibliographies by topics / Multistage optimization

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Academic literature on the topic 'Multistage optimization'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Multistage optimization"

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Nusinovich, G. S., B. Levush, and O. Dumbrajs. "Optimization of multistage harmonic gyrodevices." Physics of Plasmas 3, no. 8 (1996): 3133–44. http://dx.doi.org/10.1063/1.871589.

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Aleksandrov, V. Yu, and K. K. Klimovskii. "Optimization of multistage gas ejectors." Thermal Engineering 56, no. 9 (2009): 790–94. http://dx.doi.org/10.1134/s0040601509090146.

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Rao, S. S., and H. R. Eslampour. "Multistage Multiobjective Optimization of Gearboxes." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 4 (1986): 461–68. http://dx.doi.org/10.1115/1.3258755.

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The problems of kinematic and strength designs of multispeed gearboxes are formulated as multiobjective optimization problems. In the kinematic design stage, the speeds of all the shafts, the number of teeth on various gears and the gear module are selected so as to minimize the deviation of output speeds from specified values and the overall center distance of the gearbox. In the strength design stage, the face widths of the various gear pairs are chosen so as to minimize the volume of the material of the gears and to maximize the power transmitted by the gearbox. A goal programming approach
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Ghosh, T. K., and R. G. Carter. "Optimization of Multistage Depressed Collectors." IEEE Transactions on Electron Devices 54, no. 8 (2007): 2031–39. http://dx.doi.org/10.1109/ted.2007.900003.

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Rubchinsky, Alexander. "Choice functions in multistage optimization." Journal of Multi-Criteria Decision Analysis 3, no. 2 (1994): 105–17. http://dx.doi.org/10.1002/mcda.4020030205.

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Waleed Hammad, Azhar, and Faiz Faig Showkat. "Multistage Ant System Optimization Algorithm." Engineering and Technology Journal 29, no. 10 (2011): 1893–901. http://dx.doi.org/10.30684/etj.29.10.3.

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Moslemi, Amir, and Mirmehdi Seyyed-Esfahani. "Robust optimization of multistage process: response surface and multi-response optimization approaches." International Journal of Nonlinear Sciences and Numerical Simulation 23, no. 2 (2021): 163–75. http://dx.doi.org/10.1515/ijnsns-2017-0003.

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Abstract A multistage system refers to a system contains multiple components or stages which are necessary to finish the final product or service. To analyze these problems, the first step is model building and the other is optimization. Response surfaces are used to model multistage problem as an efficient procedure. One regular approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. OLS method is very sensitive to outliers, so some multivariate robust estimation methods have been discussed in the literature in order to estimate the respo
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Wang, Hao, Xiaohui Lei, Soon-Thiam Khu, and Lixiang Song. "Optimization of Pump Start-Up Depth in Drainage Pumping Station Based on SWMM and PSO." Water 11, no. 5 (2019): 1002. http://dx.doi.org/10.3390/w11051002.

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The pumps in multistage drainage pumping stations are often subject to frequent start-up and shutoffs during operation because of unreasonable start-up depths of the pumps; this will reduce the service lives of the pumps. To solve this problem, an optimization method for minimizing pump start-up and shutoff times is proposed. In this method, the operation of pumps in pumping station was optimized by constructing a mathematical optimization model. The storm water management model (SWMM) and particle swarm optimization (PSO) method were used to solve the problem and the optimal start-up depth of
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Bistrickas, V. J., and N. Šimelienė. "Discrete Multistage Optimization and Hierarchical Market." Nonlinear Analysis: Modelling and Control 11, no. 2 (2006): 149–56. http://dx.doi.org/10.15388/na.2006.11.2.14755.

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New simple form of mixed solutions is described by bilinear continuous optimization processes. It enables investigate an analytic solutions and the connection between discrete and continuous optimization processes. Connection between discrete and continuous processes is stochastic. Discrete optimization processes are used for the control works in levels and groups of the hierarchical market. Equilibrium between local and global levels of works is investigated in hierarchical market.
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Shi, Shuo, Lan Lan Guo, Jian Ting Sun, and Guang Sheng Du. "Optimization Design of the Spinning Multistage Centrifugal." Applied Mechanics and Materials 644-650 (September 2014): 567–70. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.567.

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This paper analyses the structure of the original spinning multistage centrifugal fan. Because of low pressure and low efficiency, optimization design was carried out for the original fan. Based on the aerodynamic calculations theory, the structure and pneumatic of single stage was studied. The influence factors are Inlet-flow loss, equivalent divergent angle, pre-swirl, Resonant frequency etc. Pneumatic recalculation and experiment are carried out for the multistage fan. Results show that after modification pressure of the multistage fan has increased raises 15.3 %.
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Dissertations / Theses on the topic "Multistage optimization"

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Rosmarin, Jonathan. "An evolutionary approach to multistage portfolio optimization." Thesis, Imperial College London, 2007. http://hdl.handle.net/10044/1/7280.

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Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific e
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Dunatunga, Manimelwadu Samson. "Optimization of multistage systems with nondifferentiable objective functions." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185050.

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This dissertation is aimed at a class of convex dynamic optimization problems in which the transition functions are twice continuously differentiable and the stagewise objective functions are convex, although not necessarily differentiable. Two basic descent algorithms which use sequential and parallel coordinating techniques are developed. In both algorithms the nondifferentiability of the objective function is accounted for by using subgradient information. The objective of the subproblems generated consists of successive piecewise linear approximations of the stagewise objective function an
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Cuadrado, 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.

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The presence of renewables in energy systems optimization have generated a high level of uncertainty in the data, which has led to a need for applying stochastic optimization to modelling problems with this characteristic. The method followed in this thesis is multistage Stochastic Programming (MSP). Central to MSP is the idea of representing uncertainty (which, in this case, is modelled with a stochastic process) using scenario trees. In this thesis, we developed a methodology that starts with available historical data; generates a set of scenarios for each random variable of the MSP model; d
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Kuhn, Daniel. "Generalized bounds for convex multistage stochastic programs /." Berlin [u.a.] : Springer, 2005. http://www.loc.gov/catdir/enhancements/fy0818/2004109705-d.html.

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Kuznia, Ludwig Charlemagne. "Extensions of Multistage Stochastic Optimization with Applications in Energy and Healthcare." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4114.

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This dissertation focuses on extending solution methods in the area of stochastic optimization. Attention is focused to three specific problems in the field. First, a solution method for mixed integer programs subject to chance constraints is discussed. This class of problems serves as an effective modeling framework for a wide variety of applied problems. Unfortunately, chance constrained mixed integer programs tend to be very challenging to solve. Thus, the aim of this work is to address some of these challenges by exploiting the structure of the deterministic reformulation for the prob
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Golari, 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.

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Electric energy constitutes one of the most crucial elements to almost every aspect of life of people. The modern electric power systems face several challenges such as efficiency, economics, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, weak conditions, unexpected events, hidde
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Chagas, 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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Submitted by Guido Chagas (guido.chagas@fgv.br) on 2016-09-09T15:34:13Z No. of bitstreams: 1 Long-Term Asset Allocation Based on Stochastic Multistage Multi-Objective Portfolio Optimization.pdf: 6336618 bytes, checksum: 67d3dd1c3b982252c5012b3078278f95 (MD5)<br>Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2016-09-09T17:20:03Z (GMT) No. of bitstreams: 1 Long-Term Asset Allocation Based on Stochastic Multistage Multi-Objective Portfolio Optimization.pdf: 6336618 bytes, checksum: 67d3dd1c3b982252c5012b3078278f95 (MD5)<br>Made available in DSpace on
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Zhou, Zhihong. "Multistage Stochastic Decomposition and its Applications." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/222892.

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In this dissertation, we focus on developing sampling-based algorithms for solving stochastic linear programs. The work covers both two stage and multistage versions of stochastic linear programs. In particular, we first study the two stage stochastic decomposition (SD) algorithm and present some extensions associated with SD. Specifically, we study two issues: a) are there conditions under which the regularized version of SD generates a unique solution? and b) in cases where a user is willing to sacrifice optimality, is there a way to modify the SD algorithm so that a user can trade-off solut
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Küchler, Christian. "Stability, approximation, and decomposition in two- and multistage stochastic programming." Wiesbaden : Vieweg + Teubner, 2009. http://d-nb.info/995018979/04.

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Yeo, 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.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title 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
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Books on the topic "Multistage optimization"

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Pflug, Georg Ch, and Alois Pichler. Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3.

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Kuhn, Daniel. Generalized bounds for convex multistage stochastic programs. Springer, 2005.

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Catalano, L. A. Two dimensional optimization of smoothing properties of multistage schemes applied to hyperbolic equations. von Karman Institute for Fluid Dynamics, 1990.

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Pflug, Georg, and Alois Pichler. Multistage Stochastic Optimization. Springer International Publishing AG, 2014.

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Pflug, Georg Ch, and Alois Pichler. Multistage Stochastic Optimization. Springer International Publishing AG, 2016.

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Pflug, Georg Ch, and Alois Pichler. Multistage Stochastic Optimization. Springer, 2014.

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Numerical Methods for Convex Multistage Stochastic Optimization. Now Publishers, 2024.

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Kutsch, Christian. Investigating Distributed Approaches for Solving Discrete, Multistage Optimization Problems. GRIN Verlag GmbH, 2013.

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Book chapters on the topic "Multistage optimization"

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Pflug, Georg Ch, and Alois Pichler. "Introduction." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_1.

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Pflug, Georg Ch, and Alois Pichler. "The Nested Distance." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_2.

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Pflug, Georg Ch, and Alois Pichler. "Risk and Utility Functionals." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_3.

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Pflug, Georg Ch, and Alois Pichler. "From Data to Models." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_4.

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Pflug, Georg Ch, and Alois Pichler. "Time Consistency." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_5.

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Pflug, Georg Ch, and Alois Pichler. "Approximations and Bounds." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_6.

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Pflug, Georg Ch, and Alois Pichler. "The Problem of Ambiguity in Stochastic Optimization." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_7.

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Pflug, Georg Ch, and Alois Pichler. "Examples." In Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3_8.

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Gulpinar, Nalan, Berc Rustem, and Reuben Settergren. "Multistage Stochastic Programming in Computational Finance." In Applied Optimization. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3613-7_3.

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Steinbach, Marc C. "Hierarchical Sparsity in Multistage Stochastic Programs." In Applied Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-6594-6_16.

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Conference papers on the topic "Multistage optimization"

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Yan, Yunhao, and Henry Lam. "Data-Driven Solutions and Uncertainty Quantification for Multistage Stochastic Optimization." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838792.

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Kök, Ali, Jonathan Hachez, and Lukas Kranzl. "A Multistage Stochastic Optimization Framework for Long-Term District Heating Capacity Expansion." In 2025 21st International Conference on the European Energy Market (EEM). IEEE, 2025. https://doi.org/10.1109/eem64765.2025.11050152.

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Torok, J., and J. L. Smee. "Optimization of a PWR Decontamination Process." In CORROSION 1985. NACE International, 1985. https://doi.org/10.5006/c1985-85118.

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Abstract An experimental program to optimize a multistage process for PWR reactor decontamination is described. Three oxidants as first stage treatment reagents in conjunction with an oxide solvent as a second stage reagent were tested. Of the oxidizing agents dilute alkaline permanganate was identified as the preferred reagent. Varying potassium permanganate in the 0.05 to 0.5% concentration range had minimal effect on the decontamination process. An increase in the concentration of the oxide solvent - LND-101A* - resulted in improved activity removal. Increasing the number of treatment stage
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Li, Huayi, Dominic Liao-McPherson, Ilya Kolmanovsky, Shinhoon Kim, and Ken Butts. "Analysis of Multistage Hybrid Powertrains Using Multistage Mixed-Integer Trajectory Optimization." In WCX SAE World Congress Experience. SAE International, 2020. http://dx.doi.org/10.4271/2020-01-0274.

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Matveev, Valety N., Grigorii M. Popov, Oleg V. Baturin, Evgenii S. Goryachkin, and Daria A. Kolmakova. "Workflow Optimization of Multistage Axial Turbine." In 51st AIAA/SAE/ASEE Joint Propulsion Conference. American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-4129.

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Potanin, Vladislav Y., and 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.

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Sozio, Ernesto, Tom Verstraete, and 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.

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Air Turbo Rocket engines, suitable for high-speed propulsion, require compact turbomachinery. This paper presents the design of an innovative multi-stage turbine mounted at the hub of a counter-rotating fan. Hence, the turbine airfoils are required to deliver high torque at low peripheral speeds. The design methodology specifically developed for this fourteen-stage turbine relies on two successive optimization cycles. The first one is based on a through-flow 1D code. This optimization cycle explores a vast set of possible design solutions. In a second step, an optimization using a 3D high fide
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Moroz, Leonid, and Yuri Govorushenko. "Multidisciplinary Optimization of Multistage Axial Turbine Flow Path." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4569.

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Jaros, Jiri. "Evolutionary optimization of multistage interconnection networks performance." In the 11th Annual conference. ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570107.

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Popov, Grigorii M., Oleg Baturin, Evgenii Goriachkin, Daria A. Kolmakova, Andrei Volkov, and Igor Egorov. "Optimization Algorithm for Axial Multistage Compressor Workflow." In AIAA Propulsion and Energy 2020 Forum. American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-3683.

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