Relevant bibliographies by topics / Multistage optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multistage optimization'

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Published: 25 May 2024

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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 (August 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 (September 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 (December 1, 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 is suggested for the solution of the multiobjective nonlinear constrained optimization problem by treating the ideal feasible solutions as the goals for the corresponding objective functions. The utility of the resulting computer program is demonstrated through the design of six- and 18-speed gearboxes. The present methodology offers the feasibility of automating the design of gearboxes by incorporating all the (conflicting) design requirements and objectives.
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Ghosh, T. K., and R. G. Carter. "Optimization of Multistage Depressed Collectors." IEEE Transactions on Electron Devices 54, no. 8 (August 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 (August 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 (July 1, 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 (November 26, 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 response surfaces accurately such as multivariate M-estimators. In optimization phase, multi-response optimization methods such as global criterion (GC) method and ε-constraints approaches are different methods to optimize the multi-objective-multistage problems. An example of the multistage problem had been estimated considering multivariate robust approaches, besides applying multi-response optimization approaches. The results show the efficiency of the proposed approaches.
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Bistrickas, V. J., and N. Šimelienė. "Discrete Multistage Optimization and Hierarchical Market." Nonlinear Analysis: Modelling and Control 11, no. 2 (May 18, 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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Bertsimas, Dimitris, and Constantine Caramanis. "Finite Adaptability in Multistage Linear Optimization." IEEE Transactions on Automatic Control 55, no. 12 (December 2010): 2751–66. http://dx.doi.org/10.1109/tac.2010.2049764.

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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 (May 13, 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 each pump is obtained. Nine pumping stations in Beijing were selected as a case study and this method was applied for multistage pumping station optimization and single pumping station optimization in the case study. Results from the case study demonstrate that the multistage pumping station optimization acquired a small number of pump start-up/shutoff times, which were from 8 to 114 in different rainfall scenarios. Compared with the multistage pumping station optimization, the single pumping station optimization had a bigger number of pump start-up/shutoff times, which were from 1 to 133 times, and the pump operating time was also longer, from 72 min to 7542 min. Therefore, the multistage pumping station optimization method was more suitable to reduce the frequency of pump start-up/shutoffs.
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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 evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.
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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 and the value function. In the parallel algorithm, an incentive coordination method is used to coordinate the subproblems. We provide proofs of convergence for these algorithms. Two variations, namely, subgradient selection and subgradient aggregation, of the basic algorithms are also discussed. In practice while subgradient selection seems to perform well, computational results with subgradient aggregation are rather disappointing. Computational results of the basic algorithms and variants based on subgradient selection are given. The effect of number of stages on performance of these algorithms is compared with a general nonlinear programming package (NPSOL).
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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; define individual scenarios that are used to build the initial stochastic process (as a fan or an initial scenario tree); and builds the final scenario trees that are the approximation of the stochastic process. The methodology proposes consists of two phases. In the first phase, we developed a procedure similar to Muñoz et al. (2013), with the difference being that the VAR models are used to predict the next day for each random parameter of the MSP models. In the second phase, we build scenario trees from the Forward Tree Construction Algorithm(FTCA), developed by Heitsch and Römisch (2009a); and an adapted version of DynamicTree Generation with a Flexible Bushiness Algorithm (DTGFBA), developed by Pflugand Pichler (2014, 2015). This methodology was used to generate scenario trees for two MSP models. A first model, Multistage Stochastic Wind Battery Virtual Power Plantmodel (MSWBVPP model) and to a second model, which is the Multistage StochasticOptimal Operation of Distribution Networks model (MSOODN model). We developed extensive computational experiments for the MSWBVPP model and generated scenario trees with real data, which were based on MIBEL prices and wind power generation of the real wind farm called Espina, located in Spain. For the MSOODN model, we obtained scenario trees by also using real data from the power load provided by FEEC-UNICAMP and photovoltaic generation of a distribution grid located in Brazil. The results show that the scenario tree generation methodology proposed in this thesis can obtain suitable scenario trees for each MSP model. In addition, results were obtained for the model using the scenario trees as input data. In the case of the MSWBVPP model, we solved three different case studies corresponding to three different hypotheses on the virtual power plant’s participation in electricity markets. In the case of the MSOODN model, two test cases were solved, with the results indicating that the EDN satisfied the limits imposed for each test case. Furthermore, the BESS case gave good results when taking into account the uncertainty in the model. Finally, the MSWBVPP model was used to study the relative performance of the FTCA and DTGFBA scenario trees, specifically by analyzing the value of the stochastic solution for the 366 daily optimal bidding problems. To this end, a variation of the classical VSS (the so-called “Forecasted Value of the Stochastic Solution”, FVSS) was defined and used together with the classical VSS.
a 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.
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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 problem. Second, a stochastic program for integrating renewable energy sources into traditional energy systems is developed. As the global push for higher utilization of such green resources increases, such models will prove invaluable to energy system designers. Finally, a process for transforming clinical medical data into a model to assist decision making during the treatment planning phase for palliative chemotherapy is outlined. This work will likely provide decision support tools for oncologists. Moreover, given the new requirements for the usage electronic medical records, such techniques will have applicability to other treatment planning applications in the future.
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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, hidden failures, human errors, terrorist attacks, and natural disasters. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic programming provides a mathematical framework for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the intrinsic uncertainty into the decision making process. In this dissertation, we focus on application of two-stage and multistage stochastic programming approaches to electric energy systems modeling and optimization. Particularly, we develop models and algorithms addressing the sustainability and reliability issues in power systems. First, we consider how to improve the reliability of power systems under severe failures or contingencies prone to cascading blackouts by so called islanding operations. We present a two-stage stochastic mixed-integer model to find optimal islanding operations as a powerful preventive action against cascading failures in case of extreme contingencies. Further, we study the properties of this problem and propose efficient solution methods to solve this problem for large-scale power systems. We present the numerical results showing the effectiveness of the model and investigate the performance of the solution methods. Next, we address the sustainability issue considering the integration of renewable energy resources into production planning of energy-intensive manufacturing industries. Recently, a growing number of manufacturing companies are considering renewable energies to meet their energy requirements to move towards green manufacturing as well as decreasing their energy costs. However, the intermittent nature of renewable energies imposes several difficulties in long term planning of how to efficiently exploit renewables. In this study, we propose a scheme for manufacturing companies to use onsite and grid renewable energies provided by their own investments and energy utilities as well as conventional grid energy to satisfy their energy requirements. We propose a multistage stochastic programming model and study an efficient solution method to solve this problem. We examine the proposed framework on a test case simulated based on a real-world semiconductor company. Moreover, we evaluate long-term profitability of such scheme via so called value of multistage stochastic programming.
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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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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.
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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 solution times with solution quality? Moreover, we present our preliminary approach to address these questions. Secondly, we investigate the multistage stochastic linear programs and propose a new approach to solving multistage stochastic decision models in the presence of constraints. The motivation for proposing the multistage stochastic decomposition algorithm is to handle large scale multistage stochastic linear programs. In our setting, the deterministic equivalent problems of the multistage stochastic linear program are too large to be solved exactly. Therefore, we seek an asymptotically optimum solution by simulating the SD algorithmic process, which was originally designed for two-stage stochastic linear programs (SLPs). More importantly, when SD is implemented in a time-staged manner, the algorithm begins to take the flavor of a simulation leading to what we refer to as optimization simulation. As for multistage stochastic decomposition, there are a couple of advantages that deserve mention. One of the benefits is that it can work directly with sample paths, and this feature makes the new algorithm much easier to be integrated within a simulation. Moreover, compared with other sampling-based algorithms for multistage stochastic programming, we also overcome certain limitations, such as a stage-wise independence assumption.
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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.
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. Cham: 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. Berlin: Springer, 2005.

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Catalano, 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.

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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, 2014.

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

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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, 1–39. Cham: 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, 41–93. Cham: 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, 95–123. Cham: 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, 125–73. Cham: 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, 175–208. Cham: 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, 209–28. Cham: 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, 229–55. Cham: 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, 257–73. Cham: 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, 35–47. Boston, MA: 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, 385–410. Boston, MA: 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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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. 400 Commonwealth Drive, Warrendale, PA, United States: 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. Reston, Virginia: 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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Abstract:
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 fidelity RANS defines the 3D airfoil geometry. In order to accelerate the entire design procedure, a special routine was developed to morph the 1D results into the required info for the 3D optimization. Both the 1D and 3D optimizations are based on differential evolution algorithm.
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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. Reston, Virigina: 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. New York, New York, USA: 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. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-3683.

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Overin, Alexander, Michael Samoilov, Semen Kudrya, and 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.

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Kim, Harrison, and 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.

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Young, Gin-Shu, and 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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