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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Teiller, Alexandre. "Aspects algorithmiques de l'optimisation « multistage »." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS471.
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En optimisation combinatoire classique, étant donné une instance d’un problème, il est demandé de trouver une bonne solution réalisable. Cependant, dans de nombreux cas, les données peuvent évoluer au cours du temps et il est demandé de résoudre une séquence d’instances. Gupta et al. (2014) et Eisenstat et al. (2014) ont proposé un modèle multistage où étant donné un horizon de temps, l’entrée est une séquence d’instances (une pour chaque pas de temps), et l’objectif est de trouver une séquence de solutions (une pour chaque pas de temps) qui atteindrait un compromis entre la qualité des solutions à chaque pas de temps et la stabilité/similarité des solutions pour des pas de temps consécutifs. Dans le Chapitre 1, nous présenterons un aperçu des problèmes d’optimisation prenant en compte des données évolutives. Dans le Chapitre 2, le problème du sac-à-dos est traité dans un contexte offline. La contribution principale est un schéma d’approximation polynomiale (PTAS). Dans le Chapitre 3, le cadre multistage est étudié pour des problèmes multistage dans un contexte online. La contribution principale est l’introduction d’une structure pour ces problèmes avec des bornes presque serrées supérieures et inférieures sur les meilleurs ratios compétitifs de ces modèles. Enfin, dans le Chapitre 4 est présenté une application directe du cadre multistage dans un contexte musical, i.e l’orchestration assistée par ordinateur avec son cible. Nous avons présenté une analyse théorique du problème, en montrant sa NP-difficulté, des résultats d’approximation ainsi que des expérimentationsN a classical combinatorial optimization setting, given an instance of a problem one needs to find a good feasible solution. However, in many situations, the data may evolve over time and one has to solve a sequence of instances. Gupta et al. (2014) and Eisenstat et al. (2014) proposed a multistage model where given a time horizon the input is a sequence of instances (one for each time step), and the goal is to find a sequence of solutions (one for each time step) reaching a trade-off between the quality of the solutions in each time step and the stability/similarity of the solutions in consecutive time steps. In Chapter 1 of the thesis, we will present an overview of optimization problems tackling evolving data. Then, in Chapter 2, the multistage knapsack problem is addressed in the offline setting. The main contribution is a polynomial time approximation scheme (PTAS) for the problem in the offline setting. In Chapter 3, the multistage framework is studied for multistage problems in the online setting. The main contribution of this chapter was the introduction of a structure for these problems and almost tight upper and lower bounds on the best-possible competitive ratio for these models. Finally in chapter 4 is presented a direct application of the multistage framework in a musical context i.e. the target-based computed-assisted orchestration problem. Is presented a theoretical analysis of the problem, with NP-hardness and approximation results as well as some experimentations
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Røstad, Lars Dybsjord, and Jeanette Christine Erichsen. "Investments in the LNG Value Chain : A Multistage Stochastic Optimization Model focusing on Floating Liquefaction Units." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for industriell økonomi og teknologiledelse, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-20980.
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In this thesis, we have developed a strategic optimization model of investments in infrastructure in the LNG value chain. The focus is on floating LNG production units: when they are a viable solution and what value they add to the LNG value chain. First a deterministic model is presented with focus on describing the value chain, before it is expanded to a multistage stochastic model with uncertain field sizes and gas prices. The objective is to maximize expected discounted profits through optimal investments in infrastructure. A dataset based on a set of potential fields on the Norwegian continental shelf, with shipping of LNG to three markets in the Atlantic basin, is used to solve the model. The results illustrate when FLNG units can add value to the value chain. They are used as a supplement to onshore processing plants; for example expanding peak capacity or to react to the resolution of uncertain parameters. The floating liquefaction option is especially attractive for fields located far from shore. We also find that the main reason for using FLNG units is their lower liquefaction costs, not the ability to move between fields. The stochastic version of the model results in solutions very similar to the solutions of the deterministic model, even though it is significantly harder to solve. Dantzig-Wolfe decomposition is implemented to reduce run times, but does not converge.
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Solak, Senay. "Efficient Solution Procedures for Multistage Stochastic Formulations of Two Problem Classes." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19812.
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We consider two classes of stochastic programming models which are motivated by two applications related to the field of aviation. The first problem we consider is the network capacity planning problem, which arises in capacity planning of systems with network structures, such as transportation terminals, roadways and telecommunication networks. We study this problem in the context of airport terminal capacity planning. In this problem, the objective is to determine the optimal design and expansion capacities for different areas of the terminal in the presence of uncertainty in future demand levels and expansion costs, such that overall passenger delay is minimized. We model this problem as a nonlinear multistage stochastic integer program with a multicommodity network flow structure. The formulation requires the use of time functions for maximum delays in passageways and processing stations, for which we derive approximations that account for the transient behavior of flow. The deterministic equivalent of the developed model is solved via a branch and bound procedure, in which a bounding heuristic is used at the nodes of the branch and bound tree to obtain integer solutions. In the second study, we consider the project portfolio optimization problem. This problem falls in the class of stochastic programs in which times of uncertainty realizations are dependent on the decisions made. The project portfolio optimization problem deals with the selection of research and development (R&D) projects and determination of optimal resource allocations for the current planning period such that the expected total discounted return or a function of this expectation for all projects over an infinite time horizon is maximized, given the uncertainties and resource limitations over a planning horizon. Accounting for endogeneity in some parameters, we propose efficient modeling and solution approaches for the resulting multistage stochastic integer programming model. We first develop a formulation that is amenable to scenario decomposition, and is applicable to the general class of stochastic problems with endogenous uncertainty. We then demonstrate the use of the sample average approximation method in solving large scale problems of this class, where the sample problems are solved through Lagrangian relaxation and lower bounding heuristics.
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Ostermaier, Georg. "Electric power system scheduling by multistage stochastic programming : an optimization approach to profitability in volatile electricity markets /." [S.l.] : [s.n.], 2001. http://aleph.unisg.ch/hsgscan/hm00151473.pdf.
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Al-Shayji, Khawla Abdul Mohsen. "Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30462.
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This dissertation introduces desalination processes in general and multistage flash (MSF) and reverse osmosis (RO) in particular. It presents the fundamental and practical aspects of neural networks and provides an overview of their structures, topology, strengths, and limitations. This study includes the neural network applications to prediction problems of large-scale commercial MSF and RO desalination plants in conjunction with statistical techniques to identify the major independent variables to optimize the process performance.
In contrast to several recent studies, this work utilizes actual operating data (not simulated) from a large-scale commercial MSF desalination plant (48 million gallonsper day capacity, MGPD) and RO plant (15 MGPD) located in Kuwait and the Kingdom of Saudi Arabia, respectively. We apply Neural Works Professional II/Plus (NeuralWare, 1993) and SAS (SAS Institute Inc., 1996) software to accomplish this task.
This dissertation demonstrates how to apply modular and equation-solving approaches for steady-state and dynamic simulations of large-scale commercial MSF desalination plants using ASPEN PLUS (Advanced System for Process Engineering PLUS) and SPEEDUP (Simulation Program for Evaluation and Evolutionary Design of Unsteady Processes) marketed by Aspen Technology, Cambridge, MA.
This work illustrates the development of an optimal operating envelope for achieving a stable operation of a commercial MSF desalination plant using the SPEEDUP model. We then discuss model linearization around nominal operating conditions and arrive at pairing schemes for manipulated and controlled variables by interaction analysis. Finally, this dissertation describes our experience in applying a commercial software, DynaPLUS, for combined steady-state and dynamic simulations of a commercial MSF desalination plant.
This dissertation is unique and significant in that it reports the first comprehensive study of predictive modeling, simulation, and optimization of large-scale commercial desalination plants. It is the first detailed and comparative study of commercial desalination plants using both artificial intelligence and computer-aided design techniques. The resulting models are able to reproduce accurately the actual operating data and to predict the optimal operating conditions of commercial desalination plants.Ph. D.
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Li, Mengting Verfasser], and Michael Z. [Akademischer Betreuer] [Hou. "Optimization of multistage hydraulic fracturing treatment for maximization of the tight gas productivity / Mengting Li ; Betreuer: Michael Z. Hou." Clausthal-Zellerfeld : Technische Universität Clausthal, 2019. http://d-nb.info/1231363568/34.
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Tekaya, Wajdi. "Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47582.
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The main objective of this thesis is to investigate risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems. The purpose of hydrothermal system operation planning is to define an operation strategy which, for each stage of the planning period, given the system state at the beginning of the stage, produces generation targets for each plant. This problem can be formulated as a large scale multistage stochastic linear programming problem. The energy rationing that took place in Brazil in the period 2001/2002 raised the question of whether a policy that is based on a criterion of minimizing the expected cost (i.e. risk neutral approach) is a valid one when it comes to meet the day-to-day supply requirements and taking into account severe weather conditions that may occur. The risk averse methodology provides a suitable framework to remedy these deficiencies. This thesis attempts to provide a better understanding of the risk averse methodology from the practice perspective and suggests further possible alternatives using robust optimization techniques. The questions investigated and the contributions of this thesis are as follows.
First, we suggest a multiplicative autoregressive time series model for the energy inflows that can be embedded into the optimization problem that we investigate. Then, computational aspects related to the stochastic dual dynamic programming (SDDP) algorithm are discussed. We investigate the stopping criteria of the algorithm and provide a framework for assessing the quality of the policy. The SDDP method works reasonably well when the number of state variables is relatively small while the number of stages can be large. However, as the number of state variables increases the convergence of the SDDP algorithm can become very slow. Afterwards, performance improvement techniques of the algorithm are discussed. We suggest a subroutine to eliminate the redundant cutting planes in the future cost functions description which allows a considerable speed up factor. Also, a design using high performance computing techniques is discussed. Moreover, an analysis of the obtained policy is outlined with focus on specific aspects of the long term operation planning problem. In the risk neutral framework, extreme events can occur and might cause considerable social costs. These costs can translate into blackouts or forced rationing similarly to what happened in 2001/2002 crisis. Finally, issues related to variability of the SAA problems and sensitivity to initial conditions are studied. No significant variability of the SAA problems is observed.
Second, we analyze the risk averse approach and its application to the hydrothermal operation planning problem. A review of the methodology is suggested and a generic description of the SDDP method for coherent risk measures is presented. A detailed study of the risk averse policy is outlined for the hydrothermal operation planning problem using different risk measures. The adaptive risk averse approach is discussed under two different perspectives: one through the mean-$avr$ and the other through the mean-upper-semideviation risk measures. Computational aspects for the hydrothermal system operation planning problem of the Brazilian interconnected power system are discussed and the contributions of the risk averse methodology when compared to the risk neutral approach are presented. We have seen that the risk averse approach ensures a reduction in the high quantile values of the individual stage costs. This protection comes with an increase of the average policy value - the price of risk aversion. Furthermore, both of the risk averse approaches come with practically no extra computational effort and, similarly to the risk neutral method, there was no significant variability of the SAA problems.
Finally, a methodology that combines robust and stochastic programming approaches is investigated. In many situations, such as the operation planning problem, the involved uncertain parameters can be naturally divided into two groups, for one group the robust approach makes sense while for the other the stochastic programming approach is more appropriate. The basic ideas are discussed in the multistage setting and a formulation with the corresponding dynamic programming equations is presented. A variant of the SDDP algorithm for solving this class of problems is suggested. The contributions of this methodology are illustrated with computational experiments of the hydrothermal operation planning problem and a comparison with the risk neutral and risk averse approaches is presented. The worst-case-expectation approach constructs a policy that is less sensitive to unexpected demand increase with a reasonable loss on average when compared to the risk neutral method. Also, we comp are the suggested method with a risk averse approach based on coherent risk measures. On the one hand, the idea behind the risk averse method is to allow a trade off between loss on average and immunity against unexpected extreme scenarios. On the other hand, the worst-case-expectation approach consists in a trade off between a loss on average and immunity against unanticipated demand increase. In some sense, there is a certain equivalence between the policies constructed using each of these methods.
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Leng, Yujun. "Preliminary design tools in turbomachinery| Non-uniformly spaced blade rows, multistage interaction, unsteady radial waves, and propeller horizontal-axis turbine optimization." Thesis, Purdue University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10149746.
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Turbomachinery flow fields are inherently unsteady and complex which makes the related CFD analyses computationally intensive. Physically based preliminary design tools are desirable for parametric studies early in the design stage, and to provide deep physical insight and a good starting point for the later CFD analyses. Four analytical/semi-analytical models are developed in this study: 1) a generalized flat plate cascade model for investigating the unsteady aerodynamics of a blade row with non-uniformly spaced blades; 2) a multistage interaction model for investigating rotor-stator interactions; 3) an analytical solution for quantifying the impeller wake convection and pressure wave propagating between a centrifugal compressor impeller and diffuser vane; and 4) a semi-analytical model based Lifting line theory for unified propeller and horizontal-axis turbine optimization. Each model has been thoroughly validated with existing models.
With these models, non-uniformly spaced blade rows and vane clocking are investigated in detail for their potential use as a passive control technique to reduce forced response, flutter and aeroacoustic problems in axial compressors. Parametric studies with different impeller blade numbers and back sweep angles are conducted to investigate their effect on impeller wake and pressure wave propagation. Results show that the scattered pressure waves with high circumferential wave numbers may be an important excitation source to the impeller as their amplitude grows much faster as they travel inwardly than the lower order primary pressure waves. Detailed analysis of Lifting line theory reveals the mathematical and physical equivalence of Lifting line models for propellers and horizontal-axis turbines. With a new implementation, the propeller optimization code can be used for horizontal-axis turbine optimization without any modification. The newly developed unified propeller and horizontal-axis turbine optimization code based on lifting line theory and interior point method has been shown to be a very versatile tool with the capability of hub modelling, working with non-uniform inflow and including extra user specified constraints.
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Caliari, Daniele. "Development and optimization of surface hardening treatments and anodizing processes." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3422679.
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The optimization of metallic solid surfaces is one of the greatest industrial challenges about surface engineering. Surface hardening treatments are applied to improve the wear resistance of parts without affecting the more tough and soft core of the treated substrates.
This doctoral work is focused on the study of gaseous multistage oxynitrocarburizing treatments for low carbon steels and hard-anodizing processes applied on aluminum HPDC components. There is still a lack of knowledge both in literature and in industry about these treatments. The aim of this work is to investigate the microstructural evolution during gaseous multistage oxynitrocarburizing treatments and the impact of the substrate’s characteristics on the hard-anodic layer, respectively. It is important to understand the impact of the process parameters and the substrate’s microstructure on the respective resulting layers; in this way, it is possible to improve the scientific knowledge and therefore to understand the basics for following replications in an industrial context. A wide-ranging view of the whole thermochemical and hard-anodizing processes has been provided by a review of the literature and several treatments replicated in an industrial plant.L’ottimizzazione delle performance di superfici metalliche è una delle sfide industriali più avvincenti nell’ambito dell’ingegneria delle superfici. I trattamenti di indurimento superficiale sono quei processi sviluppati per incrementare la durezza e resistenza a usura superficiali di componenti senza però peggiorare la tenacità che presentano a cuore. Questo lavoro di dottorato è focalizzato sullo studio sia di trattamenti multistadio di ossinitrocarburazione applicati a substrati in acciaio basso legato, sia di trattamenti di anodizzazione dura applicati a componenti pressocolati in lega di alluminio. Attualmente vi sono ancora importanti lacune riguardo queste specifiche tipologie di trattamenti, sia in letteratura che nel mondo industriale. L’obiettivo di questo lavoro è approfondire sia l’evoluzione microstrutturale durante un trattamento multistadio di ossinitrocarburazione applicato a un substrato ferroso, sia come le caratteristiche microstrutturali di un substrato in lega di alluminio impattano sul film di ossido anodico duro. L’obiettivo finale è quello di ampliare il sapere scientifico e, quindi, gettare le basi per poter poi replicare con successo su scala industriale quanto studiato in laboratorio. Un’accurata revisione della letteratura e una serie di trattamenti replicati in impianti industriali ha portato a una visione ad ampio spettro su questi trattamenti termochimici e di ossidazione anodica dura.
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Rieß, Steffen [Verfasser]. "Architecture Optimization and Implementation of a Radio Receiver with a Multistage Spectrum Sensing Technique as Part of a Low-Cost Spectrum Sensing Grid / Steffen Rieß." München : Verlag Dr. Hut, 2016. http://d-nb.info/1097818098/34.
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Vigerske, Stefan. "Decomposition in multistage stochastic programming and a constraint integer programming approach to mixed-integer nonlinear programming." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2013. http://dx.doi.org/10.18452/16704.
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Diese Arbeit leistet Beiträge zu zwei Gebieten der mathematischen Programmierung: stochastische Optimierung und gemischt-ganzzahlige nichtlineare Optimierung (MINLP). Im ersten Teil erweitern wir quantitative Stetigkeitsresultate für zweistufige stochastische gemischt-ganzzahlige lineare Programme auf Situationen in denen Unsicherheit gleichzeitig in den Kosten und der rechten Seite auftritt, geben eine ausführliche Übersicht zu Dekompositionsverfahren für zwei- und mehrstufige stochastische lineare und gemischt-ganzzahlig lineare Programme, und diskutieren Erweiterungen und Kombinationen des Nested Benders Dekompositionsverfahrens und des Nested Column Generationsverfahrens für mehrstufige stochastische lineare Programme die es erlauben die Vorteile sogenannter rekombinierender Szenariobäume auszunutzen. Als eine Anwendung dieses Verfahrens betrachten wir die optimale Zeit- und Investitionsplanung für ein regionales Energiesystem unter Einbeziehung von Windenergie und Energiespeichern. Im zweiten Teil geben wir eine ausführliche Übersicht zum Stand der Technik bzgl. Algorithmen und Lösern für MINLPs und zeigen dass einige dieser Algorithmen innerhalb des constraint integer programming Softwaresystems SCIP angewendet werden können. Letzteres erlaubt uns die Verwendung schon existierender Technologien für gemischt-ganzzahlige linear Programme und constraint Programme für den linearen und diskreten Teil des Problems. Folglich konzentrieren wir uns hauptsächlich auf die Behandlung der konvexen und nichtkonvexen nichtlinearen Nebenbedingungen mittels Variablenschrankenpropagierung, äußerer Approximation und Reformulierung. In einer ausführlichen numerischen Studie untersuchen wir die Leistung unseres Ansatzes anhand von Anwendungen aus der Tagebauplanung und des Aufbaus eines Wasserverteilungssystems und mittels verschiedener Vergleichstests. Die Ergebnisse zeigen, dass SCIP ein konkurrenzfähiger Löser für MINLPs geworden ist.This thesis contributes to two topics in mathematical programming: stochastic optimization and mixed-integer nonlinear programming (MINLP). In the first part, we extend quantitative continuity results for two-stage stochastic mixed-integer linear programs to include situations with simultaneous uncertainty in costs and right-hand side, give an extended review on decomposition algorithm for two- and multistage stochastic linear and mixed-integer linear programs, and discuss extensions and combinations of the Nested Benders Decomposition and Nested Column Generation methods for multistage stochastic linear programs to exploit the advantages of so-called recombining scenario trees. As an application of the latter, we consider the optimal scheduling and investment planning for a regional energy system including wind power and energy storages. In the second part, we give a comprehensive overview about the state-of-the-art in algorithms and solver technology for MINLPs and show that some of these algorithm can be applied within the constraint integer programming framework SCIP. The availability of the latter allows us to utilize the power of already existing mixed integer linear and constraint programming technologies to handle the linear and discrete parts of the problem. Thus, we focus mainly on the domain propagation, outer-approximation, and reformulation techniques to handle convex and nonconvex nonlinear constraints. In an extensive computational study, we investigate the performance of our approach on applications from open pit mine production scheduling and water distribution network design and on various benchmarks sets. The results show that SCIP has become a competitive solver for MINLPs.
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Mohammadi, Mehrdad. "A multi-objective optimization framework for an inspection planning problem under uncertainty and breakdown." Thesis, Paris, ENSAM, 2015. http://www.theses.fr/2015ENAM0055/document.
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Dans les systèmes manufacturiers de plus en plus complexes, les variations du processus de fabrication et de ses paramètres opératoires ainsi que leurs effets sur l’ensemble du système doivent être maîtrisés, mesurés et contrôlés. Cette thèse propose un cadre d’optimisation pour l’élaboration d’un plan d’inspection optimal qui permet une prise de décision opérationnelle afin d’assurer la satisfaction des objectifs stratégiques (réduction des coûts, amélioration de la qualité, augmentation de la productivité, …). La prise de décision se divise en trois questions : Quoi contrôler ? Comment contrôler ? Quand contrôler ? Le manque d'informations fiables sur les processus de production et plusieurs facteurs environnementaux est devenu un problème important qui impose la prise en compte de certaines incertitudes lors de la planification des inspections. Cette thèse propose plusieurs formulations du problème d’optimisation de la planification du processus d'inspection, dans lesquelles, les paramètres sont incertains et les machines de production sont sujettes aux défaillances. Ce problème est formulé par des modèles de programmation mathématique avec les objectifs : minimiser le coût total de fabrication, maximiser la satisfaction du client, et minimiser le temps de la production totale. En outre, les méthodes Taguchi et Monte Carlo sont appliquées pour faire face aux incertitudes. En raison de la complexité des modèles proposés, les algorithmes de méta-heuristiques sont utilisés pour trouver les solutions optimalesQuality inspection in multistage production systems (MPSs) has become an issue and this is because the MPS presents various possibilities for inspection. The problem of finding the best inspection plan is an “inspection planning problem”. The main simultaneous decisions in an inspection planning problem in a MPS are: 1) which quality characteristics need to be inspected, 2) what type of inspection should be performed for the selected quality characteristics, 3) where these inspections should be performed, and 4) how the inspections should be performed. In addition, lack of information about production processes and several environmental factors has become an important issue that imposes a degree of uncertainty to the inspection planning problem. This research provides an optimization framework to plan an inspection process in a MPS, wherein, input parameters are uncertain and inspection tools and production machines are subject to breakdown. This problem is formulated through several mixed-integer mathematical programming models with the objectives of minimizing total manufacturing cost, maximizing customer satisfaction, and minimizing total production time. Furthermore, Taguchi and Monte Carlo methods are applied to cope with the uncertainties. Due to the complexity of the proposed models, meta-heuristic algorithms are employed to find optimal or near-optimal solutions. Finally, this research implements the findings and methods of the inspection planning problem in another application as hub location problem. General and detail concluding remarks are provided for both inspection and hub location problems
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An, Na. "Resource Modeling and Allocation in Competitive Systems." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6997.
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This thesis includes three self-contained projects:
In the first project Bidding strategies and their impact on the auctioneer's revenue in combinatorial auctions, focusing on combinatorial auctions, we propose a simple and efficient model for evaluating the value of any bundle given limited information, design bidding strategies that efficiently select desirable bundles, and evaluate the performance of different bundling strategies under various market settings.
In the second project Retailer shelf-space management with promotion effects, promotional investment effects are integrated with retail store assortment decisions and shelf space allocation. An optimization model for the category shelf-space allocation incorporating promotion effects is presented. Based on the proposed model, a category shelf space allocation framework with trade allowances is presented where a multi-player Retailer Stackelberg game is introduced to model the interactions between retailer and manufacturers.
In the third project Supply-chain oriented robust parameter design, we introduce the game theoretical method, commonly used in supply-chain analysis to solve potential conflicts between manufacturers at various stages. These manufacturing chain partners collaboratively decide parameter design settings of the controllable factors to make the product less sensitive to process variations.
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Nasri, Karima. "Frigo pompes à absorption multiétagées de haute performance : simulation et conception d'une maquette expérimentale." Vandoeuvre-les-Nancy, INPL, 1997. http://www.theses.fr/1997INPL054N.
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Ce travail se rapporte à l'étude de pompes à chaleur à absorption multiétagées, dans le but de développer des nouvelles machines frigorifiques de faible puissance pour la climatisation des bâtiments et utilisant la combustion du gaz naturel comme source de chaleur. Ce type de travail fait l'objet d'une demande de plus en plus importante car il s'avère être une solution de substitution intéressante à l'interdiction récente de production et d'utilisation des composés fluorés tels que les CFC (ChloroFluoroCarbures) et les HCFC (HydroChloroFluoroCarbures). Nous proposons donc dans ce travail, des structures de pompes à chaleur à absorption multiétagées pour frigo pompes qui offrent une excellente voie d'amélioration des performances par rapport aux systèmes simples mono-étages. Dans cette étude, nous nous intéressons plus particulièrement à développer les structures multiétagées en parallèle thermique au niveau du mélangeur, et ce pour une production maximale de froid utile. Ce principe nécessite un mélange qui présente un large domaine de travail (en pression et température). Le mélange utilisé dans cette étude est le couple Nh3/H2O de par ses propriétés et les données thermodynamiques le concernant qui sont assez bien connues. Pour améliorer les performances de ces systèmes, nous distinguons deux grandes voies. La première voie consiste à essayer de profiter de ce besoin de rectification, tandis que la seconde voie cherche à éliminer cette rectification.
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Frithiof, Fredrik. "A framework for designing a modular muffler system by global optimization." Thesis, KTH, Optimeringslära och systemteori, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169650.
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When creating a muffler to be installed on a noise generating machine, the design parameters as well as the placements of sound attenuating elements has to be optimized in order to minimize the sound coming out of the equipage. This is exemplified in a small project task for students of a basic course in optimization at KTH. The task is however flawed, since both the way in which the optimization problem is formed is overly simplistic and the algorithm used to solve the problem, fmincon, does not cope well with the mathematical complexity of the model, meaning it gets stuck in a local optimum that is not a global optimum. This thesis is about investigating how to solve both of these problems. The model is modified to combine several frequencies and adjusting them to the sensitivity to different frequencies in the human ear. By doing this, the objective is changed from the previous way of maximizing Dynamic Insertion Loss Dilfor a specific frequency to minimize the total perceived sound level LA. The model is based on the modular design of TMM from four-pole theory. This divides the muffler into separate parts, with the sound attenuating elements being mathematically defined only by what T matrix it has. The element types to choose from are the Expansion Chamber, the Quarter Wave Resonator and the Helmholtz Resonator. The global optimization methods to choose from are Global Search, MultiStart, Genetic Algorithm, Pattern Search and Simulated Annealing. By combining the different types of sound attenuating elements in every way and solving each case with every global optimization method, the best combination to implement to the model is chosen. The choice is two Quarter Wave Resonators being solved by MultiStart, which provides satisfactory results. Further analysis is done to ensure the robustness of chosen implementation, which does not reveal any significant flaws. The purpose of this thesis is fulfilled.När man skapar en ljuddämpare som ska installeras på en ljud-genererande maskin bör designparametrarna samt placeringarna av ljuddämpande element optimeras för att minimera ljudet som kommer ut ur ekipaget. Detta exemplifieras i en liten projektuppgift för studenter till en grundkurs i optimering på KTH. Uppgiften är dock bristfällig, eftersom både det sätt som optimeringsproblemet är utformat är alltför förenklat och den algoritm som används för att lösa problemet, fmincon, inte klarar av modellens matematiska komplexitet bra, vilket menas med att den fastnar i ett lokalt optimum som inte är ett globalt optimum. Detta examensarbete handlar om att undersöka hur man kan lösa båda dessa problem. Modellen är modifierad för att kombinera flera frekvenser och anpassa dem till känsligheten för olika frekvenser i det mänskliga örat. Genom att göra detta är målet ändrat från det tidigare sättet att maximera den dynamiska insatsisoleringen DIL för en specifik frekvens till att minimera den totala upplevda ljudnivån LA. Modellen bygger på den modulära designen av TMM från 4-polsteori. Detta delar upp ljuddämparen i separata delar, med ljuddämpande element som matematiskt endast definieras av vilken T matris de har. De elementtyper att välja mellan är expansionskammare, kvartsvågsresonator och Helmholtzresonator. De globala optimeringsmetoder att välja mellan är Global Search, MultiStart, Genetic Algorithm, Pattern Search och Simulated Annealing. Genom att kombinera de olika typerna av ljuddämpande element på alla sätt och lösa varje fall med varje global optimeringsmetod, blir den bästa kombinationen vald och implementerad i modellen. Valet är två kvartsvågsresonatorer som löses genom MultiStart, vilket ger tillfredsställande resultat. Ytterligare analyser görs för att säkerställa robustheten av den valda implementationen, som inte avslöjar några väsentliga brister. Syftet med detta examensarbete är uppfyllt.
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Pendleton, Tyler M. "Design and Fabrication of Rotationally Tristable Compliant Mechanisms." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1552.pdf.
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Hussein, Hussein. "Contribution to digital microrobotics : modeling, design and fabrication of curved beams, U-shaped actuators and multistable microrobots." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2048/document.
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Un nombre de sujets concernant la microrobotique numérique ont été abordés dans le cadre de cette the` se. Une nouvelle génération du microrobot numérique ”DiMiBot” a e´ te´ proposé ce qui rend le DiMiBot plus précis, plus contrôlable et plus petit. La nouvelle structure est formée de deux modules multistables seulement, ce qui ajoute des fonctionnalités´ s importantes comme l’augmentation du nombre de positions avec une taille plus réduite et la capacité´ de réaliser des trajectoires complexes dans l’espace de travail. Le principe du nouveau module multistable combine les avantages des microactionneurs pas à pas en termes du principe et du concept numérique en termes de la répétabilité et la robustesse en boucle ouverte. Un mécanisme de positionnement précis, capable de compenser les incertitudes de fabrication a e´ te´ développé et utilise´ pour assurer un positionnement précis. En parallèle, des modèles analytiques ont e´ te´ développés pour les principaux composants dans le DiMiBot: poutres flambées préformées et actionneurs e´ électrothermiques en U. Des méthodes de conception ont été développées par la suite qui permettent de choisir les dimensions optimales garantissant les performances requises en respectant les spécifications et limites de design. Des prototypes de modules multistables, fabrique´ s dans la salle Blanche MIMENTO, ont montré´ un bon Fonctionnement dans les expériencesA number of topics concerning digital microrobotics were addressed in this thesis. A new generation of the digital microrobot ”DiMiBot” was proposed with several advantages making the DiMiBot more accurate, more controllable and smaller. The new structure consists of only two multistable modules which adds some important features such as increasing the number of positions with smaller size and the ability to realize complex trajectories in the workspace. The principle of the new multistable module combines the advantages of the stepping microactuators in terms of the principle and of the digital concept in terms of the repeatability and robustness without feedback. The accuracy is ensured with an accurate positioning mechanism that compensate the fabrication tolerances. In parallel, analytical models was developed for the main components in the DiMiBot: preshaped curved beams and U-shaped electrothermal actuators. Subsequently, design methods were developed that allow choosing the optimal dimensions that ensure the desired outputs and respecting the design specifications and limitations. Multistable module prototypes, fabricated in the clean room MIMENTO, showed a proper functioning in the experiments
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Ertl, Jakub. "Spolehlivost výkonových olejových transformátorů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-234174.
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The doctoral thesis deals with design of non-invasive methods for estimated reliability analysis of power oil transformers. Such procedures also solve two-state and multistate reliability problems together with applying of gained reliability characteristics in planning further operation of these non-rotating electrical machines. Reliability module for diagnostic system, which is developed at the training workplace, is designed in conclusion of this thesis. All the above procedures were verified on data obtained from diagnostic measurements of ten power oil transformers operating in different power plants in the Czech Republic.
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Linowsky, Karsten. "Sampling-based decomposition methods in multistage stochastic optimization /." 2005. http://www.gbv.de/dms/zbw/50285023X.pdf.
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Chen, Wei 1974. "Multistage stochastic programming models for the portfolio optimization of oil projects." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-3884.
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Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business.
This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques.
Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk.
For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects.
For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm.
The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information.text
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Ming-TsungHung and 洪銘聰. "Using Simulation Optimization to Choose an Optimal Inspection Policy in Multistage Production System." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/kwcn27.
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碩士國立成功大學
工業與資訊管理學系
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The aim of this study is to develop a heuristic for a multistage production system to find the optimal sample size and acceptance number for each stage, which can minimize the total cost while maintaining the required average outgoing quality. The heuristic uses metamodel to reduce the required replications of simulation, and combines with multiple feasibility check procedure to guarantee that the solution we found is feasible with respect to multiple stochastic constraints under a specified confidence level. The heuristic is compared with the OptQuest which is commonly embedded in Arena. The results show that our heuristic outperforms OptQuest in terms of objective values and the probability of finding feasible solutions.
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Queiroz, Anderson Rodrigo de. "A sampling-based decomposition algorithm with application to hydrothermal scheduling : cut formation and solution quality." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4690.
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We consider a hydrothermal scheduling problem with a mid-term horizon(HTSPM) modeled as a large-scale multistage stochastic program with stochastic monthly inflows of water to each hydro generator. In the HTSPM we seek an operating policy to minimize the sum of present and expected future
costs, which include thermal generation costs and load curtailment costs. In addition to various simple bounds, problem constraints involve water balance,
demand satisfaction and power interchanges.
Sampling-based decomposition algorithms (SBDAs) have been used in the literature to solve HTSPM. SBDAs can be used to approximately solve problem instances with many time stages and with inflows that exhibit interstage dependence. Such dependence requires care in computing valid cuts for the decomposition algorithm.
In order to help maintain tractability, we employ an aggregate reservoir representation (ARR). In an ARR all the hydro generators inside a specific
region are grouped to effectively form one hydro plant with reservoir storage and generation capacity proportional to the parameters of the hydro plants
used to form that aggregate reservoir. The ARR has been used in the literature with energy balance constraints, rather than water balance constraints, coupled with time series forecasts of energy inflows. Instead, we prefer as a model primitive to have the time series model forecast water inflows. This, in turn, requires that we extend existing methods to compute valid cuts for the decomposition method under the resulting form of interstage dependence.
We form a sample average approximation of the original problem and then solve this problem by these special-purpose algorithms. And, we assess the quality of the resulting policy for operating the system. In our analysis, we compute a confidence interval on the optimality gap of a policy generated by solving an approximation on a sampled scenario tree. We present computational results on test problems with 24 monthly stages in which the inter-stage dependency of hydro inflows is modeled using a dynamic linear model. We further
develop a parallel implementation of an SBDA. We apply SBDA to solve
the HTSPM for the Brazilian power system that has 150 hydro generators, 151 thermal generators and 4 regions that each characterize an aggregate reservoir.
We create and solve four different HTSPM instances where we change the input parameters with respect to generation capacity, transmission capacity and load in order to analyze the difference in the total expected cost.text
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YANG, PEI-JUN, and 楊佩君. "Design and stepwise set-point optimization for multistage adiabatic reactors subject to catalyst deactivation with interstage heat exchange and cold shot cooling." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/31151593282455931621.
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Horejšová, Markéta. "Vícestupňové vnořené vzdálenosti v stochastické optimalizaci." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-382742.
Full textAbstract:
Multistage stochastic optimization is used to solve many real-life problems where decisions are taken at multiple times, e.g., portfolio selection problems. Such problems need the definition of stochastic processes, which are usually approxim- ated by scenario trees. The choice of the size of the scenario trees is the result of a compromise between the best approximation and the possibilities of the com- puter technology. Therefore, once a master scenario tree has been generated, it can be needed to reduce its dimension in order to make the problem computation- ally tractable. In this thesis, we introduce several scenario reduction algorithms and we compare them numerically for different types of master trees. A simple portfolio selection problem is also solved within the study. The distance from the initial scenario tree, the computational time, and the distance between the optimal objective values and solutions are compared for all the scenario reduction algorithms. In particular, we adopt the nested distance to measure the distance between two scenario trees. 1
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Uhliar, Miroslav. "Ekonomické růstové modely ve stochastickém prostředí." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-367898.
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Cordeiro, Tiago Alexandre Barrinha. "A global optimization algorithm using trust-region methods and clever multistart." Master's thesis, 2021. http://hdl.handle.net/10362/135864.
Full textAbstract:
Global optimization is an important scientific domain, not only due to the algorithmic
challenges associated with this area, but also due to its practical application in different
areas of knowledge, from Biology to Aerospace Engineering.
In this work we develop an algorithm based on trust-region methods for solving
global optimization problems with derivatives, using a clever multistart strategy, testing
its efficiency and effectiveness by comparison with other global optimization algorithms.
Based on an idea applied to the resolution of problems in derivative-free optimization,
this algorithm seeks to reduce the computational effort that the search for a global
optimum requires, by comparing points that are relatively close to each other, using as
comparison radius the one associated with the trust-region method, retaining only the
most promising ones, which will continue to be explored. The proposed method has the
added benefit of not only reporting the global optimum but also a list of local optima that
may be of interest, depending on the context of the problem in question.A otimização global é um importante domínio científico, não só pelos desafios algorítmicos que lhe estão associados, mas pela sua aplicação prática em diferentes áreas do conhecimento, que vão desde a Biologia à Engenharia Aeroespacial. Neste trabalho é desenvolvido um algoritmo baseado em métodos de regiões de confiança, para problemas de otimização global com derivadas, usando uma estratégia de multi-inicializações inteligente, sendo testada a sua eficiência e eficácia por comparação com outros algoritmos de otimização global. Baseado numa ideia aplicada à resolução de problemas de otimização sem derivadas, este algoritmo procura reduzir o esforço computacional que a busca de ótimos globais requer, comparando pontos que se situam relativamente próximos usando como raio de comparação o raio associado ao método de região de confiança, e retendo apenas os mais promissores, que continuarão a ser explorados. O método proposto permite não só a obtenção do ótimo global mas também de uma lista de ótimos locais que podem ser de interesse, dependendo do contexto do problema em questão.
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Huang, Sheng-Fu, and 黃聖富. "Multi-objective Optimization for a Multistate Job- Shop Production Network Using NSGA-II and TOPSIS." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sw79cy.
Full textAbstract:
碩士國立臺灣科技大學
工業管理系
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A job-shop production system (JPS) is a general manufacturing system. In a JPS, each workstation configures multiple types of machines in order to increase flexibility of production. In a JPS, the number of normal machines in each workstation presents multiple levels due to partial failures, unexpected failures, and maintenance, etc. Therefore, it is suitable to state that the number of normal machines in each workstation is stochastic (i.e. multistate). To reflect the phenomenon of stochastic number of normal machines, network reliability can assess the performance of a JPS facing uncertain demand. The multi-objective optimization in this thesis is focusing on maximizing the network reliability and minimizing the total cost of JPS, which most supervisors pursue. In order to solve the multi-objective optimization problem, we separate it into two parts. First, we transform JPS into a multistate job-shop production network (MJPN) by using network topology, proposing an algorithm to evaluate network reliability. The major difficulty in evaluating network reliability of the MJPN is that the state distribution is not determined. When the number of machine types is large, it is impossible to calculate the probability one by one. Therefore, a machine vector (MV), representing the current number of normal machines in a workstation, is introduced to overcome the difficulty. We propose an algorithm based on the depth-first search (DFS) with special expanding technique, to search all MVs, which satisfying demand. Second, to search the machine configuration (MF) with maximal network reliability and minimal total cost simultaneously, we propose a two-stage approach based on NSGA-II and TOPSIS. In addition, a real case of t-shirt production is utilized to illustrate the proposed method. Supervisors can apply it to find the proper MF based on their preference.