Дисертації з теми "Mathematical programming formulation"
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Grodal, Evert Olaus. "Designing primary hydrocarbon production separation systems : a mathematical programming formulation." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/12084.
Повний текст джерелаBrooks, J. Paul. "Solving a mixed-integer programming formulation of a classification model with misclassification limits." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-08232005-133023/.
Повний текст джерелаPrausnitz, Mark, Committee Member ; Vidakovic, Brani, Committee Member ; Lee, Eva, Committee Chair ; Nemhauser, George, Committee Member ; Johnson, Ellis, Committee Member. Includes bibliographical references.
Abdullah, Ali H. "The weighted maximal planar graph : mathematical formulations and solutions." Thesis, University of Kent, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250315.
Повний текст джерелаMelo, Márcio Daniel Tavares de. "Network virtualisation from an operator perspective." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/12876.
Повний текст джерелаNetwork virtualisation is seen as a promising approach to overcome the so-called “Internet impasse” and bring innovation back into the Internet, by allowing easier migration towards novel networking approaches as well as the coexistence of complementary network architectures on a shared infrastructure in a commercial context. Recently, the interest from the operators and mainstream industry in network virtualisation has grown quite significantly, as the potential benefits of virtualisation became clearer, both from an economical and an operational point of view. In the beginning, the concept has been mainly a research topic and has been materialized in small-scale testbeds and research network environments. This PhD Thesis aims to provide the network operator with a set of mechanisms and algorithms capable of managing and controlling virtual networks. To this end, we propose a framework that aims to allocate, monitor and control virtual resources in a centralized and efficient manner. In order to analyse the performance of the framework, we performed the implementation and evaluation on a small-scale testbed. To enable the operator to make an efficient allocation, in real-time, and on-demand, of virtual networks onto the substrate network, it is proposed a heuristic algorithm to perform the virtual network mapping. For the network operator to obtain the highest profit of the physical network, it is also proposed a mathematical formulation that aims to maximize the number of allocated virtual networks onto the physical network. Since the power consumption of the physical network is very significant in the operating costs, it is important to make the allocation of virtual networks in fewer physical resources and onto physical resources already active. To address this challenge, we propose a mathematical formulation that aims to minimize the energy consumption of the physical network without affecting the efficiency of the allocation of virtual networks. To minimize fragmentation of the physical network while increasing the revenue of the operator, it is extended the initial formulation to contemplate the re-optimization of previously mapped virtual networks, so that the operator has a better use of its physical infrastructure. It is also necessary to address the migration of virtual networks, either for reasons of load balancing or for reasons of imminent failure of physical resources, without affecting the proper functioning of the virtual network. To this end, we propose a method based on cloning techniques to perform the migration of virtual networks across the physical infrastructure, transparently, and without affecting the virtual network. In order to assess the resilience of virtual networks to physical network failures, while obtaining the optimal solution for the migration of virtual networks in case of imminent failure of physical resources, the mathematical formulation is extended to minimize the number of nodes migrated and the relocation of virtual links. In comparison with our optimization proposals, we found out that existing heuristics for mapping virtual networks have a poor performance. We also found that it is possible to minimize the energy consumption without penalizing the efficient allocation. By applying the re-optimization on the virtual networks, it has been shown that it is possible to obtain more free resources as well as having the physical resources better balanced. Finally, it was shown that virtual networks are quite resilient to failures on the physical network.
A virtualização de rede é vista como uma abordagem promissora para ultrapassar o “Impasse da Internet” e permitir inovação na Internet, possibilitando assim uma migração fácil para novas abordagens de redes, bem como a coexistência de arquiteturas de redes complementares numa infraestrutura compartilhada e em ambiente comercial. Recentemente tem crescido de forma bastante significativa o interesse pela virtualização de rede por parte dos operadores e dos grandes fabricantes, desde que os potenciais benefícios da virtualização se tornaram claros, tanto de ponto de vista económico como operacional. No início, o conceito foi versado pelo meio académico, onde foram realizadas provas de conceito de pequena escala, e em que a virtualização de rede foi considerada como forma de investigação de novos protocolos. Esta Tese de Doutoramento tem como objetivo geral dotar uma rede de operador de um conjunto de mecanismos e algoritmos capazes de gerir e controlar redes virtuais. Para este fim, é proposta uma framework que visa alocar, monitorizar e controlar recursos virtuais de uma forma centralizada e eficiente. De forma a analisar o desempenho da framework, procedeu-se à sua implementação e avaliação numa rede de pequena dimensão. De forma a permitir que se possa efetuar uma alocação eficiente, em tempo real, e a pedido, de redes virtuais numa rede física, é proposta uma heurística para efetuar o mapeamento na rede física. Para que o operador de rede possa rentabilizar ao máximo a sua infraestrutura de rede, é ainda proposta uma formulação matemática que, através de programação linear, visa maximizar o número de redes alocadas na infraestrutura de rede. Dado que o consumo energético de uma infraestrutura de rede começa a ter significância nos custos de operação, é importante que se faça a alocação das redes virtuais no menor número de recursos físicos e também em recursos físicos ativos. Para endereçar este desafio é proposta uma formulação matemática que visa minimizar o consumo energético da rede física sem afetar a eficiência da alocação de redes virtuais. Para minimizar a fragmentação da infraestrutura de rede e ao mesmo tempo aumentar as receitas do operador, é também estendida a formulação inicial para contemplar a re-otimização de redes virtuais previamente mapeadas, fazendo com que o operador tenha um melhor aproveitamento da sua infraestrutura física. Será ainda necessário endereçar a migração de redes virtuais, quer por motivos de balanceamento de carga, quer por motivos de falha iminente de recursos físicos, sem afetar o bom funcionamento da rede virtual. Para este fim, é proposto um método baseado em técnicas de clonagem, para efetuar a migração de redes virtuais entre recursos da infraestrutura física de forma transparente e sem impacto para a rede virtual. De forma a avaliar a resiliência das redes virtuais a falhas na rede física, e ao mesmo tempo obter a solução ótima de migração de redes virtuais em caso de falha iminente dos recursos físicos, a formulação matemática é estendida para minimizar o número de nós migrados em simultâneo com a realocação de ligações virtuais. Em comparação com as nossas propostas de otimização verificou-se que as heurísticas existentes para mapeamento de redes virtuais têm um desempenho muito baixo. Verificou-se ainda que é possível efetuar a redução do consumo energético sem a penalização da alocação eficiente. Com a re-otimização das redes virtuais mostrou-se que é possível obter mais recursos livres, assim como obter uma melhor distribuição dos recursos. Por último, demonstrou-se que as redes virtuais são bastante resilientes a falhas na rede física.
Dietert, Grant. "An integer linear programming formulation for tiling large rectangles using 4 x 6 and 5 x 7 tiles /." Online version of thesis, 2010. http://hdl.handle.net/1850/12275.
Повний текст джерелаFigueiredo, Tatiane Fernandes. "The socio-technical teams formation problem: Complexity, Mathematical Formulations and Computational Results." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15392.
Повний текст джерелаUtilizando conceitos da Teoria dos Sistemas SociotÃcnicos, este trabalho define matematicamente os problemas de formaÃÃo de equipes cooperativas considerando separadamente restriÃÃes sociais e tÃcnicas e apresenta a complexidade computacional dos mesmos. Sobretudo, à definido e estudado o problema central deste trabalho, que considera conjuntamente requisitos sociais e tÃcnicos para criaÃÃo de equipes de trabalho cooperativo, denominado FEST (Problema de FormaÃÃo de Equipes SociotÃcnicas). Duas formulaÃÃes matemÃticas e uma meta-heurÃstica para o FEST sÃo propostas. Uma formulaÃÃo utiliza um nÃmero cÃbico de variÃveis e restriÃÃes, enquanto a segunda formulaÃÃo possui um nÃmero quadrÃtico de variÃveis, mas um nÃmero exponencial de restriÃÃes. A meta-heurÃstica proposta à baseada no Simulated Annealing NÃo-MonotÃnico com busca local que usa operadores tipo swap. A corretude de ambas as formulaÃÃes à provada. Um algoritmo polinomial para separar as restriÃÃes da segunda formulaÃÃo à apresentado. Mostra-se que as duas formulaÃÃes fornecem o mesmo limite de programaÃÃo linear, e desigualdades vÃlidas para fortalecÃ-lo sÃo propostas. Para a formulaÃÃo compacta, algumas classes de desigualdades vÃlidas sÃo demonstradas indutoras de facetas sob hipÃteses apropriadas. Por fim, foi analisado estatisticamente o desempenho das formulaÃÃes e da meta-heurÃstica apresentadas. InstÃncias reais e geradas aleatoriamente sÃo usadas nos experimentos computacionais.
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.
Повний текст джерелаSmith, Jonathan Cole. "Tight Discrete Formulations to Enhance Solvability with Applications to Production, Telecommunications, and Air Transportation Problems." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/26710.
Повний текст джерелаPh. D.
Boberg, Jessika. "A comparison of sequencing formulations in a constraint generation procedure for avionics scheduling." Thesis, Linköpings universitet, Optimeringslära, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143274.
Повний текст джерелаTaeihagh, Araz. "A novel approach for the development of policies for socio-technical systems." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:0183f800-51bf-4e4d-abba-cd91b7bf48f0.
Повний текст джерела"Dynamic portfolio analysis: mean-variance formulation and iterative parametric dynamic programming." 1998. http://library.cuhk.edu.hk/record=b5889737.
Повний текст джерелаThesis submitted in: November 1997.
On added t.p.: January 19, 1998.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 114-119).
Abstract also in Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview --- p.1
Chapter 1.2 --- Organization Outline --- p.5
Chapter 2 --- Literature Review --- p.7
Chapter 2.1 --- Modern Portfolio Theory --- p.7
Chapter 2.1.1 --- Mean-Variance Model --- p.9
Chapter 2.1.2 --- Setting-up the relationship between the portfolio and its component securities --- p.11
Chapter 2.1.3 --- Identifying the efficient frontier --- p.12
Chapter 2.1.4 --- Selecting the best compromised portfolio --- p.13
Chapter 2.2 --- Stochastic Optimal Control --- p.17
Chapter 2.2.1 --- Dynamic Programming --- p.18
Chapter 2.2.2 --- Dynamic Programming Decomposition --- p.21
Chapter 3 --- Multiple Period Portfolio Analysis --- p.23
Chapter 3.1 --- Maximization of Multi-period Consumptions --- p.24
Chapter 3.2 --- Maximization of Utility of Terminal Wealth --- p.29
Chapter 3.3 --- Maximization of Expected Average Compounded Return --- p.33
Chapter 3.4 --- Minimization of Time to Reach Target --- p.35
Chapter 3.5 --- Goal-Seeking Investment Model --- p.37
Chapter 4 --- Multi-period Mean-Variance Analysis with a Riskless Asset --- p.40
Chapter 4.1 --- Motivation --- p.40
Chapter 4.2 --- Dynamic Mean-Variance Analysis Formulation --- p.43
Chapter 4.3 --- Auxiliary Problem Formulation --- p.45
Chapter 4.4 --- Efficient Frontier in Multi-period Portfolio Selection --- p.53
Chapter 4.5 --- Obseravtions --- p.58
Chapter 4.6 --- Solution Algorithm for Problem E (w) --- p.62
Chapter 4.7 --- Illstrative Examples --- p.63
Chapter 4.8 --- Verification with Single-period Efficient Frontier --- p.72
Chapter 4.9 --- Generalization to Cases with Nonlinear Utility Function of E (xT) and Var (xT) --- p.75
Chapter 5 --- Dynamic Portfolio Selection without Risk-less Assets --- p.84
Chapter 5.1 --- Construction of Auxiliuary Problem --- p.88
Chapter 5.2 --- Analytical Solution for Efficient Frontier --- p.89
Chapter 5.3 --- Reduction to Investment Situations with One Risk-free Asset --- p.101
Chapter 5.4 --- "Multi-period Portfolio Selection via Maximizing Utility function U(E {xT),Var (xT))" --- p.103
Chapter 6 --- Conclusions and Recommendations --- p.108
Chapter 6.1 --- Summaries and Achievements --- p.108
Chapter 6.2 --- Future Studies --- p.110
Chapter 6.2.1 --- Constrained Investment Situations --- p.110
Chapter 6.2.2 --- Including Higher Moments --- p.111
"Goal programming approach for channel assignment formulation and schemes." 2005. http://library.cuhk.edu.hk/record=b5892633.
Повний текст джерелаThesis (M.Phil.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (leaves 70-74).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iii
Preface --- p.x
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Multiple Access --- p.1
Chapter 1.1.1 --- Time Division Multiple Access --- p.2
Chapter 1.1.2 --- Frequency Division Multiple Access --- p.3
Chapter 1.1.3 --- Code Division Multiple Access --- p.3
Chapter 1.1.4 --- Hybrid Multiple Access Scheme --- p.4
Chapter 1.2 --- Goal Programming --- p.5
Chapter 2 --- Previous Works in Channel Assignment --- p.10
Chapter 2.1 --- Voice Service Network --- p.10
Chapter 2.2 --- Data Network --- p.11
Chapter 2.2.1 --- Throughput Optimization --- p.13
Chapter 2.2.2 --- Channel Assignment Schemes with QoS Consideration --- p.14
Chapter 3 --- General Channel Assignment Scheme --- p.16
Chapter 3.1 --- Baseline Model --- p.17
Chapter 3.2 --- Goal Ranking --- p.22
Chapter 3.3 --- Model Transformation --- p.22
Chapter 3.4 --- Proposed Algorithms --- p.23
Chapter 3.4.1 --- Channel Swapping Algorithm --- p.24
Chapter 3.4.2 --- Best-First-Assign Algorithm --- p.26
Chapter 4 --- Special Case Algorithms --- p.28
Chapter 4.1 --- Single Order of Selection Diversity --- p.28
Chapter 4.1.1 --- System Model --- p.29
Chapter 4.1.2 --- Proposed Algorithm --- p.30
Chapter 4.1.3 --- Extension of Algorithm --- p.31
Chapter 4.2 --- Single Channel Assignment --- p.32
Chapter 4.2.1 --- System Model --- p.33
Chapter 4.2.2 --- Proposed Algorithms --- p.34
Chapter 5 --- Performance Evaluation --- p.37
Chapter 5.1 --- General Channel Assignment and Single Channel Assignment --- p.37
Chapter 5.1.1 --- System Model --- p.38
Chapter 5.1.2 --- Lower Bound of Weighted Sum of Unsatisfactory Function --- p.40
Chapter 5.1.3 --- Performance Evaluation I --- p.41
Chapter 5.1.4 --- Discussion --- p.44
Chapter 5.1.5 --- Performance Evaluation II --- p.44
Chapter 5.2 --- Single Order of Selection Diversity Algorithm --- p.47
Chapter 5.2.1 --- System Model --- p.47
Chapter 5.2.2 --- Performance Evaluation I --- p.49
Chapter 5.2.3 --- Performance Evaluation II --- p.53
Chapter 6 --- Conclusion and Future Works --- p.58
Chapter 6.1 --- Conclusion --- p.58
Chapter 6.2 --- Future Works --- p.60
Chapter 6.2.1 --- Multi-cell Channel Assignment --- p.60
Chapter 6.2.2 --- Theoretical Studies --- p.62
Chapter 6.2.3 --- Adaptive Algorithms --- p.62
Chapter 6.2.4 --- Assignment of Non-orthogonal Channels --- p.63
Chapter A --- Proof of Proposition 3.1 --- p.64
Chapter B --- Proof of Proposition 4.1 --- p.66
Chapter C --- Assignment Problem --- p.68
Bibliography --- p.74
Zvereva, Margarita. "Mathematical Programming Formulations of the Planar Facility Location Problem." Thesis, 2007. http://hdl.handle.net/10012/3283.
Повний текст джерелаYu-TaTsai and 蔡侑達. "Formulating Intuitionistic Fuzzy Regression Models By Mathematical Programming Methods." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7n3jf7.
Повний текст джерела國立成功大學
工業與資訊管理學系
105
Regression analysis is one of the most widely used decision making tools. It allows decision makers to determine the relationship between input variables and output variables. In a statistical regression analysis, data is always precise figures, which are called crisp values. However, in the complex real-world environment, data may be uncertain, written in linguistic terms, or based on personal subjective attitudes. Therefore, fuzzy set theory was developed to deal with these data. In order to express the essence of uncertainty better, scholars proposed the concept of intuitionistic fuzzy sets (IFS) as a generalization of the fuzzy set theory. In addition to including positive information, it also includes negative information. There have been few studies of intuitionistic fuzzy regression (IFR) models. The direction of these studies was decided by three elements: data-type and parameter-type, solution approaches, and computation of the estimation error. In this study, the available data for both input variables and output variables are assumed to be intuitionistic fuzzy numbers (IFN), and the model parameters are crisp numbers. The parameters are crisp values rather than IFN because IFN multiplied with each other will bring about an over-increase in the spread of the IFN. In other words, the fuzziness of numbers will be over-increased. Different from the traditional solution methods such as the least-squares method, this study uses mathematical programming methods. The concept of decomposition rules and IFN-cuts are also used to build models. Two different approaches are proposed in this study. The predictive ability of the obtained models is evaluated by using similarity and distance measures. The results indicate that the models proposed in this study are better than their counterparts.
Sussman, Gerald Jay, and Jack Wisdom. "The Role of Programming in the Formulation of Ideas." 2002. http://hdl.handle.net/1721.1/6707.
Повний текст джерела(9811760), Scott Ladley. "An investigation into the application of evolutionary algorithms on highly constrained optimal control problems and the development of a graphical user interface for comprehensive algorithm control and monitoring." Thesis, 2003. https://figshare.com/articles/thesis/An_investigation_into_the_application_of_evolutionary_algorithms_on_highly_constrained_optimal_control_problems_and_the_development_of_a_graphical_user_interface_for_comprehensive_algorithm_control_and_monitoring/19930160.
Повний текст джерелаIn this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be applied to finding solutions to discrete optimal control problems. Also, a detailed investigation is carried out into the design and development of a superior execution environment for Evolutionary Algorithms.
An overview of the basic processes of an Evolutionary Algorithm is given, as well as detailed descriptions for several genetic operators. Several additional operators that may be applied in conjunction with an Evolutionary Algorithm are also studied. These operators include several versions of the simplex method, as well as 3 distinct hill -climbers, each designed for a specific purpose. The hill -climbing routines have been designed for purposes that include local search, escaping local minima, and a hill -climbing routine designed for self -adaptation to a broad range of problems.
The mathematical programming formulation of discrete optimal control problems is used to generate a class of highly constrained problems. Techniques are developed to accurately and rapidly solve these problems, whilst satisfying the equality constraints to machine accuracy.
The improved execution environment for Evolutionary Algorithms proposes the use of a Graphical User Interface for data visualisation, algorithm control and monitoring, as well as a Client/Server network interface for connecting the GUI to remotely run algorithms.
"Single Machine Scheduling: Comparison of MIP Formulations and Heuristics for Interfering Job Sets." Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.14756.
Повний текст джерелаDissertation/Thesis
Ph.D. Industrial Engineering 2012