Tradeoff studies are a common part of engineering practice. Designers conduct tradeoff studies in order to improve their understanding of how various design considerations relate to one another. Generally a tradeoff study involves a systematic multi-criteria evaluation of various alternatives for a particular system or subsystem. After evaluating these alternatives, designers eliminate those that perform poorly under the given criteria and explore more carefully those that remain. The capability to compose preexisting tradeoff studies is advantageous to the designers of engineered systems, such as aircraft, military equipment, and automobiles. Such systems are comprised of many subsystems for which prior tradeoff studies may exist. System designers conceivably could explore system-level tradeoffs more quickly by leveraging this knowledge. For example, automotive systems engineers could combine tradeoff studies from the engine and transmission subsystems quickly to produce a comprehensive tradeoff study for the power train. This level of knowledge reuse is in keeping with good systems engineering practice. However, existing procedures for generating tradeoff studies under uncertainty involve assumptions that preclude engineers from composing them in a mathematically rigorous way. In uncertain problems, designers can eliminate inferior alternatives using stochastic dominance, which compares the probability distributions defined in the design criteria space. Although this is well-founded mathematically, the procedure can be computationally expensive because it typically entails a sampling-based uncertainty propagation method for each alternative being considered. This thesis describes two novel extensions that permit engineers to compose preexisting subsystem-level tradeoff studies under uncertainty into mathematically valid system-level tradeoff studies and efficiently eliminate inferior alternatives through intelligent sampling. The approaches are based on three key ideas: the use of stochastic dominance methods to enable the tradeoff evaluation when the design criteria are uncertain, the use of parameterized efficient sets to enable reuse and composition of subsystem-level tradeoff studies, and the use of statistical tests in dominance testing to reduce the number of behavioral model evaluations. The approaches are demonstrated in the context of a tradeoff study for a motor vehicle.

京都大学 / 新制・課程博士 / 博士(経済学) / 甲第24381号 / 経博第668号 / 新制||経||303(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 関口 格, 教授 原 千秋, 教授 NEWTON Jonathan Charles Scott / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

When systems designers are making decisions about which components or technologies to select for a design, they often use experience or intuition to select one technology over another. Additionally, developers of new technologies rarely provide more information about their inventions than discrete data points attained in testing, usually in a laboratory. This makes it difficult for system designers to select newer technologies in favor of proven ones. They lack the knowledge about these new technologies to consider them equally with existing technologies. Prior research suggests that set-based design representations can be useful for facilitating collaboration among engineers in a design project, both within and across organizational boundaries. However, existing set-based methods are limited in terms of how the sets are constructed and in terms of the representational capability of the sets. The goal of this research is to introduce and demonstrate new, more general set-based design methods that are effective for characterizing and comparing competing technologies in a utility-based decision framework. To demonstrate the new methods and compare their relative strengths and weaknesses, different technologies for a power plant condenser are compared. The capabilities of different condenser technologies are characterized in terms of sets defined over the space of common condenser attributes (cross sectional area, heat exchange effectiveness, pressure drop, etc.). It is shown that systems designers can use the resulting sets to explore the space of possible condenser designs quickly and effectively. It is expected that this technique will be a useful tool for system designers to evaluate new technologies and compare them to existing ones, while also encouraging the use of new technologies by providing a more accurate representation of their capabilities. I compare four representational methods by measuring the solution accuracy (compared to a more comprehensive optimization procedure's solution), computation time, and scalability (how a model changes with different data sizes). My results demonstrate that a support vector domain description-based method provides the best combination of these traits for this example. When combined with recent research on reducing its computation time, this method becomes even more favorable.

The broad aim of this research is to contribute knowledge that enables improvements in how designers model decision alternatives at the systems level—i.e., how they model different system configurations and concepts. There are three principal complications: (1) design concepts and systems configurations are partially-defined solutions to a problem that correspond to a large set of possible design implementations, (2) each concept or configuration may operate on different physical principles, and (3) decisions typically involve tradeoffs between multiple competing objectives that can include "non-engineering" considerations such as production costs and profits. This research is an investigation of a data-driven approach to modeling partially-defined system alternatives that addresses these issues. The approach is based on compositional strategy in which designers model a system alternative using abstract models of its components. The component models are representations of the rational tradeoffs available to designers when implementing the components. Using these models, designers can predict key properties of the final implementation of each system alternative. A new construct, called a parameterized efficient set, is introduced as the decision-theoretic basis for generating the component-level tradeoff models. Appropriate efficiency criteria are defined for the cases of deterministic and uncertain data. It is shown that the model composition procedure is mathematically sound under reasonable assumptions for the case of deterministic data. This research also introduces an approach for describing the valid domain of a data-driven model based on the use of support-vector machines. Engineering examples include performing requirements allocation for a hydraulic log splitter and architecture selection for a hybrid vehicle.

Submitted by Ronildo Prado (ronisp@ufscar.br) on 2017-08-23T12:44:58Z No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-23T12:45:05Z (GMT) No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-23T12:45:10Z (GMT) No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5) / Made available in DSpace on 2017-08-23T12:45:16Z (GMT). No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5) Previous issue date: 2017-02-14 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / The production scheduling activity is considered as one of the most complex activities in production management. This activity is part of the class of NP-Hard problems found in the area of computer science, that is, those problems that can not be solved deterministically in polynomial time. In addition, the complexity of this activity may increase according to the constraints imposed on each programming system/problem. In this research, the problem of programming of production the Flexible Job Shop (JSF) is studied. This problem is considered an extension of the Job Shop programming problem. In JSF, a group of jobs (i.e., products, items, part of an item) formed by a set of operations and each operation must be programmed by a resource (i.e., machine) that belongs to a group of resources that have the same functional characteristics (e.g., cut, sanding, painting). This problem is characterized in two sub-problems being routing and sequencing activity. Routing involves determining which resource will process a given operation. Sequencing is the order in which each operation will be processed on a resource. Through established programming, the objective of this research is to optimize performance multicriteria: the makespan (i.e., time spent to produce a set of jobs), processing time spent on the resource that worked by more time and total production time. In order to reach the objectives mentioned above, a hybrid swarm approach is proposed in this research. In this approach, two auxiliary methods are used to treat the abovementioned sub-problems: genetic operator of mutation to perform the routing activity and for the sequencing activity, an adaptive method of neighborhood structures is proposed. In order to deal with the multiobjectivity of the problem, we propose the Pareto dominance method. Experimental results obtained through commonly used benchmarks prove the efficacy and superiority of the proposed approach when compared to other approaches also applied to the problem studied. / A atividade de programação da produção é considerada como uma das atividades mais complexas no gerenciamento da produção. Essa atividade faz parte da classe de problemas NP-Difícil encontrados na área da ciência da computação, ou seja, aqueles problemas que não podem ser solucionados deterministicamente em tempo polinomial. Além disso, a complexidade dessa atividade pode aumentar de acordo com as restrições impostas a cada sistema/problema de programação. Nesta pesquisa, estuda-se o problema de programação da produção Job Shop Flexível (JSF). Esse problema é considerado como uma extensão do problema de programação Job Shop. No JSF, deve-se programar um grupo de jobs (i.e., produtos, itens, parte de um item) formados por um conjunto de operações e cada operação é processada por um recurso (i.e., máquina) que pertence a um grupo de recursos que possuam mesmas caraterísticas funcionais (e.g., cortar, lixar, pintar). Esse problema é caracterizado em dois sub-problemas, sendo eles, a atividade de roteamento e de sequenciamento. O roteamento implica em definir qual recurso irá processar uma determinada operação. O sequenciamento é a ordem em que cada operação será processada em um recurso. Por meio da programação estabelecida objetiva-se nessa pesquisa, otimizar multicritérios de desempenho, sendo eles: makespan (i.e., tempo gasto para produzir um conjunto de jobs), tempo de processamento gasto no recurso que trabalhou por mais tempo e tempo total de produção. Para alcançar os objetivos supracitados é proposto nessa pesquisa uma abordagem híbrida de enxame de abelhas. Nessa abordagem, utiliza-se dois métodos auxiliares para tratar os sub-problemas supracitados, sendo eles: operador genético de mutação para realizar a atividade de roteamento e para a atividade de sequenciamento é proposto um método adaptativo de estruturas de vizinhança. Para tratar a multiobjetividade do problema, propõe-se o método dominância de Pareto. Resultados experimentais obtidos por meio de benchmarks comumente usados comprovam a eficácia e a superioridade da abordagem proposta quando comparada com outras abordagens também aplicadas ao problema estudado.

Finding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into GA enhances the efficiency of the parallel GA by adaptively modifying the migration rates between different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed which automatically generates radial topologies and prevents the construction of infeasible radial networks during the optimization process. The main shortcoming of the proposed algorithm in Chapter 2 is that it identifies only one single solution. It means that the system operator will not have any option but relying on the found solution. That is why a novel hybrid optimization algorithm is proposed in the third chapter of this dissertation that determines Pareto frontiers, as candidate solutions, for multi-objective distribution network reconfiguration problem. Implementing this model, the system operator will have more flexibility in choosing the best configuration among the alternative solutions. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance (FPD) with shuffled frog leaping algorithm (SFLA) to recognize non-dominated suboptimal solutions identified by SFLA. The local search step of SFLA is also customized for power systems applications so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure which significantly increases the convergence speed of the algorithm. In the fourth chapter, the problem of optimal network reconfiguration is solved for the case in which the system operator is going to employ an optimization algorithm that is automatically modifying its parameters during the optimization process. Defining three fuzzy functions, the probability of crossover and mutation will be adaptively tuned as the algorithm proceeds and the premature convergence will be avoided while the convergence speed of identifying the optimal configuration will not decrease. This modified genetic algorithm is considered a step towards making the parallel GA, presented in the second chapter of this dissertation, more robust in avoiding from getting stuck in local optimums. In the fifth chapter, the concentration will be on finding a potential smart grid solution to more high-quality suboptimal configurations of distribution networks. This chapter is considered an improvement for the third chapter of this dissertation for two reasons: (1) A fuzzy logic is used in the partitioning step of SFLA to improve the proposed optimization algorithm and to yield more accurate classification of frogs. (2) The problem of system reconfiguration is solved considering the presence of distributed generation (DG) units in the network. In order to study the new paradigm of integrating smart grids into power systems, it will be analyzed how the quality of suboptimal solutions can be affected when DG units are continuously added to the distribution network. The heuristic optimization algorithm which is proposed in Chapter 3 and is improved in Chapter 5 is implemented on a smaller case study in Chapter 6 to demonstrate that the identified solution through the optimization process is the same with the optimal solution found by an exhaustive search.

Made available in DSpace on 2014-12-17T15:47:46Z (GMT). No. of bitstreams: 1 FrederikoSLNA.pdf: 5281687 bytes, checksum: 9fe12b6bcc355f7c67cf2f2c3ad9812b (MD5) Previous issue date: 2006-02-16 / This work performs an algorithmic study of optimization of a conformal radiotherapy plan treatment. Initially we show: an overview about cancer, radiotherapy and the physics of interaction of ionizing radiation with matery. A proposal for optimization of a plan of treatment in radiotherapy is developed in a systematic way. We show the paradigm of multicriteria problem, the concept of Pareto optimum and Pareto dominance. A generic optimization model for radioterapic treatment is proposed. We construct the input of the model, estimate the dose given by the radiation using the dose matrix, and show the objective function for the model. The complexity of optimization models in radiotherapy treatment is typically NP which justifyis the use of heuristic methods. We propose three distinct methods: MOGA, MOSA e MOTS. The project of these three metaheuristic procedures is shown. For each procedures follows: a brief motivation, the algorithm itself and the method for tuning its parameters. The three method are applied to a concrete case and we confront their performances. Finally it is analyzed for each method: the quality of the Pareto sets, some solutions and the respective Pareto curves / O presente trabalho realiza um Estudo Algor?tmico para Otimiza??o do Plano de Tratamento da Radioterapia Conformal. Inicialmente s?o apresentadas: uma vis?o geral sobre o c?ncer, o tratamento com radioterapia e no??es sobre a intera??o do feixe de radia??es ionizantes com a mat?ria. Uma proposta para Otimiza??o do Plano de Tratamento Radioter?pico ? desenvolvida de modo sistem?tico. ? apresentado o paradigma de problemas multicrit?rio, os conceitos de Pareto otimalidade e Pareto Domin?ncia. Um modelo Gen?rico de Otimiza??o para o Plano de Tratamento Radioter?pico ? proposto. S?o constru?das suas entradas, ? calculada a dose depositada no corpo do paciente atrav?s do conceito de matriz de dose, e ? apresentada a fun??o objetivo deste modelo. A complexidade dos problemas de otimiza??o do tratamento radioter?pico s?o classificados como de complexidade NP, este resultado justifica o desenvolvimento de m?todos heur?sticos para a sua resolu??o. S?o propostas tr?s metaheur?sticas para a Otimiza??o do Plano de Tratamento Radioter?pico: MOGA, MOSA e MOTS de acordo como o modelo gen?rico de otimiza??o proposto. Os projetos desses procedimentos metaheur?sticos s?o devidamente apresentados. Para cada m?todo se faz uma introdu??o liter?ria, dos seus algoritmos e a da metodologia usada para a afina??o dos par?metros. Os m?todos s?o aplicados a um caso concreto e confrontados atrav?s de medidas de performance. Finalmente ? analisado a qualidade dos conjuntos de Pareto produzidos por cada m?todo, s?o exibidas algumas solu??es geradas e as respectivas curvas de Pareto associadas