Otimização baseada em metamodelos: uma abordagem para metamodelagem em simulação a eventos discretos

In the context of industry 4.0, optimization via simulation (OvS) emerges as one of the most powerful tools in the modern industry, allowing decision-makers to allocate their resources more assertively. However, in very complex systems, the use of conventional OvS techniques requires computational time, which frequently, makes its application unfeasible. In recent years, the development in the machine learning area has emerged algorithms with high learning capacity, making the use of optimization via simulation by metamodeling (OvSM) techniques to solve complex problems a promising field of study. In this sense, the present study proposes a framework for OvSM based on the insights and analyses derived from the systematic literature review carried out. The proposed framework incorporates the use of discrete event simulation techniques, design of experiments, machine learning algorithms, and hyper-parameter optimization via genetic algorithm for OvS problems. To validate the proposed method, this dissertation tested and compared six machine learning algorithms (Support Vector Machine, Artificial Neural Networks, Gradient-Boosted Trees, Randon Forest, Polynomial Regression, and Gaussian Process) with and without the hyper optimization step -parameters in two experimental arrangements (Latin Hypercube Design and Random) applied to the problem of resource allocation in three real cases in the industry. With the application of the method in the study objects presented, the best performing metamodels obtained solutions that reached, respectively, 100%, 96.17%, and 100% of the optimal benchmark location, demanding, on average, 35.22% less time computational. Also, the incorporation of the hyper-parameter optimization step in the proposed metamodeling method allowed a 31.28% reduction in the root mean square error of the metamodels compared to the traditional method, which does not include this step.Agência 1No contexto da indústria 4.0, a otimização via simulação (OvS) surge como uma das mais potentes ferramentas da indústria moderna, permitindo aos decisores alocarem seus recursos de forma mais assertiva. Todavia, em sistemas muito complexos, o uso de técnicas convencionais de OvS demandam um tempo computacional que, muitas vezes, inviabiliza sua aplicação. Nos últimos anos, o desenvolvimento na área de machine learning surgiram algoritmos com alta capacidade de aprendizado, tornando o uso das técnicas de otimização via simulação por metamodelagem (OvSM) para solucionar problemas complexos um campo de estudo promissor. Neste sentido, o presente estudo propõe um framework para OvSM embasado nos insights e análises provindos da revisão sistemática de literatura realizada. O framework proposto incorpora o uso de técnicas de simulação a eventos discretos, design of experiments, algoritmos de machine learning, e otimização de hiper-parâmetros via algoritmo genético para problemas de OvS. A fim de validar o framework proposto, esta dissertação testou e comparou seis algoritmos de machine learning (Support Vector Machine, Redes Neurais Artificiais, Gradient-Boosted Trees, Randon Forest, Regressão Polinomial e Gaussian Process) com e sem a etapa de otimização de hiper-parâmetros em dois arranjos experimentais (Latin Hipercube Design e Aleatório) aplicados ao problema de alocação de recursos em três casos reais da indústria. Com a aplicação do método nos objetos de estudo apresentados, os metamodelos de melhor performance obtiveram soluções que atingiram, respectivamente, 100%, 96,17%, e 100% do ótimo local benchmark, demandando, em média, 35,22% menos tempo computacional. Além disto, a incorporação da etapa de otimização de hiper-parâmetros no método de metamodelagem proposto permitiu uma redução de 31,28% no root mean square error dos metamodelos se comparado ao método tradicional, que não contempla esta etapa

Uncertainty modeling : fundamental concepts and models

This book series represents a commendable effort in compiling the latest developments on three important Engineering subjects: discrete modeling, inverse methods, and uncertainty structural integrity. Although academic publications on these subjects are plenty, this book series may be the first time that these modern topics are compiled together, grouped in volumes, and made available for the community. The application of numerical or analytical techniques to model complex Engineering problems, fed by experimental data, usually translated in the form of stochastic information collected from the problem in hand, is much closer to real-world situations than the conventional solution of PDEs. Moreover, inverse problems are becoming almost as common as direct problems, given the need in the industry to maintain current processes working efficiently, as well as to create new solutions based on the immense amount of information available digitally these days. On top of all this, deterministic analysis is slowly giving space to statistically driven structural analysis, delivering upper and lower bound solutions which help immensely the analyst in the decisionmaking process. All these trends have been topics of investigation for decades, and in recent years the application of these methods in the industry proves that they have achieved the necessary maturity to be definitely incorporated into the roster of modern Engineering tools. The present book series fulfills its role by collecting and organizing these topics, found otherwise scattered in the literature and not always accessible to industry. Moreover, many of the chapters compiled in these books present ongoing research topics conducted by capable fellows from academia and research institutes. They contain novel contributions to several investigation fields and constitute therefore a useful source of bibliographical reference and results repository. The Latin American Journal of Solids and Structures (LAJSS) is honored in supporting the publication of this book series, for it contributes academically and carries technologically significant content in the field of structural mechanics