The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Redes adversárias generativas: uma alternativa para modelagem de dados de entrada em projetos de simulação

In general, stochastic simulation consists of input data and logic, the former being the basic source of uncertainty in a simulation model. For this reason, data modeling is an essential step in the development of stochastic simulation projects. Many advances have been observed in recent years in simulation software and in data collection tools. However, the methods for input data modeling have remained largely unchanged for over 30 years. In their daily lives, modelers face difficulties related to the choice of input data models, mainly due to the challenge of modeling non Independent and Identically Distributed Data (IID) data, which requires specific tools not offered by simulation software and their data modeling packages. For this reason, few studies consider elements of complexity such as heterogeneities, dependencies, and autocorrelations, underestimating the uncertainty of the stochastic system. Given the new developments in Artificial Intelligence, it is possible to seek synergies to solve this problem. The present study aims to evaluate the results of the application of Generative Adversarial Networks (GANs) for input data modeling. Such networks constitute one of the most recent architectures of artificial neural networks, being able to learn complex distributions and, therefore, generate synthetic samples with the same behavior as real data. Therefore, this thesis proposes a method for Input Data Modeling based on GANs (MDE-GANs) and implements it through the Python language. Considering a series of theoretical and real study objects, the results are evaluated in terms of representation quality of the input models and comparisons are made with traditional modeling methods. As a main conclusion, it was possible to identify that the application of MDE-GANs allows obtaining input data models with strong accuracy, surpassing the results of traditional methods in cases of non-IID data. Thus, the present thesis contributes by offering a new alternative for input data modeling, capable of overcoming some of the challenges faced by modelers.De forma geral, a simulação estocástica consiste em dados de entrada e lógicas, sendo os primeiros as fontes básicas de incerteza em um modelo de simulação. Por essa razão, a modelagem de dados é uma etapa essencial no desenvolvimento de projetos na área. Muitos avanços foram observados nos últimos anos nos programas de simulação e em ferramentas para coleta. Porém, os métodos para modelagem de dados permanecem praticamente inalterados há mais de 30 anos. Em seu dia a dia, praticantes de simulação enfrentam dificuldades relacionadas à escolha de Modelos de Dados de Entrada (MDEs), principalmente devido ao desafio da modelagem de dados não Independentes e Identicamente Distribuídos (IID), o que requer ferramentas específicas e não oferecidas por programas de simulação e seus pacotes de estatísticos. Por essa razão, poucos estudos consideram elementos de complexidade como heterogeneidades, dependências e autocorrelações, subestimando a incerteza do sistema estocástico. Diante dos novos desenvolvimentos na área de Inteligência Artificial, é possível buscar sinergias para resolução desse problema. O presente estudo tem como objetivo avaliar os resultados da aplicação de Redes Adversárias Generativas, ou Generative Adversarial Networks (GANs) para obtenção de MDEs. Tais redes constituem uma das mais recentes arquiteturas de redes neurais artificiais, sendo capazes de aprender distribuições complexas e, com isso, gerar amostras sintéticas com o mesmo comportamento dos dados reais. Para tanto, esta tese propõe um método para Modelagem de Dados de Entrada baseado em GANs (MDEGANs) e o implementa por meio da linguagem Python. Considerando uma série de objetos de estudo teóricos e reais, são avaliados os resultados em termos de qualidade de representação dos MDEs e realizadas comparações com métodos tradicionais. Como principal conclusão, foi possível identificar que a aplicação de MDE-GANs permite obter MDEs com forte acurácia, superando os resultados dos métodos tradicionais nos casos de dados não IID. Com isso, a presente tese contribui ao oferecer uma nova alternativa para a área, capaz de contornar alguns dos desafios enfrentados por modeladores

Quantifying and reducing Input modelling error in simulation

This thesis presents new methodology in the field of quantifying and reducing input modelling error in computer simulation. Input modelling error is the uncertainty in the output of a simulation that propagates from the errors in the input models used to drive it. When the input models are estimated from observations of the real-world system input modelling error will always arise as only a finite number of observations can ever be collected. Input modelling error can be broken down into two components: variance, known in the literature as input uncertainty; and bias. In this thesis new methodology is contributed for the quantification of both of these sources of error. To date research into input modelling error has been focused on quantifying the input uncertainty (IU) variance. In this thesis current IU quantification techniques for simulation models with time homogeneous inputs are extended to simulation models with nonstationary input processes. Unlike the IU variance, the bias caused by input modelling has, until now, been virtually ignored. This thesis provides the first method for quantifying bias caused by input modelling. Also presented is a bias detection test for identifying, with controlled power, a bias due to input modelling of a size that would be concerning to a practitioner. The final contribution of this thesis is a spline-based arrival process model. By utilising a highly flexible spline representation, the error in the input model is reduced; it is believed that this will also reduce the input modelling error that passes to the simulation output. The methods described in this thesis are not available in the current literature and can be used in a wide range of simulation contexts for quantifying input modelling error and modelling input processes

History of input modeling

In stochastic simulation, input modeling refers to the process of identifying and selecting the probability distributions, called input models, from which are generated the random variates that are the source of the stochastic variation in the simulation model when it is run. This article reviews the history of the development and use of such models with the main focus on discrete-event simulation (DES).</p