Resumo:
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.
