Estratégias para alocação de recursos de controle ótimo em cenários estocásticos
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2019-06-10
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Universidade Federal de Goiás
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Application of computational models has contributed to understanding of different dynamics
as well as possible more effective control strategies. Three widely used examples
are deterministic formulations of compartmental models, individuals based models, and
complex networks based models. An alternative to such models is a stochastic approach,
which allows uncertainties insertion to models, providing more realistic results. In this
context, this work proposes use of deterministic compartmental models to obtain optimum
control policies, and later evaluation of such policy applied in a stochastic scenario
using a equivalent individual based model. It also proposes three new control strategies
based on dynamics and topology in complex network models. To models validation, a
case study based on epidemiological dynamics was done, in which proposed strategies
resulted in significant reductions in number of infected individuals, optimizing resource
spending. Insertion of uncertainty in models was positive for average behavior analysis
of dynamics. In addition, a parallel MBI model was proposed to be processed in graphic
cards. With this improvement it was possible to obtain a reduction by a factor of twenty
in processing time.
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GALVÃO FILHO, Arlindo Rodrigues. Estratégias para alocação de recursos de controle ótimo em cenários estocásticos. 2019. 126 f. Tese (Doutorado em Ciência da Computação em Rede) - Universidade Federal de Goiás, Goiânia, 2019.