Publication
Estratégias para alocação de recursos de controle ótimo em cenários estocásticos
| Summary: | 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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| Main Authors: | Galvão Filho, Arlindo Rodrigues |
| Subject: | Modelagem estocástica Controle ótimo Epidemiologia matemática SIR SIRC MBI Redes complexas Stochastic modeling Optimal control Mathematical epidemiology Complex networks CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| Year: | 2019 |
| Country: | Brazil |
| Document type: | doctoral thesis |
| Access type: | unknown |
| Associated institution: | IBICT |
| Language: | Portuguese |
| Origin: | Oasisbr |
| Summary: | 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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