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Optimization via Monte Carlo simulation combined with the response surface method: a proposal for insertion of uncertainty in optimization of experimental problems


With the increase of competitiveness in general, it has been sought the optimization of processes that depend on many variables to achieve an objective or several objectives. In this research project will be studied experimental problems of the Response Surface Methodology and Mixture Problems, with the inclusion of the uncertainties in productive processes of Biodiesel through the cultivation of microalgae. This approach will also be applied in studies already carried out in the Design of Experiments literature, aiming at comparing of the results of this new approach with the traditional optimization methods, which are the desirability function using the Generalized Reduced Gradient Algorithm. Generally, in the optimization of experimental problems does not take into account the uncertainties inherent to the experiment, as well as uncertainties related to the development of the empirical functions. These uncertainties can affect the quality of the solution obtained by the optimization, that is, the adjustments of the investigated factors, consequently affecting the investigated process, leading to the loss of productivity. This work can be classified as an applied research, having descriptive empirical objectives, since the modeling and optimization aims to understand causal relations that can occur in the reality, favoring the understanding of real processes. The approach is quantitative, with the research method being modeling and simulation. The expected results are the publication of this research in Qualis A1 ENG III journals and in congresses, and the technological contribution to the development of new strategies to optimize experimental problems in the context of uncertainty that are present in the industrial sector. (AU)

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Scientific publications (8)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DA SILVA, ANEIRSON FRANCISCO; SILVA MARINS, FERNANDO AUGUSTO; DIAS, ERICA XIMENES; MIRANDA, RAFAEL DE CARVALHO. Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components. EXPERT SYSTEMS, v. 39, n. 2 SEP 2021. Web of Science Citations: 0.
DE OLIVEIRA, STEFANO P.; LUCHE, JOSE ROBERTO D.; MARINS, FERNANDO A. S.; DA SILVA, ANEIRSON F.; COSTA, ANTONIO F. B. Design of a Bike-Bus Network for a City of Half a Million Citizens. JOURNAL OF URBAN PLANNING AND DEVELOPMENT, v. 147, n. 3 SEP 2021. Web of Science Citations: 0.
DEFALQUE, CRISTIANE MARIA; MARINS, FERNANDO AUGUSTO SILVA; DA SILVA, ANEIRSON FRANCISCO; RODRIGUEZ, ELEN YANINA AGUIRRE. A review of waste paper recycling networks focusing on quantitative methods and sustainability. JOURNAL OF MATERIAL CYCLES AND WASTE MANAGEMENT, v. 23, n. 1, p. 55-76, JAN 2021. Web of Science Citations: 1.
SILVA MARINS, FERNANDO AUGUSTO; DA SILVA, ANEIRSON FRANCISCO; MIRANDA, RAFAEL DE CARVALHO; BARRA MONTEVECHI, JOSE ARNALDO. A new approach using fuzzy DEA models to reduce search space and eliminate replications in simulation optimization problems. EXPERT SYSTEMS WITH APPLICATIONS, v. 144, APR 15 2020. Web of Science Citations: 0.
DA SILVA, ANEIRSON FRANCISCO; SILVA MARINS, FERNANDO AUGUSTO; DIAS, ERICA XIMENES; USHIZIMA, CARLOS ALBERTO. Improving manufacturing cycle efficiency through new multiple criteria data envelopment analysis models: an application in green and lean manufacturing processes. PRODUCTION PLANNING & CONTROL, v. 32, n. 2 JAN 2020. Web of Science Citations: 2.
BRUNO VINÍCIUS RIBEIRO FURLANETTO; FERNANDO AUGUSTO SILVA MARINS; ANEIRSON FRANCISCO DA SILVA; CRISTIANE MARIA DEFALQUE. Optimization of a logistics network considering allocation of facilities and taxation aspects. Gestão & Produção, v. 27, n. 4, p. -, 2020.
DA SILVA, ANEIRSON FRANCISCO; MARINS, FERNANDO AUGUSTO S.; DIAS, ERICA XIMENES. Improving the discrimination power with a new multi-criteria data envelopment model. ANNALS OF OPERATIONS RESEARCH, OCT 2019. Web of Science Citations: 1.
DA SILVA, ANEIRSON FRANCISCO; SILVA MARINS, FERNANDO AUGUSTO; DIAS, ERICA XIMENES; DA SILVA OLIVEIRA, JOSE BENEDITO. Modeling the uncertainty in response surface methodology through optimization and Monte Carlo simulation: An application in stamping process. MATERIALS & DESIGN, v. 173, JUL 5 2019. Web of Science Citations: 0.

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