A model for the decision-maker preferences in a polymer extrusion process

The NN-DM is a method developed to find a mathematical model that represents the Decision-Maker (DM) by employing an artificial neural network (NN) in situations in which the preferences can be represented by a utility function. This paper presents further developments to the NN-DM method to find a model in a polymer extrusion process. The form of the DM's interaction, the domain assignment, the ranking process, and the performance assessment are adapted to a real context of a multi-objective optimization problem followed by a design decision. The DM is then requested to fill a matrix expressing his preferences considering pairwise comparisons expressing ordinal relations only. Two multi-objective optimization problems are tested, each one with three estimates of different Pareto-optimal fronts. The adapted NN-DM method is able to provide a model which sorts the available solutions from the best to the worst according to the DM's preferences.info:eu-repo/semantics/publishedVersio

Autocoleta de swab nasofaríngeo e teste molecular em pool testing como estratégias para detecção de coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2): viabilidade em estudantes de medicina da Universidade Federal de Minas Gerais, 2021

Objective: To show the feasibility of using combined nasopharyngeal swab auto-collection and pool testing to detect SARS-CoV-2 in epidemiological surveys. Methods: The study involved a sample of 154 students from the Universidade Federal de Minas Gerais, who performed the self-collection of the nasopharyngeal swab in individual booths without supervision. Molecular testing was performed using the pool testing technique. Results: Obtaining samples lasted about 5 minutes each. Analysis 6 was performed to detect endogenous RNA in 40 samples, and the results indicated that no failures resulted from self-collection. None of the pools detected the presence of viral RNA. The cost of performing the molecular test (RT-PCR) by pool testing with samples obtained by self-collection was about 10 times lower than with the usual methods. Conclusion: The investigated strategies showed to be economically feasible and valid for the research of SARS-CoV-2 in epidemiological surveys.Objetivo: Demostrar la viabilidad de utilizar el uso combinado de la autocollección de swabs nasofaríngeos y pool testing para la detección del SARS-CoV2 en encuestas epidemiológicas. Métodos: El estudio involucró a una muestra de 154 estudiantes de la Universidade Federal de Minas Gerais, quienes realizaron la autocolección del hisopo nasofaríngeo en cabinas individuales sin supervision. La prueba molecular se realizó utilizando la técnica de prueba de grupo. Resultados: La obtención de muestras duró unos 5 minutos por persona. Se realizó un análisis para detectar RNA endógeno en 40 muestras y los resultados indicaron que no hubo fallas derivadas de la autocolección. Ninguno de los grupos detectó la presencia de RNA viral. El costo de realizar una prueba molecular (RT-PCR) por pool con muestras obtenidas por auto-recolección fue aproximadamente 10 veces menor que con los métodos habituales. Conclusión: Las estrategias investigadas demonstraram ser económicamente viables y válidas para la investigación del SARS-CoV-2 en encuestas epidemiológicas.Objetivo: Demonstrar a viabilidade da utilização combinada da autocoleta de swab nasofaríngeo e pool testing para detecção do SARS-CoV-2 em inquéritos epidemiológicos. Métodos: O estudo envolveu amostra de 154 estudantes da Universidade Federal de Minas Gerais, que realizaram a autocoleta do swab nasofaríngeo em cabines individuais e sem supervisão. O teste molecular foi realizado utilizando-se a técnica de pool testing. Resultados: A obtenção de amostras durou cerca de 5 minutos por pessoa. Realizou-se análise para detecção de RNA endógeno em 40 amostras e os resultados indicaram que não houve falhas decorrentes da autocoleta. Nenhum dos pools detectou presença de RNA viral. O custo da realização do teste molecular (RT-PCR) por pool testing com amostras obtidas por autocoleta foi cerca de dez vezes menor do que nos métodos habituais. Conclusão: As estratégias investigadas mostraram-se economicamente viáveis e válidas para a pesquisa de SARS-CoV-2 em inquéritos epidemiológicos

Controle singular de sistemas incertos

Orientador: Pedro Luis Dias PeresTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Não informadoAbstract: Not informed.DoutoradoDoutor em Engenharia Elétric

On the performance degradation of dominance-based evolutionary algorithms in many-objective optimization.

Abstract?In the last decade, it has become apparent that the performance of Pareto-dominance based evolutionary multiobjective optimization algorithms degrades as the number of objective functions of the problem, given by n, grows. This performance degradation has been the subject of several studies in the last years, but the exact mechanism behind this phenomenon has not been fully understood yet. This paper presents an analytical study of this phenomenon under problems with continuous variables, by a simple setup of quadratic objective functions with spherical contour curves and a symmetrical arrangement of the function minima location. Within such a setup, some analytical formulae are derived to describe the probability of the optimization progress as a function of the distance to the exact Pareto-set. A main conclusion is stated about the nature and structure of the performance degradation phenomenon in manyobjective problems: when a current solution reaches a that is an order of magnitude smaller than the length of the Pareto-set, the probability of finding a new point that dominates the current one is given by a power law function of with exponent (n?1). The dimension of the space of decision variables has no influence on that exponent. Those results give support to a discussion about some general directions that are currently under consideration within the research community

On the performance degradation of dominance-based evolutionary algorithms in many-objective optimization.

In the last decade, it has become apparent that the performance of Pareto-dominance based evolutionary multiobjective optimization algorithms degrades as the number of objective functions of the problem, given by n, grows. This performance degradation has been the subject of several studies in the last years, but the exact mechanism behind this phenomenon has not been fully understood yet. This paper presents an analytical study of this phenomenon under problems with continuous variables, by a simple setup of quadratic objective functions with spherical contour curves and a symmetrical arrangement of the function minima location. Within such a setup, some analytical formulae are derived to describe the probability of the optimization progress as a function of the distance ? to the exact Pareto-set. A main conclusion is stated about the nature and structure of the performance degradation phenomenon in manyobjective problems: when a current solution reaches a ? that is an order of magnitude smaller than the length of the Pareto-set, the probability of finding a new point that dominates the current one is given by a power law function of ? with exponent (n?1). The dimension of the space of decision variables has no influence on that exponent. Those results give support to a discussion about some general directions that are currently under consideration within the research community

Hybrid multicriteria algorithms applied to structural design of wireless local area networks.

This manuscript presents a novel approach based on hybrid optimization techniques for planning Wireless Local Area Networks in two stages: i) network structure design for access point (AP) placement and channel assignment and ii) channel assignment enhancement. We consider two objective functions: network load balance and signal-to-interference-plus-noise ratio; and three hard constraints: maximum AP capacities, client demand attendance, and minimum coverage levels. The proposed algorithm delivers an approximation of the efficient solution set, considering the two functions described above. The results from two scenarios were compared to the following four approaches: two multiobjective evolutionary algorithms, a well-known commercial tool, and a greedy technique. Finally, the solutions were subjected to sensitivity analysis to validate their robustness regarding user mobility and AP failures

Multi-objective dynamic programming for spatial cluster detection.

The detection and inference of arbitrarily shaped spatial clusters in aggregated geographical areas is described here as a multi-objective combinatorial optimization problem. A multi-objective dynamic programming algorithm, the Geo Dynamic Scan, is proposed for this formulation, finding a collection of Pareto-optimal solutions. It takes into account the geographical proximity between areas, thus allowing a disconnected subset of aggregated areas to be included in the efficient solutions set. It is shown that the collection of efficient solutions generated by this approach contains all the solutions maximizing the spatial scan statistic. The plurality of the efficient solutions set is potentially useful to analyze variations of the most likely cluster and to investigate covariates. Numerical simulations are conducted to evaluate the algorithm. A study case with Chagas’ disease clusters in Brazil is presented, with covariate analysis showing strong correlation of disease occurrence with environmental data

Control of flexible manufacturing systems under model uncertainty using supervisory control theory and evolutionary computation schedule synthesis.

A new approach for the problem of optimal task scheduling in flexible manufacturing systems is proposed in this work, as a combination of metaheuristic optimization techniques with the supervisory control theory of discrete-event systems. A specific encoding, the word-shuffling encoding, which avoids the generation of a large number of infeasible sequences, is employed. A metaheuristic method based on a Variable Neighborhood Search is then built using such an encoding. The optimization algorithm performs the search for the optimal schedules, while the supervisory control has the role of codifying all the problem constraints, allowing an efficient feasibility correction procedure, and avoiding schedules that are sensitive to uncertainties in the execution times associated with the plant operation. In this way, the proposed methodology achieves a system performance which is typical from model-predictive scheduling, combined with the robustness which is required from a structural control

Multi-objective decision in machine learning.

Thiswork presents a novel approach for decisionmaking for multi-objective binary classification problems. The purpose of the decision process is to select within a set of Pareto-optimal solutions, one model that minimizes the structural risk (generalization error). This new approach utilizes a kind of prior knowledge that, if available, allows the selection of a model that better represents the problem in question. Prior knowledge about the imprecisions of the collected data enables the identification of the region of equivalent solutions within the set of Pareto-optimal solutions. Results for binary classification problems with sets of synthetic and real data indicate equal or better performance in terms of decision efficiency compared to similar approaches