Abstract Paper aims Frequently, researchers try to find a better way to allocate assets in order to have maximum return and low variability in a portfolio as diverse as possible. This paper aims to apply D-Optimal Design in the context of Mixture Design and portfolio optimization to efficiently select the runs of the proposed experimental design. Originality A new approach to find the optimal weights that maximize the returns and minimize the risk using D-Optimal Design was used. A multi-response optimization problem considering returns, variability and entropy as functions of the weights was proposed. However, as there is a significant correlation between the objective functions, a Factor Analysis combined with FMSE to dimensionality reduction was used. Research method All the steps for both stages of the methodology applied in this paper are presented below: select real time series; predict one step ahead; generate a D-Optimal mixture design; apply weights and generate mathematical models; solve the optimization problem. Main findings Using the desirability method, the optimal values were determined, obtaining approximately 79% for the compound desirability function. The proposed method presented a 16.80% higher return with a 4.98% higher risk exposure if compared against Naïve method. Implications for theory and practice The proposed methodology can be applied to any portfolio optimization study. Mixture Design studies have already been proposed for modeling portfolio optimization problems. However, the D-Optimal Design proved to be adequate, which represents less computational effort.

Abstract Paper aims This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series. Originality This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient. Research method Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

Resumo Neste estudo, buscou-se analisar a variabilidade de instrumentos utilizados na indústria para avaliar as características geométricas de um ponto de solda. Para tal, inicialmente, utilizou-se da técnica de planejamento de experimentos para gerar um arranjo fatorial fracionado para as configurações dos parâmetros de soldagem em corpos de prova de aços galvanizados por imersão a quente, a fim de representar a amplitude real do processo de soldagem por pontos. Para as análises, utilizou-se o estudo de repetitividade e reprodutibilidade (GR&R), pelo método de análise de variância, para identificar os componentes de variação do sistema de medição avaliando três instrumentos distintos, comparando as medições realizadas por um analisador de imagens, a partir do ensaio metalográfico, e métricas de instrumentos convencionais de metrologia como o relógio apalpador e o paquímetro manual. Foram avaliadas duas características da qualidade, sendo elas: a profundidade de indentação e o diâmetro do ponto. A partir dos resultados foi possível verificar que o analisador de imagem apresentou uma menor variabilidade nas medições, caracterizando-se como a melhor escolha para as medições das respostas de qualidade do processo de solda por pontos apresentando um GR&R classificado como aceitável.

Abstract In this study, we sought to analyze the variability of instruments used in industry to evaluate the geometric characteristics of a welding point. For this purpose, the design of experiments technique was used to generate a fractional factorial design for the welding parameter configurations in test specimens of hot dip galvanized steels in order to represent the real amplitude of the resistance spot welding process. For the analyzes, the repeatability and reproducibility (GR&R) study was used by the analysis of variance method to identify the variation components of the measurement system by evaluating three different instruments, comparing the measurements performed by an image analyzer, from the metallography and measurements of conventional metrology instruments such as the dial-gauge and the caliper. Two quality characteristics were evaluated: indentation depth and nugget width. From the results it was possible to verify that the image analyzer presented a lower variability in the measurements, being the best choice for the measurements of the quality responses of the spot welding process, presenting a GR&R classified as acceptable.