Design of Experiments: An Overview

Design Of Experiments (DOE) is needed for experiments with real-life systems, and with either deterministic or random simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random simulation. DOE may have two goals: sensitivity analysis including factor screening and optimization. This contribution starts with classic DOE including 2k-p and Central Composite designs. Next, it discusses factor screening through Sequential Bifurcation. Then it discusses Kriging including Latin Hyper cube Sampling and sequential designs. It ends with optimization through Generalized Response Surface Methodology and Kriging combined with Mathematical Programming, including Taguchian robust optimization.simulation;sensitivity analysis;optimization;factor screening;Kriging;RSM;Taguchi

QUALITY DESIGN OF TAGUCHI’S DIGITAL DYNAMIC SYSTEMS

Taguchi’s method is a widely employed methodology in many different industries for improving product quality and process performance. Digital dynamic systems denote the problems where both the input and output are digital values in Taguchi’s method. When the signal factor levels are classified into two classes and the output is classified into two or more classes, two or more errors will occur in experiments. The digital dynamic systems are widely applied in the fields of telecommunication, computer operations, chemistry and tests of detection of medicine or environmental pollution. The SN ratio recommended by Taguchi is based on the errors with the same loss coefficient to optimize the problems. However, the losses due to the errors are not equal in practice. This paper proposes a general model for optimizing parameter design and selecting threshold value for the digital dynamic systems where the output is classified into four classes. The implementation and the effectiveness of the proposed approach are illustrated through two case studies

Forest-Genetic method to optimize parameter design of multiresponse experiment

[EN] We propose a methodology for the improvement of the parameter design that consists of the combination of Random Forest (RF) with Genetic Algorithms (GA) in 3 phases: normalization, modelling and optimization. The first phase corresponds to the previous preparation of the data set by using normalization functions. In the second phase, we designed a modelling scheme adjusted to multiple quality characteristics and we have called it Multivariate Random Forest (MRF) for the determination of the objective function. Finally, in the third phase, we obtained the optimal combination of parameter levels with the integration of properties of our modelling scheme and desirability functions in the establishment of the corresponding GA. Two illustrative cases allow us to compare and validate the virtues of our methodology versus other proposals involving Artificial Neural Networks (ANN) and Simulated Annealing (SA).[ES] Proponemos una metodología para la mejora del diseño de parámetros que consiste en la combinación de Random Forest (RF) con Algoritmos Genéticos (GA) en 3 fases: normalización, modelización y optimización. La primera fase corresponde a la preparación previa del conjunto de datos mediante funciones de normalización. En la segunda fase, diseñamos un esquema de modelización ajustado a múltiples características de calidad, que hemos llamado Multivariante Random Forest (MRF) para la determinación de la función objetivo. Finalmente, en la tercera fase se obtiene la combinación ¿optima de los niveles de los parámetros mediante la integración de propiedades dadas por nuestro esquema de modelización y las desirabibity functions en el establecimiento del correspondiente GA. Dos casos ilustrativos nos permiten comparar y validar las virtudes de nuestra metodología versus otras propuestas que involucran Redes Neuronales Artificiales (ANN) y Simulated Annealing (SA).Villa Murillo, A.; Carrión García, A.; Sozzi, A. (2020). Forest-Genetic method to optimize parameter design of multiresponse experiment. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial. 23(66):9-25. https://doi.org/10.4114/intartif.vol23iss66pp9-25S925236

Orthogonal Array Experiment in Systems

This paper espouses the application of orthogonal array experiment to solve a class of engineering optimization problems encountered in systems engineering and architecting. It also illustrates the applicability of orthogonal array experiment in systems engineering and architecting with two examples: verification and validation of the performance of a bandwidth allocation algorithm and architecting of a system of systems to respond to small boat attacks by terrorists. The orthogonal array experiment approach does not call for linearization of nonlinear engineering optimization problems; using orthogonal arrays, it solves them directly by carrying out the smallest possible number of experiments and determining their solutions from the results of the experiments. The orthogonal array experiment method has been found to be effective and efficient for these problems. The feasibility of applying the orthogonal array experiment approach to these problems suggests its potential application to other optimization problems encountered in systems engineering and architecting

A systemic approach to the database marketing process

The role of database marketing (DBM) has become increasingly important for organisations that have large databases of information on customers with whom they deal directly. At the same time, DBM models used in practice have increased in sophistication. This paper examines a systemic view of DBM and the role of analytical techniques within DBM. It extends existing process models to develop a systemic model that encompasses the increased complexity of DBM in practice. The systemic model provides a framework to integrate data mining, experimental design and prioritisation decisions. This paper goes on to identify opportunities for research in DBM, including DBM process models used in practice, the use of evolutionary operations techniques in DBM, prioritisation decisions, and the factors that surround the uptake of DBM.<br /