Mauricio Cabrera-Ríos

An approach to the design of a manufacturing cell under economic considerations

Abstract A method is proposed for the system design of a manufacturing cell aiming for its profit maximization over a certain period of time. The proposed method makes use of simulation, design of experiments, regression analysis, Taguchi methods, and a profit model to generate several feasible and potentially profitable designs. A decision-maker may then choose the best alternative based on the profit value, robustness, and other practical considerations. The application of the method is illustrated with a case study where a partial design of a manufacturing cell is accomplished in a manufacturing company.

Designing a Sustainable and Distributed Generation System for Semiconductor Wafer Fabs

Driven by wind and solar photovoltaics technology, the power industry is shifting towards a distributed generation (DG) paradigm. A key challenge in deploying a renewable DG system is the power volatility. This study proposes a visionary energy concept and further presents a mathematical model that could help the large industry consumers adopt this new energy technology. The study seeks to design a grid-connected DG system that is capable of providing the necessary electricity for wafer fabs. Simulation-based optimization algorithm was applied to determine the equipment type and capacity aiming to minimize the DG lifecycle cost. The proposed method was demonstrated on fab facilitates located in three different regions in the US.

MULTIPLE QUALITY CRITERIA OPTIMIZATION IN REACTIVE IN-MOLD COATING WITH A DATA ENVELOPMENT ANALYSIS APPROACH II: A CASE WITH MORE THAN THREE PERFORMANCE MEASURES

Sheet Molding Compound (SMC) is a widely utilized material to manufacture automotive exterior body panels. Compression molded SMC parts are often coated with the objective to provide additional surface properties, provide environmental protection, and/or enhance aesthetics. One of the most rapidly increasing coating methods is called In-Mold Coating (IMC) in which a liquid thermoset is injected onto the surface of the cured SMC part while this is still in the mold. Cycle time, dimensional consistency, and surface finish are among the most important performance measures (PMs) to consider in the production of SMC due to their impact in profit and quality, hence these measures are also important when using IMC. Frequently, the PMs exhibit conflicting behavior i.e. lowering the cycle time might imply decreasing the part surface quality and/or achieving a lower overall part dimensional consistency. For this reason, one must exercise especial care to identify the best compromises between the PMs along with the processing conditions that result in these best compromises. The task of finding the best compromises poses a multiple criteria optimization problem. This paper describes an application of IMC to SMC where the multiple criteria optimization problem is addressed with a non-parametric approach known as Data Envelopment Analysis (DEA). The use of a graphical approach to identify the best compromises is not possible in the case presented here because four PMs have been included. This fact makes the use of the proposed approach completely necessary to solve the problem at hand.

Multiple quality criteria optimization in reactive in-mold coating (IMC) with a data envelopment analysis approach

Reactive in-mold coating (IMC) products have been used successfully for many years to improve the surface quality of Sheet Molding Compound (SMC) compression molded parts. IMC provides a smooth, sealed surface used as conductive or non-conductive primer for subsequent finished painting operations. The success of IMC for SMC parts has recently attracted the interest of thermoplastic injection molders. The potential environmental and economic benefits of using IMC as a primer and, in the ideal case, to replace painting completely are large. Most optimization studies in Reactive Polymer Processing involve a compromise between different performance measures. In most cases the controllable variables have a conflicting effect on the relevant performance measures. IMC is not the exception. These performance measures need to be balanced, each against the other, in order to obtain the best compromise. The ideal compromise will depend on the final part quality requirements. In this work, the use of Data Envelopment Analysis (DEA) is explored to identify the best compromises among several performance measures. We have selected two case studies to illustrate the use of this technique. In the first case, we apply DEA to select the locations for two IMC injection nozzles for a thermoplastic part to optimize two quality measures. In the second case, we study the simultaneous optimization of cycle time, surface quality, and dimensional consistency for SMC parts. The first case is aimed to demonstrate the application of DEA with a simple example; in fact, the best compromises in such example could have been identified graphically. The second case, however, provides an example where the multidimensionality of the problem makes the use of DEA critical to elicit a proper solution.

Setting the Processing Parameters in Injection Molding Through Multiple-Criteria Optimization: A Case Study

In this correspondence, a case study involving statistical characterization and multiple criteria optimization on injection molding is presented. This case study is the first application of a method previously described in the literature involving data envelopment analysis geared toward setting design and process variables to meet several performance measures.

Identification of potential biomarkers from microarray experiments using multiple criteria optimization

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.

A multicriteria simulation optimization method for injection molding

Abstract Injection molding is the most important process for mass-producing plastic products. To help improve and facilitate the molding of plastic parts, advanced computer simulation tools have been developed. While modeling is complicated by itself, the difficulty of optimizing the injection molding process is that the performance measures involving the injection molding process usually show conflicting behaviors. Therefore, the best solution for one performance measure is usually not the best in some other performance measures. This paper introduces a simulation optimization method which considers multiple performance measures and is able to find a set of efficient solutions without having to evaluate a large number of simulations. The main components of the method are metamodeling, design of experiments, and data envelopment analysis. The method is illustrated and detailed here using a simple test example, and it is applied to a real injection molding case. The performance of the method using a different design of experiments is also discussed.

The search for experimental design with tens of variables: preliminary results

Simulation models have importantly expanded the analysis capabilities in engineering designs. With larger computing power, more variables can be modeled to estimate their effect in ever-larger number of performance measures. Statistical experimental designs, however, are still somewhat focused on the variation of less than about a dozen variables. In this work, an effort to identify strategies to deal with tens of variables is undertaken. The aim is to be able to generate designs capable to estimate full-quadratic models. Several strategies are contrasted: (i) generate designs with random numbers, (ii) use designs already in the literature, and (iii) generate designs under a clustering strategy. The first strategy is an easy way to generate a design. The second strategy does focus on statistical properties, but the designs become somewhat inconvenient to generate when increasing the number of variables. The third strategy is currently being investigated as a possibility to provide a balance between (i) and (ii).

Simulation optimization applied to injection molding

In this work, a simulation optimization method developed by Villarreal and Cabrera-Rios (2007) is applied to injection molding. The method uses design of experiments and adaptive metamodeling techniques. The application of the method to several global optimization test functions as well as non linear polynomial and non polynomial functions point towards a quick convergence to highly attractive solutions with a low number of simulations. Here the method is used to select the best processing conditions for injection molding a simple rectangular plaque and a real automotive part using different performance criteria.

Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge

Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization‐based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.