Chao-Ton Su

AN INVENTORY MODEL WITH DETERIORATING ITEMS UNDER INFLATION WHEN A DELAY IN PAYMENT IS PERMISSIBLE

Abstract This study develops an inventory model for initial-stock-dependent consumption rate when a delay in payment is permissible. In the inventory model, shortages are not allowed. The effect of the inflation rate, deterioration rate, initial-stock-dependent consumption rate and delay in payment are discussed. In the study, mathematical models are also derived under two different circumstances, i.e., Case I: The credit period is less than or equal to the cycle time for settling the account; and Case II: The credit period is greater than the cycle time for settling the account. Besides, expressions for an inventory systems total cost are derived for these two cases. Moreover, a computational procedure and GINO (Lasdon et al., ACM Transactions Mathematical Software 4 (1978) 34–50) are proposed to obtain the optimal order size and cycle time. The results can help managers determine the optimal total cost. Finally, a numerical example demonstrates the applicability of the proposed model.

Multi-response robust design by principal component analysis

Abstract Most previous Taguchi method applications have only addressed a single-response problem. However, more than one correlated response normally occurs in a manufactured product. The multi-response problem has received only limited attention. In this work, we propose an effective procedure on the basis of principal component analysis (PCA) to optimize the multi-response problems in the Taguchi method. With the PCA, a set of original responses can be transformed into a set of uncorrelated components. Therefore, the conflict for determining the optimal settings of the design parameters for the multi-response problems can be reduced. Two case studies are evaluated, indicating that the proposed procedure yields a satisfactory result.

OPTIMIZING MULTI-RESPONSE PROBLEMS IN THE TAGUCHI METHOD BY FUZZY MULTIPLE ATTRIBUTE DECISION MAKING

One of the conventional approaches used in off-line quality control is the Taguchi method. However, most previous Taguchi method applications have only dealt with a single-response problem and the multi-response problem has received only limited attention. The theoretical analysis in this study reveals that Taguchis quadratic loss function and the indifference curve in the TOPSIS (Technique for order preference by similarity to ideal solution) method have similar features. The Taguchi method deals with a one-dimensional problem and TOPSIS handles multi-dimensional problems. As a result, the relative closeness computed in TOPSIS can be used as a performance measurement index for optimizing multi-response problems in the Taguchi method. Next, an effective procedure is proposed by applying fuzzy set theory to multiple attribute decision making (MADM). The procedure can reduce the uncertainty for determining a weight of each response and it is a universal approach which can simultaneously deal with continuous and discrete data. Finally, the effectiveness of the proposed procedure is verified with an example of analysing a plasma enhanced chemical vapour deposition (PECVD) process experiment.

The optimization of multi‐response problems in the Taguchi method

The Taguchi method is the conventional approach used in off‐line quality control. However, most previous Taguchi method applications have dealt only with a single‐response problem. The multi‐response problem has received only limited attention. Proposes an effective procedure on the basis of the quality loss of each response so as to achieve the optimization on multi‐response problems in the Taguchi method. The procedure is a universal approach which can simultaneously deal with continuous and discrete data. Evaluates a plasma‐enhanced chemical vapour deposition (PECVD) process experiment and a case study, indicating that the proposed procedure yields a satisfactory result.

A systematic methodology for the creation of Six Sigma projects: A case study of semiconductor foundry

Nowadays, Six Sigma has been widely adopted in a variety of industries in the world and it has become one of the most important subjects of debate in quality management. Six Sigma is a well-structured methodology that can help a company achieve expected goal through continuous project improvement. Some challenges, however, have emerged with the execution of the Six Sigma. For examples, how are feasible projects generated? How are critical Six Sigma projects selected given the finite resources of the organization? This study aims to develop a novel approach to create critical Six Sigma projects and identify the priority of these projects. Firstly, the projects are created from two aspects, namely, organizations business strategic policies and voice of customer. Secondly, an analytic hierarchy process (AHP) model is implemented to evaluate the benefits of each project and; a hierarchical failure mode effects analysis (FMEA) is also developed to evaluate the risk of each project; and from which the priority of Six Sigma projects can be determined. Finally, based on the project benefits and risk, projects can be defined as Green Belt, Black Belt, or others types of projects. An empirical case study of semiconductor foundry will be utilized to explore the effectiveness of our proposed approach.

An extended Chi2 algorithm for discretization of real value attributes

The variable precision rough sets (VPRS) model is a powerful tool for data mining, as it has been widely applied to acquire knowledge. Despite its diverse applications in many domains, the VPRS model unfortunately cannot be applied to real-world classification tasks involving continuous attributes. This requires a discretization method to preprocess the data. Discretization is an effective technique to deal with continuous attributes for data mining, especially for the classification problem. The modified Chi2 algorithm is one of the modifications to the Chi2 algorithm, replacing the inconsistency check in the Chi2 algorithm by using the quality of approximation, coined from the rough sets theory (RST), in which it takes into account the effect of degrees of freedom. However, the classification with a controlled degree of uncertainty, or a misclassification error, is outside the realm of RST. This algorithm also ignores the effect of variance in the two merged intervals. In this study, we propose a new algorithm, named the extended Chi2 algorithm, to overcome these two drawbacks. By running the software of See5, our proposed algorithm possesses a better performance than the original and modified Chi2 algorithms.

A neural-network approach for semiconductor wafer post-sawing inspection

Semiconductor wafer post-sawing requires full inspection to assure defect-free outgoing dies. A defect problem is usually identified through visual judgment by the aid of a scanning electron microscope. By this means, potential misjudgment may be introduced into the inspection process due to human fatigue. In addition, the full inspection process can incur significant personnel costs. This research proposed a neural-network approach for semiconductor wafer post-sawing inspection. Three types of neural networks: backpropagation, radial basis function network, and learning vector quantization, were proposed and tested. The inspection time by the proposed approach was less than one second per die, which is efficient enough for a practical application purpose. The pros and cons for the proposed methodology in comparison with two other inspection methods, visual inspection and feature extraction inspection, are discussed. Empirical results showed promise for the proposed approach to solve real-world applications. Finally, we proposed a neural-network-based automatic inspection system framework as future research opportunities.

Systematic layout planning: a study on semiconductor wafer fabrication facilities

This paper proposes to use Muther’s systematic layout planning procedure as the infrastructure to solve a fab layout design problem. A multiple objective decision making tool, analytic hierarchy process, is then proposed to evaluate the design alternatives. The proposed procedure is illustrated to be a viable approach for solving a fab layout design problem through a real‐world case study. It features both the simplicity of the design process and the objectivity of the multiple‐criteria evaluation process as opposed to existing solution methodologies.

A Kano-CKM model for customer knowledge discovery

Abstract In the digital economy, knowledge is regarded as an asset in an organization, and knowledge management (KM) implementation supports a company in developing innovative products and making critical management strategic decisions for business excellence. Customer relationship management (CRM) is an information-technology-enabling management tool, which manages the relationship with customers to understand, target, and attract them, with the objective of satisfying and retaining customers. Their synergy potential draws attention in the academic community and has led to the emergence of the customer knowledge management (CKM) model. In a knowledge management domain, an important task is the conversion of tacit knowledge into explicit knowledge, whereby the well-established Kanos Method has come up to demand and extract customer knowledge for attractive quality creation in new product development projects. As a consequence, this study proposes a Kano-CKM model with a methodology to delineate precisely the process of customer knowledge discovery for innovative product development. An industry level market research project applying the Kano-CKM model has been accomplished to present a satisfactory justification and will be described in the case study part of this article.

Knowledge acquisition through information granulation for imbalanced data

Abstract When learning from imbalanced/skewed data, which almost all the instances are labeled as one class while far few instances are labeled as the other class, traditional machine learning algorithms tend to produce high accuracy over the majority class but poor predictive accuracy over the minority class. This paper proposes a novel method called ‘knowledge acquisition via information granulation’ (KAIG) model which not only can remove some unnecessary details and provide a better insight into the essence of data but also effectively solve ‘class imbalance’ problems. In this model, the homogeneity index (H-index) and the undistinguishable ratio (U-ratio) are successfully introduced to determine a suitable level of granularity. We also developed the concept of sub-attributes to describe granules and tackle the overlapping among granules. Seven data sets from UCI data bank, including one imbalanced diagnosis data (pima-Indians-diabetes), are provided to evaluate the effectiveness of KAIG model. By using different performance indexes, overall accuracy, G-mean and Receiver Operation Characteristic (ROC) curve, the experimental results comparing with C4.5 and Support Vector Machine (SVM) demonstrate the superiority of our method.