Liang Hsuan Chen

An evaluation approach to engineering design in QFD processes using fuzzy goal programming models

Quality function deployment (QFD) is a product development process used to achieve higher customer satisfaction: the engineering characteristics affecting the product performance are designed to match the customer requirements. From the viewpoint of QFDs designers, product design processes are performed in uncertain environments, and usually more than one goal must be taken into account. Therefore, when dealing with the fuzzy nature in QFD processes, fuzzy approaches are applied to formulate the relationships between customer requirements (CRs) and engineering design requirements (DRs), and among DRs. In addition to customer satisfaction, the cost and technical difficulty of DRs are also considered as the other two goals, and are evaluated in linguistic terms. Fuzzy goal programming models are proposed to determine the fulfillment levels of the DRs. Differing from existing fuzzy goal programming models, the coefficients in the proposed model are also fuzzy in order to expose the fuzziness of the linguistic information. Our model also considers business competition by specifying the minimum fulfillment levels of DRs and the preemptive priorities between goals. The proposed approach can attain the maximal sum of satisfaction degrees of all goals under each confidence degree. A numerical example is used to illustrate the applicability of the approach. 2004 Elsevier B.V. All rights reserved.

AN APPROXIMATE APPROACH FOR RANKING FUZZY NUMBERS BASED ON LEFT AND RIGHT DOMINANCE

This study presents an approximate approach for ranking fuzzy numbers based on the left and right dominance. The proposed approach only requires a few left and right spreads at some α-levels of fuzzy numbers to determine the respective dominance of one fuzzy number over the other. The total dominance is then determined by combining the left and right dominance based on a decision makers optimistic perspectives. Such a dominance is useful in ranking the fuzzy numbers when membership functions cannot be acquired. The approach proposed herein is relatively simple in terms of computational efforts and is efficient when ranking a large quantity of fuzzy numbers. By using a few left and right spreads, two groups of examples demonstrate the accuracy and applicability of the proposed approach.

Integrated vendor-buyer cooperative inventory models with variant permissible delay in payments

Abstract The integrated inventory models usually have the advantage of reducing total cost. However, the way to allocate the cost savings from the integration to the buyer and vendor is critical to the success of the joint relationship between both sides. To deal with this problem, this paper develops the integrated models with the permissible delay in payments for determining the optimal replenishment time interval and replenishment frequency. Applying the models and considering the coefficient of negotiation and the maximum delay payment period, a simple solution algorithm is presented to resolve the allocation of cost savings in the integration model. The coefficient of negotiation is adopted to determine the compromise between the buyer’s and vendor’s cost savings. A numerical example is used to demonstrate the feasibility of the proposed integration models. A sensitivity analysis is also conducted to demonstrate some properties. Using the information from the proposed models, the buyer and vendor can achieve an acceptable compromise solution for both sides in the supply chain management.

A fuzzy credit-rating approach for commercial loans: a Taiwan case

Credit rating for commercial loans is an important task for loan officers of a bank who usually use a credit-rating table based on a point system. However, the employment of such a table may neglect the fuzzy nature of credit-rating processes. This paper presents a fuzzy credit-rating approach to deal with the problem arisen from the credit-rating table currently used in Taiwan. First, the evaluation criteria are modeled as a hierarchical decision structure. An evidence fusion technique, namely the fuzzy integral, is then employed for aggregating credit information in a bottom-up way. This technique not only considers the objective evidence but also the relative importance of each criterion. Furthermore, the proposed approach uses fuzzy sets (fuzzy numbers) to describe the criteria, so that the final credit-rating results can reveal changes of credit information. The membership degrees of the five rating levels for describing the final evaluation results can provide loan officers with more valuable information for making decisions. A numerical example is used to demonstrate the applicability of the approach.

A fuzzy model for exploiting quality function deployment

Quality function deployment (QFD) is the product development process to maximize customer satisfaction. The engineering design characteristics related to product performance are specified for this purpose. For dealing with the fuzzy nature in the product design processes, fuzzy approaches are applied to represent the relationships between customer requirements (CRs) and engineering design requirements (DRs) as well as among the DRs. A new measure for evaluating the fuzzy normalized relationships is derived. A fuzzy model is formulated to determine the fulfillment level of each DR for maximizing the customer satisfaction under the resource limitation and the considerations of technical difficulty and market competition. The producing ranges of fulfillment level of each DR and those of customer satisfaction can provide the QFD team with more information. An example is used to illustrate the model.

Fuzzy approaches to quality function deployment for new product design

For new product development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of the fulfillment levels of design requirements (DRs) and parts characteristics (PCs) in phases 1 and 2 is an important issue during QFD processes for new product design. Unlike the existing literature, which mainly focuses on the DRs, this paper proposes fuzzy nonlinear programming models based on Kanos concept to determine the fulfillment levels of PCs with the aim of achieving the determined contribution levels of DRs in phase 1 for customer satisfaction. In addition, to deal with the design risk, this study incorporates failure modes and effects analysis (FMEA) into QFD processes, and treats it as the constraint factor in the models. To cope with the vague nature of product development processes, fuzzy approaches are used for both FMEA and QFD. The applicability of the proposed models in practice is demonstrated with a numerical example.

A fuzzy nonlinear model for quality function deployment considering Kano's concept

Quality function deployment (QFD) is a customer-driven approach for processing new product developments in order to maximize customer satisfaction. Each engineering design characteristic is maximized for product performance according to the level of customer satisfaction. To cope with the vague nature of product development processes, fuzzy approaches are used to represent the importance scores of customer requirements (CRs), the relationship between CRs and design requirements (DRs) and relationship between the DRs themselves. Considering Kanos category of design requirements, this paper extends Chen and Wengs model (2003), and presents a fuzzy nonlinear model to determine the performance level of each DR for maximizing customer satisfaction, under the same group of constraints as that in Chen and Wengs model. The results, from an illustrative example, indicate that the total degree of customer satisfaction achieved by the proposed nonlinear model is greater than that by the original fuzzy model.

Measuring the national competitiveness of Southeast Asian countries

National competitiveness is a measure of the relative ability of a nation to create and maintain an environment in which enterprises can compete so that the level of prosperity can be improved. This paper proposes a methodology for measuring the national competitiveness and uses the 10 Southeast Asian countries for illustration. The basic idea is to deconstruct the complicated concept of national competitiveness to measurable criteria. The observations (data) on the criteria are then aggregated according to their importance to obtain an index of national competitiveness. For data collected from questionnaire surveys, a calibration technique has been devised to alleviate bias due to personal prejudice. In data aggregation, the importance is expressed by both a priori weights and a posteriori weights. These two types of weights consistently show that Singapore, Malaysia, and Thailand have the highest national competitiveness, while Myanmar, Cambodia, and Laos are the least competitive countries. The performance of each country in every criteria measured also provides directions for these countries to make improvements and for investors to allocate resources.

Portfolio optimization of equity mutual funds with fuzzy return rates and risks

Portfolio selection is an important issue for researchers and practitioners. Focusing on equity mutual funds, this paper proposes a basic portfolio selection model in which future return rates and future risks of mutual funds are represented by triangular fuzzy numbers. Firstly, a cluster analysis is proposed to categorize the huge amount of equity mutual funds into several groups based on four evaluation indices: rates of return, standard deviation, turnover rate, and Treynor index, in order to aid investors in making the investment decision. The fuzzy optimization model is proposed to determine the optimal investment proportion of each cluster. The portfolio optimization problem is developed in two ways: to maximize the future expected return subject to the given greatest future risk, and to minimize the future risk subject to a required lowest future expected return. The proposed approaches are demonstrated by Taiwan equity mutual funds.

Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study

This research is aimed at establishing the diagnosis models for business crises through integrating a real-valued genetic algorithm to determine the optimum parameters and SVM to perform learning and classification on data. After finishing the training processes, the proposed GA-SVM can reach a prediction accuracy of up to 95.56% for all the tested business data. Particularly, only six influential features are included in the proposed model with intellectual capital and financial features after the 2-phase selecting process; the six features are ordinary and widely available from public business reports. The proposed GA-SVM is available for business managers to conduct self-diagnosis in order to realize whether business units are really facing a crisis.