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An approach using defuzzifying methods is proposed for the fuzzy multiple attribute decision-making (MADM) problems. The computing effectiveness of the proposed defuzzifying methods combined with the simple additive weighting (SAW) method and the technique for order preference by similarity to ideal solution (TOPSIS) method are evaluated based on a comparison to the improved fuzzy weighted average (IFWA) followed by a ranking method. Both SAW and TOPSIS methods are two of classic MADM methods. The purpose of this application is to make the method easier to program and data easier to manipulate. This results in a more practical method for fuzzy decisions. A numerical example and experiment are discussed to demonstrate the implementation of the methods in different input conditions.

This paper presents a new approach to optimizing the cutting glass fiber with multiple performance characteristics, based on reliability analysis, Taguchi, and fuzzy logic. The speed, volume, and cutting load parameters were optimized during the cutting process with the application of the Weibull modulus to the blade wear. In this study, optimization with multiple performance characteristics was found to be the highest cutting speed and smallest cutting volume, and highest cutting load. An analysis of variance (ANOVA) of the multiple performance characteristic indicates that the cutting speed (37.22%), the cutting volume (26.74%), and the cutting load (20.50%) are the crucial parameters in the cutting process of glass fibers. In summary, the optimal cutting parameter should be A3B1C2. The experimental results are presented to demonstrate the effectiveness of this approach.

The common version of Genichi Taguchi's parameter design entails a marginal analysis procedure for determining parameter settings that will maximize or minimize a response. Unless all significant parameter interactions are known a priori and provided for in the orthogonal array of the parameter design experiment, conclusions drawn from marginal analysis will not necessarily be correct. In this paper, the probability that the routine parameter design procedure will actually succeed in realizing the optimization objective is discussed, followed by illustrations based on data from the commonly cited literature.

Two-level fractional factorial design is an efficient technique for experiments considering a large number of factors. To evaluate the efficiency and analyze the data for such a design, we need to know the generators for the design, so that, using the generators, we can generate its defining relation and alias structure. Although knowing the generators is important for a two-level fractional factorial design, it is not unusual in actual industrial situations for the generators used in the design to be lost or overlooked while the design is performed. Since Taguchi methods has been widely applied in industry, in this research, an efficient algorithm based on Taguchi orthogonal arrays (OA's) and interaction tables is developed to identify the generators for given designs. Furthermore, with the investigation of the insights of Taguchi OA's and interaction tables, this research may provide ideas for making Taguchi methods a simple tool for developing optimal designs for 2^{k - p} experiments.

Developed in Japan and becoming increasingly talked about under the name 'Taguchi methods', robust design minimizes the sensitivity of a product or a process to external uncontrolled factors. The present paper expands robust design methods to situations common in electronics design in which design factors interact or in which multiple performance characteristics are to be made robust. This paper proposes a constrained optimization method to achieve robust performance of electronic devices, by limiting attention to only the truly relevant (i.e. feasible) designs that meet all target performance criteria. A recently-solved circuit design problem is revisited to illustrate the proposed method's superior model development, optimization, and robustness-seeking capability.