Ta-Chung Chu

Bio: Ta-Chung Chu is an academic researcher from National Taiwan University. The author has contributed to research in topics: Fuzzy logic & Fuzzy number. The author has an hindex of 5, co-authored 5 publications receiving 846 citations.

Ranking fuzzy numbers with an area between the centroid point and original point

Abstract: To improve the ranking method of Lee and Li [1], Cheng [2] proposed the coefficient of variation (CV index). Shortcomings are also found in the CV index. Cheng [2] also proposed the distance method to improve the ranking method of Murakami et al. However, the distance method is not sound either. Moreover, the CV index contradicts the distance method in ranking some fuzzy numbers. Therefore, to overcome the above shortcomings, we propose ranking fuzzy numbers with the area between the centroid point and original point.

Facility location selection using fuzzy topsis under group decisions

Abstract: This work presents a fuzzy TOPSIS model under group decisions for solving the facility location selection problem, where the ratings of various alternative locations under different subjective attributes and the importance weights of all attributes are assessed in linguistic values represented by fuzzy numbers. The objective attributes are transformed into dimensionless indices to ensure compatibility with the linguistic ratings of the subjective attributes. Furthermore, the membership function of the aggregation of the ratings and weights for each alternative location versus each attribute can be developed by interval arithmetic and α -cuts of fuzzy numbers. The ranking method of the mean of the integral values is applied to help derive the ideal and negative-ideal fuzzy solutions to complete the proposed fuzzy TOPSIS model. Finally, a numerical example demonstrates the computational process of the proposed model.

An extension to fuzzy MCDM

Abstract: An extension to the fuzzy multiple criteria decision making (MCDM) model is suggested in this work, where the ratings of alternatives versus criteria, and the importance weights of all criteria, are assessed in linguistic values represented by fuzzy numbers. Moreover, values of alternatives under objective criteria are normalized by a suggested approach. Meanwhile, the membership function of the final fuzzy evaluation value of each alternative can be developed. In addition, a Riemann integral based mean of removals is suggested to rank all the final fuzzy evaluation values for final decision making, so that the ranking procedure can be clearly formulated. Finally, a numerical example demonstrates the feasibility of the proposed model.

An improved fuzzy mcdm model based on ideal and anti-ideal concepts

Abstract: Liaiig presented a fuzzy multiple criteria decision making (MCDM) method based on the concepts of ideal and anti-ideal points. Despite its merits, Liang method has the following limitations: (i) the objective criteria are converted into dimensionless indices and the subjective criteria are not converted, which may prevent compatibility for these criteria, (ii) the formulas for converting objective criteria are not reliable, and (iii) an unreliable ranking method, i.e. maximizing set and minimizing set, is applied to rank the fuzzy numbers. This paper applies the Hsu and Chen method and suggests a fuzzy number ranking method to propose an improved fuzzy MCDM model based on ideal and anti-ideal concepts to overcome the shortcomings of the Liang method. Numerical examples demonstrate the effectiveness and feasibility of the proposed ranking method and the improved model, respectively.

A novel defuzzifying approach to car evaluation and selection under fuzzy environment

Abstract: This paper proposes a new method for ranking fuzzy numbers by difference between relative areas. Comparative examples illustrate the advantage of the proposed method. The ranking method is further applied to help establish a defuzzified multiple level FMADM model, which avoids the complicated aggregation of fuzzy numbers so that the multiple level FMADM problem can be efficiently solved. A numerical example of car evaluation and selection illustrates the feasibility of the proposed model.

Fuzzy sets and systems

Abstract: The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment

Abstract: This paper proposes a fuzzy TOPSIS method based on alpha level sets and presents a nonlinear programming (NLP) solution procedure. The relationship between the fuzzy TOPSIS method and fuzzy weighted average (FWA) is also discussed. Three numerical examples including an application to bridge risk assessment are investigated using the proposed fuzzy TOPSIS method to illustrate its applications and the differences from the other procedures. It is shown that the proposed fuzzy TOPSIS method performs better than the other fuzzy versions of the TOPSIS method.

State of art surveys of overviews on MCDM/MADM methods

Abstract: Decision-making is primarily a process that involves different actors: people, groups of people, institutions and the state. As a discipline, multi-criteria decision-making has a relatively short history. Since 1950s and 1960s, when foundations of modern multi-criteria decision-making methods have been laid, many researches devoted their time to development of new multi-criteria decision-making models and techniques. In the past decades, researches and development in the field have accelerated and seem to continue growing exponentially. Despite the intensive development worldwide, few attempts have been made to systematically present the theoretical bases and developments of multi-criteria decision-making methods. However, the methodological choices and framework for assessment of decisions are still under discussion. The article describes the situation with reviews of MCDM/MADM methods. Furthermore, there is a need for research to study the strengths and weaknesses of different decision-making me...