Hsuan-Shih Lee

A fuzzy multi-criteria decision making model for the selection of the distribution center

The location selection of distribution is one of the most important decision issues for logistic managers. In order to encompass vagueness in decision data, a new fuzzy multiple criteria decision-making method is proposed to solve the distribution center selection problem under fuzzy environment. In the proposed method, the ratings of alternatives and the weights of the criteria are given in terms of linguistic variables which is in turns represented by triangular fuzzy numbers.

A fuzzy multiple criteria decision making model for airline competitiveness evaluation

This paper presents a fuzzy multiple criteria decision making model to the evaluation of airline competitiveness over a period. The evaluation problem is formulated as a fuzzy multiple criteria decision making problem and solved by our strength-weakness based approach. After the strength and weakness matrices for airlines are derived, the weights of criteria, strength matrix and weakness matrix can be aggregated into strength indices and weakness indices for airlines, by which each airline can identify his own strength and weakness. The strength and weakness indices can be further integrated into an overall performance indices, by which airlines can identify their competitiveness ranking.

A fuzzy multiple objective DEA for the human development index

Traditionally, the HDI is calculated under the assumption that all component indices are given the same weights. Although this assumption has been supported in the Human Development Reports, it has met also considerable criticism in the literature. In this paper, we present a new model to determine the weights of component indices in the light of data envelopment analysis (DEA). We develop a fuzzy multiple objective DEA model to assess the relative performance of the countries in terms of human development by using optimal weights for the component indices of the HDI.

Estimating Missing Values in Incomplete Additive Fuzzy Preference Relations

In some occasions, decision makers may have to deal with the problems in which only partial information is available. As a result, decision makers embody their preferences as incomplete fuzzy preference relations. In this paper, we propose an iterative procedure to estimate the missing values of the incomplete fuzzy preference relations that are assumed to be additive. The procedure is based on additive transitivity property. Measures of consistency and completeness of an incomplete fuzzy preference are also developed to assist decision makers to identify the quality of their decisions.

A fuzzy method for evaluating suppliers

A key objective of procurement is to purchase the right product from right supplier at right price in due time. In this paper, we are going to propose a fuzzy method for the evaluation of suppliers under fuzzy environment. First we identify the strength and weakness of suppliers from their ratings. Based on the strength and weakness of suppliers, a fuzzy preference relation is constructed. Then we propose a linear programming model to derive the priorities of suppliers.

Goal programming methods for constructing additive consistency fuzzy preference relations

Decision makers may present their preferences over alternatives as fuzzy preference relations. Usually, there exist inconsistencies in the preference relation given by decision makers. In this paper, we propose methods based on goal programming to obtain fuzzy preference relations that satisfy additive consistency from the subjective preference relations given by decision makers.

Correspondence between Incomplete Fuzzy Preference Relation and Its Priority Vector

Fuzzy preference relations are frequently adopted by decision makers to express their preference tendency toward alternatives. Due to the lack of expertise of knowledge, decision makers may not be able to specify complete preference relation. To deal with incomplete fuzzy preference relations, Xu [26] proposed prioritization methods for incomplete fuzzy preference relations where he postulated a correspondence between priority vector and additive consistent incomplete fuzzy preference relation. In this paper, we are going to prove the correspondence does not always hold.

A fuzzy method for measuring efficiency under fuzzy environment

DEA (data envelopment analysis) is a non-parametric technique for measuring and evaluating the relative efficiencies of a set of decision-making units (DMUs) in terms of a set of common inputs and outputs. Traditionally, the data of inputs and outputs are assumed to be measured with precision, i.e., the coefficients of DEA models are crisp value. However, this may not be always true. There are many circumstances where precise inputs and outputs can not be obtained. Under such situations, data of inputs and outputs can be represented by fuzzy numbers. Based on the dual program of DEA models, we propose fuzzy DEA models for CCR and BCC models. Our fuzzy DEA models provide crisp efficiency with fuzzy input and output data.

Prioritization of Incomplete Fuzzy Preference Relation

The concept of incomplete fuzzy preference relation is discussed in this paper. We focus on additive consistent incomplete fuzzy preference relations, in which the correspondence between priority vector and incomplete fuzzy preference relation is investigated. We find that an additive consistent incomplete fuzzy preference relation does not imply there is a priority vector that satisfies additive transitivity.

Fuzzy Multi-criteria Decision Making Based on Fuzzy Preference Relation

In this paper, we propose models to determine the weights of attributes by combining subjective information given by fuzzy preference relation and objective information given by the decision matrix. The fuzzy preference relations considered include the fuzzy preference relation with multiplicative transitivity and the fuzzy preference relation with additive transitivity. The proposed approaches are simpler than the previous methods that combine both subjective and objective information.