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2 Fundamenta Informaticae

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Interval-valued Fuzzy Soft Decision Making Methods Based on MABAC, Similarity Measure and EDAS

Peng X., Dai J., Yuan H.

Fundamenta Informaticae

2017

Vol. 152, nr 4

373--396

Interval-valued fuzzy soft decision making problems have obtained great popularity recently. Most of the current methods depend on level soft set that provide choice value of alternatives to be ranked. Such choice value always encounter the equal condition that the optimal alternative can't be gained. Most important of all, the current decision making procedure is not in accordance with the way that the decision makers think about the decision making problems. In this paper, we initiate a new axiomatic definition of interval-valued fuzzy distance measure and similarity measure, which is expressed by interval-valued fuzzy number (IVFN) that will reduce the information loss and keep more original information. Later, the objective weights of various parameters are determined via grey system theory, meanwhile, we develop the combined weights, which can show both the subjective information and the objective information. Then, we present three algorithms to solve interval-valued fuzzy soft decision making problems by Multi- Attributive Border Approximation area Comparison (MABAC), Evaluation based on Distance from Average Solution (EDAS) and new similarity measure. Three approaches solve some unreasonable conditions and promote the development of decision making methods. Finally, the effectiveness and feasibility of approaches are demonstrated by some numerical examples.

A Revised TOPSIS Method Based on Interval Fuzzy Soft Set Models with Incomplete Weight Information

Yang Y., Peng X.

Fundamenta Informaticae

2017

Vol. 152, nr 3

297--321

Soft set theory was originally proposed by Molodtsov in 1999 as a general mathematical tool for dealing with uncertainty. However, it has been pointed out that classical soft set model is not appropriate to deal with imprecise and fuzzy problems. In order to handle these types of problems, some fuzzy extensions of soft set theory are presented, yielding fuzzy soft set theory. As a further research, in this work, we first propose concepts of interval fuzzy sets and interval fuzzy soft sets, define some operations on them and study some of their relevant properties, especially, the dual laws are discussed with respect to difference operation in interval fuzzy soft set theory. We then introduce a revised Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and choice value method for interval fuzzy soft set which the weight information is completely unknown. Meanwhile, an analysis of computation complexity is employed, also the discriminative power of two methods are shown. Finally, two illustrative examples are employed to show that they can be successfully applied to problems that contain uncertainties.