Comment on “A fuzzy soft set theoretic approach to decision making problems”

Kong, Zhi; Gao, Liqun; Wang, Lifu

doi:10.1016/j.cam.2008.01.011

Cited by 223 publications

(128 citation statements)

References 8 publications

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“…As mentioned above, a list of different approaches to fuzzy soft set based decision making includes the references Roy and Maji [2], Kong et al [3], Feng et al [4] and Alcantud [5]. We proceed to survey their main characteristics and differences below in subsection 2.2.…”

Section: Definition 2 (Maji Biswas and Roy [14])mentioning

confidence: 99%

“…Roy and Maji [2] first proposed an algorithm to prioritize alternatives or objects characterized by fuzzy soft sets. A different procedure is given by Kong et al [3]. Although these authors claim that the Roy and Maji's algorithm is incorrect on the grounds of a single naive example, such presumed "counterexample" does not stand inspection (cf., Feng et al [4,Subsection 3.2]).…”

Section: Fuzzy Soft Set Based Decision Making: a Review Of The Literamentioning

confidence: 99%

“…In this paper we revisit the fuzzy soft set based decision making problem as posed by Roy and Maji [2], and subsequently approached by authors like Kong et al [3], Feng et al [4] and Alcantud [5].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy soft set based decision making: a novel alternative approach

Alcantud¹

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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We take advantage of a recent development in Social Choice in order to propose a new algorithm for the prioritization of objects characterized by fuzzy soft sets. It benefits from the performance of an endogenous scoring rule recently proposed by Herrero [1]. Our procedure constructs a "comparison matrix" and grades the objects according to the corresponding component of an eigenvector associated with its dominant eigenvalue. This eigenvector can be easily calculated because the dominant eigenvalue is computed directly from the size of the tabular representation of the problem.

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Section: Definition 2 (Maji Biswas and Roy [14])mentioning

confidence: 99%

Section: Fuzzy Soft Set Based Decision Making: a Review Of The Literamentioning

confidence: 99%

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Fuzzy soft set based decision making: a novel alternative approach

Alcantud¹

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…Rodríguez et al [13] overviewed on fuzzy modeling of complex linguistic preferences in decision-making and pointed out the different points of view used in each proposal to model these complex preferences. Kong et al [14] revised this method, and their revision (the fuzzy choice value based method) has been proved as another method based on the maximum fuzzy choice value. Feng et al [15] presented a novel approach to fuzzy soft set based decision-making problems by using level soft sets.…”

Section: Introductionmentioning

confidence: 99%

A Method for Fuzzy Soft Sets in Decision-Making Based on an Ideal Solution

2017

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Abstract:In this paper, a decision model based on a fuzzy soft set and ideal solution approaches is proposed. This new decision-making method uses the divide-and-conquer algorithm, and it is different from the existing algorithm (the choice value based approach and the comparison score based approach). The ideal solution is generated according to each attribute (pros or cons of the attributes, with or without constraints) of the fuzzy soft sets. Finally, the weighted Hamming distance is used to compute all possible alternatives and get the final result. The core of the decision process is the design phase, the existing decision models based on soft sets mostly neglect the analysis of attributes and decision objectives. This algorithm emphasizes the correct expression of the purpose of the decision maker and the analysis of attributes, as well as the explicit decision function. Additionally, this paper shows the fact that the rank reversal phenomenon occurs in the comparison score algorithm, and an example is provided to illustrate the rank reversal phenomenon. Experiments indicate that the decision model proposed in this paper is efficient and will be useful for practical problems. In addition, as a general model, it can be extended to a wider range of fields, such as classifications, optimization problems, etc.

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“…Roy and Maji [11] proposed a novel method of object recognition from an imprecise multi-observer data and a decision making application of fuzzy soft set. Although the algorithm was proved incorrectly by Kong et al [12], fuzzy soft sets and multi-observer concepts are valuable to successive researchers. Cagman and Enginoglu [13] defined products of soft sets and uni-int decision function.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for decision making with dependence & feedback under incomplete information

Chen

Zou

2016

SHS Web of Conferences

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This paper presents a hybrid method to tackle multiple criteria decision making problems with incomplete weight information in the context of fuzzy soft sets. In order to determine the weights of criteria, we develop a comprehensive two-stage framework. Stage One: We first define the distance between two fuzzy soft numbers. Next, we establish an optimization model based on ideal point of attribute values, by which the attribute weights can be determined. Stage Two: To get the global weights, we use fuzzy cognitive maps to depict the dependent and feedback effect among criteria. Next, we require constructing fuzzy soft set to decide the desirable alternative. Finally, a case study is given to clarify the proposed approach of this paper.

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Comment on “A fuzzy soft set theoretic approach to decision making problems”

Cited by 223 publications

References 8 publications

Fuzzy soft set based decision making: a novel alternative approach

Fuzzy soft set based decision making: a novel alternative approach

A Method for Fuzzy Soft Sets in Decision-Making Based on an Ideal Solution

A hybrid method for decision making with dependence & feedback under incomplete information

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