Higher separation axioms in bifuzzy topological spaces

Safiya, A. S. Abu; Fora, Ali; Warner, M. W.

doi:10.1016/0165-0114(94)00093-x

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…In other words, the bifuzzy variable is a fuzzy variable with fuzzy parameters. The properties of bifuzzy variables were also examined by Safiya et al [17], Srivastava and Srivastava [20] and Mukherjee [15] from various topological perspectives.…”

Section: Introductionmentioning

confidence: 99%

Imprecise data envelopment analysis model with bifuzzy variables

Paryab

Shiraz

Jalalzadeh

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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Data envelopment analysis (DEA) is a non-parametric approach for measuring and evaluating the relative efficiencies of a set of entities with common crisp inputs and outputs. Whereas crisp input-output data are required in the traditional DEA evaluation process, the observed input-output data in real-world performance evaluation problems are quite often imprecise or vague. The impreciseness and vagueness related to the input-output data in DEA can be represented by fuzzy variables. The purpose of this paper is three-fold. First, the current study introduces a non-deterministic chance constrained DEA model, which solves the Charnes, Cooper and Rhodes (CCR) model by treating the input-output data as bifuzzy variables which are fuzzy variables with fuzzy parameters. Second, by assuming that decision making units operate in a bifuzzy environment, the study derives a deterministic model equivalent to the chance constrained model. Finally, two numerical examples are presented to demonstrate the applicability of the proposed framework.

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Section: Introductionmentioning

confidence: 99%

Imprecise data envelopment analysis model with bifuzzy variables

Paryab

Shiraz

Jalalzadeh

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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show abstract

“…As a result, introduced the concept of ''bifuzzy variable'' that maps functions with double fuzziness between the credibility space to the set of fuzzy variables. Later, Safiya et al (1996), Srivastava and Srivastava (2001) and Mukherjee (2002) investigated the properties of the bifuzzy topological spaces from various perspectives.…”

Section: Introductionmentioning

confidence: 99%

The sufficient and necessary condition for chance distribution of bifuzzy variable

Qin

2010

Soft Comput

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Fuzzy sets and fuzzy variables have undergone several different extensions overtime. One of them involved including a ''bifuzzy variable'' as a fuzzy element for describing the more complete systems. The properties of bifuzzy variable were obtained by introducing the concept of ''chance distribution''. In this paper, we will present a sufficient and necessary condition for chance distribution of bifuzzy variable. Here we present a constructive proof base on credibility theory for the sufficient part.

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On intuitionistic gradation of openness

Mondal

Samanta

2002

Fuzzy Sets and Systems

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Higher separation axioms in bifuzzy topological spaces

Cited by 4 publications

References 7 publications

Imprecise data envelopment analysis model with bifuzzy variables

Imprecise data envelopment analysis model with bifuzzy variables

The sufficient and necessary condition for chance distribution of bifuzzy variable

On intuitionistic gradation of openness

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