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Since the performance measure is completely described by a membership function rather than by a crisp value, the fuzziness of input parameters is conserved completely.
Since the objective value is completely expressed by a membership function rather than by a crisp value, it conserves the fuzziness of the input information, thus more information is provided for designing queueing systems.
The mathematical relationship is modeled by a membership function.
A fuzzy set A in the universe X is characterized by a membership function μ A : X → [0, 1].
A fuzzy set x ˜ of ℝ is characterized by a membership function μ x ˜ : R → [ 0, 1 ].
A fuzzy set A defined on the universe X is characterized by a membership function such that μ A ( x ) : x → [ 0, 1 ].
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Contrary, the fuzzy set theory extends the membership of an element by introducing a membership function μ(x) with μ(x) ∈ [0,���1].
(2004) extended the fuzzy approach of Abo-Sinna (2001) for solving non-linear bi-level and tri-level multiobjective decision making under fuzziness by defining a membership function of each objective function and control variable of each decision maker to formulate a single level programming problem which is simpler to solve than the original problem.
The fuzzy requirements of the problem (3.1) can be quantified by electing a membership function μ ( f ( x ) ) (Figure 2) which is differentiable in the open interval f ( x 1 ) < f ( x ) < f ( x 0 ) where μ ( f ( x ) ) is defined by μ ( f ( x ) ) = { 1, f ( x ) ≤ f ( x 1 ), f ( x ) − f ( x 1 ) f ( x ¯ 0 ) − f ( x ¯ 1 ), f ( x 1 ) ≤ f ( x ) ≤ f ( x 0 ), 0, f ( x ) ≥ f ( x 0 ), (4.6).
The MADs are calculated by means of a membership function and they are then combined using "fuzzy mixed connectives" as aggregation operators in order to obtain the global adequacy degree (GAD) of an element to a class.
A membership function, denoted by μ z (x), reflects the degree of membership of an input value x to a fuzzy set z with a value between 0 and 1.
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