Applications of Fuzzy Sets Theory: 7th International by Célia da Costa Pereira, Andrea G. B. Tettamanzi (auth.),

By Célia da Costa Pereira, Andrea G. B. Tettamanzi (auth.), Francesco Masulli, Sushmita Mitra, Gabriella Pasi (eds.)

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Read Online or Download Applications of Fuzzy Sets Theory: 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007. Proceedings PDF

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Additional resources for Applications of Fuzzy Sets Theory: 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007. Proceedings

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But applying probability theory to some optimization problems may have a negative effect on the computational efficiency. Alternatively, PLP approach not only provides more computational efficiency, but also supports possibilistic decision environment [12,22,24]. Possibility distribution offers an effectual alternative for proceeding with inherent ambiguous phonemia in determining environmental coefficients and related parameters [12]. Therefore a possibilistic programming model is constructed to determine the optimum transportation amount between storage site and customer by transportation mode.

However, although aggregators &L and ∨G have been evaluated in both derivations, in D1∗ such evaluations have not been explicitly counted as interpretive steps, and consequently they have not been added to increase the interpretive cost measure Ic . This unrealistic situation is reflected by the abnormal result: Ic (D1 ) = 4 > 2 = Ic (D1∗ ). It is important to note that R∗1 can not be considered an optimized version of R1 : both rules have a similar computational behaviour, even when the wrong measure Ic seems to indicate the contrary.

Related studies on the use of fuzzy programming method to solve the fuzzy transportation problems include [8,13,14,19,21]. In this study, blending, storage and transportation costs are represented by triangular possibility distributions. The parameters of a triangular possibility distribution are given as the optimistic, the most possible, and the pessimistic values, which were estimated by a decision maker. 22 3 B. Bilgen Possibilistic Programming Model Development The following notation is used.

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