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.)
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
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 . 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.