عنوان مجله
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Sahand Communications in Mathematical Analysis
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چکیده
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The inherent feature of real-world data is uncertainty.
If data is generated in valid experiments or standard collections,
probability theory or fuzzy theory is a powerful tool for analyzing them. But data is not always reliable, especially when it is
not possible to perform a reliable test or data collection multiple
times. In this situations, referring to the beliefs of experts in the
field in question is an alternative approach and uncertainty theory
is a tool by which the beliefs of experts can be mathematically incorporated into the problem-solving structure. In this paper, we
investigate the finding minimum weighted maximal matching with
uncertain weights. For this purpose, we offer two methods. In the
first method, by introducing the concept of chance constraint, we
obtain model with definite coeffcients. The second method is based
on the concept of uncertain expected value. Finally, a numerical
example for these two methods is presented.
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