首页> 外文期刊>Fuzzy Optimization and Decision Making: A Journal of Modeling and Computation Under Uncertainty >The Karush-Kuhn-Tucker optimality conditions for multi-objective programming problems with fuzzy-valued objective functions
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The Karush-Kuhn-Tucker optimality conditions for multi-objective programming problems with fuzzy-valued objective functions

机译:具有模糊值目标函数的多目标规划问题的Karush-Kuhn-Tucker最优性条件

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摘要

The KKT optimality conditions for multiobjective programming problems with fuzzy-valued objective functions are derived in this paper. The solution concepts are proposed by defining an ordering relation on the class of all fuzzy numbers. Owing to this ordering relation being a partial ordering, the solution concepts proposed in this paper will follow from the similar solution concept, called Pareto optimal solution, in the conventional multiobjective programming problems. In order to consider the differentiation of fuzzy-valued function, we invoke the Hausdorff metric to define the distance between two fuzzy numbers and the Hukuhara difference to define the difference of two fuzzy numbers. Under these settings, the KKT optimality conditions are elicited naturally by introducing the Lagrange function multipliers.
机译:本文推导了具有模糊值目标函数的多目标规划问题的KKT最优条件。通过在所有模糊数的类别上定义排序关系来提出解决方案概念。由于这种排序关系是部分排序,因此本文提出的解决方案概念将遵循常规多目标规划问题中类似的称为Pareto最优解的解决方案概念。为了考虑模糊值函数的微分,我们调用Hausdorff度量来定义两个模糊数之间的距离,并调用Hukuhara差来定义两个模糊数之间的差。在这些设置下,通过引入拉格朗日函数乘法器自然可以得出KKT最优条件。

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