具有模糊联合参数的线性规划:一种累积隶属函数方法

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society Pub Date : 2008-05-19 DOI:10.1109/NAFIPS.2008.4531293

J. Garcia, C. Bello

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引用次数: 15

摘要

本文给出了一种寻找模糊环境下线性规划问题最优解的替代方法。利用Ax - s - s格式的模糊约束来处理经典模糊线性规划(FLP)问题，其中表示1型模糊集(Tl - s)。该方法采用阿布列夫和布列夫联合模糊参数来求解不确定条件下的线性规划模型。三角模糊集用于降低模型的计算复杂度，但也可以使用其他类型的模糊集。定义了一种累积隶属函数(CMF)方法，讨论了若干最优性条件，并证明了一个新定理。最后提供了一个小示例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Linear Programming with fuzzy joint parameters: A Cumulative Membership Function approach

This paper shows an alternative methodology to find optimal solutions of a linear programming problem defined in a fuzzy environment. The classical fuzzy linear programming (FLP) problem is treated by using fuzzy restrictions in the form Ax les bbreve where indicates a type-1 fuzzy set (Tl FS). The proposed approach uses joint Abreve and bbreve fuzzy parameters to solve a linear programming model under uncertainty conditions. Triangular fuzzy sets are used to reduce the computational complexity of the model, however other types of fuzzy sets can be used. A cumulative membership function (CMF) approach is defined, some optimality conditions are discussed and a new theorem is proved. Finally a small example is provided.

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NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society

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