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arXiv 0907.0644math.OC
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一类模糊整数规划的复杂性

On the complexity of some fuzzy integer programs

Victor Blanco, Justo Puerto

摘要:

模糊优化处理的问题是确定最优化问题的“最优”解决方案时,出现在问题中的一些元素是不精确的。在实际情况下,在所考虑的系统中,通常有不准确的信息。这种不精确性可以在模糊环境中建模。Zadeh(1965)分析了允许真假值在0和1之间的逻辑系统,而不是经典的二元真假逻辑。在这个框架中,满足一定的条件意味着评估所涉及的元素与完全满意的程度。然后,“模糊集”中的每个元素都与[0,1]中的一个值相耦合,该值表示该集合的成员级别。在线性规划中,描述问题的部分或全部元素可以被认为是模糊的:目标函数,右侧向量或约束矩阵,最优性的概念(在可行解上排序),约束的满足程度等。在这里,我们提出了新的线性整数规划的一些元素被认为是模糊的,使用以前的结果短生成函数求解多目标整数规划的复杂性结果。

英文摘要:

Fuzzy optimization deals with the problem of determining 'optimal'solutions of an optimization problem when some of the elements that appear in the problem are not precise. In real situations it is usual to have information, in systems under consideration, that is not exact. Thisimprecision can be modeled in a fuzzy environment. Zadeh (1965) analyzed systems of logic that permit truth values between zero and one instead of the classical binary true-false logic. In this framework, satisfying a certain condition means to evaluate how close are the elements involved to the complete satisfaction. Then, each element in a 'fuzzy set' is coupled with a value in [0,1] that represents the membership level to the set. In linear programming some or all the elements that describe a problem may be considered fuzzy: objective function, right-hand side vector or constraint matrix, the notion of optimality (ordering over the feasible solutions), the level of satisfaction of the constraints, etc. Moreover, in integer programs, the integrality constraints may be seen as fuzzy constraints. Here, we present new complexity results about linear integer programming where some of its elements are considered fuzzy using previous results on short generating functions for solving multiobjective integer programs.

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