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Interactive fuzzy programming for decentralized two-level linear fractional programming (DTLLFP) problems

机译:分散二级线性分数规划(DTLLFP)问题的交互式模糊规划

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This paper presents two new interactive fuzzy programming approaches for a decentralized two-level linear fractional programming (DTLLFP) problem with a single decision maker (DM_0) at the upper level and multiple DMs (DM_i, i = 1,..., k) at the lower level. In the first approach, DM_0 specifies the minimal satisfactory level for own objective without considering the satisfactory levels of own decision variables and decreases it in favour of objectives at the lower level. Whereas, in the second approach, DM_0 does not specify the minimal satisfactory level for own objective, but instead DM_0 transfers the degree of satisfaction for not only own objective but also the own decision variables to the lower level. In both our approaches, with the help of analytic hierarchy process (AHP) method [Saaty TL. The analytical hierarchy process. New York: McGraw-Hill, 1980], DM_0 assigns weights w_1, w_2,..., w_k to objectives at the lower level. The most important idea to be emphasized is that equivalence is established such that the satisfactory levels of all objectives are proportional to their own weights. To obtain an overall satisfactory balance between both levels, by updating the satisfactory degree of the DM_0 which is in the first approach or the tolerances of the DM_0's decision variables which is in the second approach, transformed main problems are constructed corresponding to DTLLFP. Maximizing the least degree of equivalent satisfaction among all DMs, they efficiently find a satisfactory or compromise solution from a Pareto optimal set for DTLLFP problem. If the DM_0 is not satisfied with this solution, a strongly efficient satisfactory solution can be reached by interacting with him or her. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed methods.
机译:本文提出了两种新的交互式模糊规划方法,用于分散式两级线性分数规划(DTLLFP)问题,该决策程序具有较高级别的单个决策者(DM_0)和多个DM(DM_i,i = 1,...,k)在较低的级别。在第一种方法中,DM_0为自己的目标指定了最低的满意水平,而没有考虑自己的决策变量的满意水平,并根据较低的目标降低了满意水平。而在第二种方法中,DM_0并未为自己的目标指定最低的满意水平,而是DM_0不仅将对自己目标的满意程度也将对自己决策变量的满意程度转移到了较低级别。在我们两种方法中,借助层次分析法(AHP)[Saaty TL。分析层次结构过程。纽约:McGraw-Hill,1980年],DM_0将权重w_1,w_2,...,w_k分配给较低级别​​的目标。要强调的最重要的想法是建立等效性,以使所有目标的满意水平与其自身权重成正比。为了获得两个级别之间的总体令人满意的平衡,通过更新第一种方法中的DM_0的满意程度或第二种方法中的DM_0决策变量的公差,可以构造对应于DTLLFP的变换后的主要问题。他们在所有DM中将同等满意度的最低程度最大化,从而有效地从DTLLFP问题的帕累托最优集中找到了令人满意的折衷解决方案。如果DM_0对此解决方案不满意,则可以通过与他或她进行交互来获得非常有效的令人满意的解决方案。提供了一个说明性的数值示例,以证明所提出方法的可行性和效率。

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