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Interactive Fuzzy Dual Decomposition Approach to Large-Scale Multi-Level Multi-Objective Programming Problems

机译:大规模多层次多目标规划问题的交互式模糊对偶分解方法

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In this paper, we focus on multi-level multiobjective programming problems with block angular structures where multiple decision makers in a hierarchical organization have their own multiple objective functions, and propose an interactive algorithm based on the dual decomposition method to obtain the satisfactory solution which reflects not only the hierarchical relationships between multiple decision makers but also their own preferences for their objective functions. In the proposed algorithm, assuming that each of the decision makers has fuzzy goals for his/her objective functions, corresponding membership functions are elicited from the decision makers in their subjective manner. In order to deal with the fuzzy multi-level multiobjective programming problem, a new kind of Pareto optimality concept in membership spaces is defined and the concept of decision powers of multiple decision makers in a hierarchical decision structure are introduced. After each of the decision makers specifies his/her decision power and his/her reference membership values, the minimax problem is solved efficiently on the basis of the dual decomposition method, and the corresponding candidate of the satisfactory solution is obtained. If at least one of the decision makers is not satisfied with the current values of the membership functions, he/she updates his/her reference membership values and/or his/her decision power.
机译:本文针对具有块角结构的多级多目标规划问题,其中分层组织中的多个决策者具有自己的多个目标函数,并提出了一种基于对偶分解方法的交互式算法,以获得令人满意的解决方案不仅是多个决策者之间的等级关系,还包括他们对目标功能的偏好。在提出的算法中,假设每个决策者的目标函数都有模糊的目标,则以决策者的主观方式得出相应的隶属函数。为了解决模糊的多级多目标规划问题,定义了一种新的隶属空间中的帕累托最优概念,并引入了层次决策结构中多个决策者的决策权概念。在每个决策者指定了他/她的决策权和他/她的参考隶属度值之后,基于对偶分解方法有效地解决了极小极大问题,并获得了满意解的对应候选者。如果至少一个决策者对成员函数的当前值不满意,则他/她更新他/她的参考成员值和/或他/她的决策能力。

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    《 》|2006年|3097-3102|共6页
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    Yano; Hitoshi;

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