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Genetic algorithm based hybrid approach to solve fuzzy multi-objective assignment problem using exponential membership function

机译:基于遗传算法的混合混合方法用指数隶属函数求解模糊多目标分配问题

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摘要

This paper presents a genetic algorithm based hybrid approach for solving a fuzzy multi-objective assignment problem (FMOAP) by using an exponential membership function in which the coefficient of the objective function is described by a triangular possibility distribution. Moreover, in this study, fuzzy judgment was classified using α-level sets for the decision maker (DM) to simultaneously optimize the optimistic, most likely, and pessimistic scenarios of fuzzy objective functions. To demonstrate the effectiveness of the proposed approach, a numerical example is provided with a data set from a realistic situation. This paper concludes that the developed hybrid approach can manage FMOAP efficiently and effectively with an effective output to enable the DM to take a decision.
机译:本文提出了一种基于遗传算法的混合方法,该方法使用指数隶属函数来解决模糊多目标分配问题(FMOAP),其中目标函数的系数由三角形可能性分布描述。此外,在这项研究中,使用α级集对决策者(DM)进行模糊判断,以同时优化模糊目标函数的乐观,最可能和悲观的情况。为了证明所提出方法的有效性,提供了一个数值示例,并提供了一个来自实际情况的数据集。本文的结论是,开发的混合方法可以有效地管理FMOAP,并具有有效的输出,从而使DM能够做出决策。

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