Consensus Modelling on Interval-Valued Fuzzy Preference Relations with Normal Distribution

Consensus Modelling on Interval-Valued Fuzzy Preference Relations with Normal Distribution
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正态分布区间值模糊偏好关系的一致性建模

DOI:
10.2991/ijcis.11.1.54
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发表时间:
2018
影响因子:
2.9
通讯作者:
Ning Zhang
Ning Zhang
中科院分区:
计算机科学4区
文献类型:
--
作者:
Lihong Wang;Zaiwu Gong;Ning Zhang

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研究了具有分布特征的区间值模糊偏好关系的一致性决策问题。建议的群体一致性决策模型的构建,考虑的情况下,DM分别是平等的和不平等的加权和DM的偏好是随机分布的。目标是找到理想DM和所有单个DM之间的最小偏差。因此,目标函数是具有一定概率的最大共识。交互过程更有效地模拟了决策者的不确定性判断信息。采用遗传算法和蒙特卡罗方法得到的Pareto最优解更接近实际。在本研究的模型求解过程中,蒙特卡洛模拟方法的本质是一个涉及决策信息的交互过程。因此,本研究为交互式决策支持系统的框架和优化算法提供了参考。
This paper investigates the consensus decision making problem of the interval-valued fuzzy preference relation with distribution characteristics. The proposed group consensus decision making model is constructed by considering the scenarios in which the DMs are respectively equally and non-equally weighted and the DM’s preferences are randomly distributed. The goal is to find the minimum deviation between an ideal DM and all individual DMs. Accordingly, the objective function is the maximum consensus with a certain probability. The interactive process simulates the DM’s uncertainty judgment information more effectively. The Pareto optimization solution derived using a genetic algorithm and Monte Carlo approach is closer to reality. In the process of solving the model in this study, the essence of the Monte Carlo simulation method is an interactive process involving decision information. Therefore, this study provides a reference for the framework and optimization algorithm of the interactive decision support system.
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