基于ITFN信息关联输入的改进群体MULTIMOORA决策方法

发布时间：2018-05-21 04:29

本文选题：直觉梯形模糊数 + ITFB平均算子　；参考：《系统工程理论与实践》2014年12期

【摘要】：提出基于直觉梯形模糊数(intuitionistic trapezoidal fuzzy number,ITFN)极小、极大期望值的序关系判别准则,并引入风险系数构建ITFN相对完善的带有决策者风险偏好的运算规则,在此基础上定义直觉梯形模糊Bonferroni(intuitionistic trapezoidal fuzzy Bonferroni,ITFB)平均算子,验证其相关性质.针对决策者之间、属性之间分别存在关联关系且权重均未知的多属性群决策问题,提出基于ITFN信息关联输入的改进群体MULTIMOORA决策方法.首先,构建直觉梯形模糊决策矩阵序列,予以标准化处理,并将其转化为极小期望决策矩阵序列;其次,综合利用基于熵权法和考虑决策者偏好关联的基于2-可加模糊测度与Choquet积分联合的主客观赋权法确定决策者权重及属性权重;最后,分别引入WITFB平均算子及ITFN的Hamming距离以改进传统MULTIMOORA决策方法,基于优势理论可对方案展开综合排序以确定最优方案.通过算例分析验证本文方法的可行性及有效性.
[Abstract]:Based on intuitionistic trapezoidal fuzzy number of intuitionistic trapezoidal fuzzy number ITFNs, an order relation criterion is proposed, and risk coefficient is introduced to construct a relatively perfect ITFN operation rule with decision makers' risk preference. On this basis, the intuitionistic trapezoidal fuzzy Bonferroni(intuitionistic trapezoidal fuzzy Bonferronike ITFB averaging operator is defined and its related properties are verified. An improved group MULTIMOORA decision making method based on ITFN information association input is proposed to solve the problem of multi-attribute group decision making in which there is correlation relationship between attributes and the weights are unknown. Firstly, the sequence of intuitionistic trapezoidal fuzzy decision matrix is constructed and standardized, and transformed into the sequence of minimal expectation decision matrix. The weight of decision maker and attribute weight are determined by the combination of 2-additive fuzzy measure and Choquet integral based on entropy weight method and decision maker preference correlation. Finally, The WITFB average operator and the Hamming distance of ITFN are introduced to improve the traditional MULTIMOORA decision method. Based on the superiority theory, the schemes can be sorted synthetically to determine the optimal scheme. The feasibility and effectiveness of this method are verified by an example.
【作者单位】：西南交通大学交通运输与物流学院;西南交通大学综合交通运输智能化国家地方联合工程实验室;
【基金】：国家自然科学基金(71371156,70971017) 西南交通大学优秀博士学位论文培育项目
【分类号】：C934;O225

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