A new probabilistic hesitant fuzzy multi-attribute group decision making method based on improved distance measures
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NETL
NSTL
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针对概率犹豫模糊环境下属性权重已知的多属性群决策问题,考虑概率犹豫模糊集的犹豫度,提出基于改进距离测度的概率犹豫模糊多属性群决策新方法.结合传统的概率犹豫模糊距离测度,通过信息融合定义融入犹豫度的概率犹豫模糊距离测度:汉明距离、欧氏距离、广义欧氏距离,并通过组合系数来实现理论扩张和融合优化,同时研究距离测度的大小关系及参数单调性.将融入犹豫度的距离测度与基于理想解相似度的逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)结合构建多属性群决策新方法,采用公司选址案例进行决策选择,通过参数分析和决策比较来揭示所建方法的有效性.相关研究系统深化概率犹豫模糊距离测度,并丰富了多属性群决策方法.
Aiming at the multi-attribute group decision making problem with known attribute weights under probabilistic hesitant fuzzy environments,hesitation degrees of probabilistic hesitant fuzzy sets are considered,and thus a new method of probabilistic hesitant fuzzy multi-attribute group decision making is proposed based on improved distance measures.Firstly,combining the tradi-tional probabilistic hesitant fuzzy distance measures,improved probabilistic hesitant fuzzy distance measures with hesitation degrees are defined through information fusion,including the Hamming distance,Euclidean distance,and generalized Euclidean distance.These new measures depend on combination coefficients to achieve the theoretical expansion and fusion optimization,and size rela-tionships and parameter monotonicity of distance measures are studied.Secondly,according to the improved distance measures,a new method of multi-attribute group decision making is constructed by using the technique for order preference by similarity to ideal solution(TOPSIS)method,and an example of company location is used to make decision selection.The effectiveness of the pro-posed method is revealed by parameter analysis and decision comparison.Related researches systematically deepen probabilistic hesi-tant fuzzy distance measures,and effectively enrich multi-attribute group decision-making methods.
multi-attribute group decision makingprobabilistic hesitant fuzzy sethesitation degreeinformation fusiondistance measure
NETL
NSTL
万方数据
