计算机工程与应用
計算機工程與應用
계산궤공정여응용
Computer Engineering and Applications
2015年
22期
134-140
,共7页
犹豫正态模糊元%多属性群决策%信息集成算子%计算机网络系统
猶豫正態模糊元%多屬性群決策%信息集成算子%計算機網絡繫統
유예정태모호원%다속성군결책%신식집성산자%계산궤망락계통
hesitant normal fuzzy elements%multi-attribute group decision making%information aggregation operator%com-puter network systems
定义了犹豫正态模糊元及其运算法则、得分函数、Euclidean距离等概念；提出了广义犹豫正态模糊有序加权平均算子，并研究其性质，该算子不仅尽可能多地保留决策者的偏好信息，还可依据决策者的主观意愿选择不同的参数和属性权重，使得决策结果达到决策者的期望值；紧接着对属性权重和算子参数赋予不同的数值，获取广义犹豫正态模糊有序加权平均算子的若干种特殊算子，并探讨两个常用算子的大小关系；针对属性权重完全未知的多属性群决策问题，构建一种基于广义犹豫正态模糊有序加权平均算子的群决策方法。该方法利用同一属性下所有方案属性值间的距离求得最优权重，然后将同一方案下各属性值集结成为综合属性值，进而得到方案优劣排序。通过实例分析说明该方法的可行性和有效性。
定義瞭猶豫正態模糊元及其運算法則、得分函數、Euclidean距離等概唸；提齣瞭廣義猶豫正態模糊有序加權平均算子，併研究其性質，該算子不僅儘可能多地保留決策者的偏好信息，還可依據決策者的主觀意願選擇不同的參數和屬性權重，使得決策結果達到決策者的期望值；緊接著對屬性權重和算子參數賦予不同的數值，穫取廣義猶豫正態模糊有序加權平均算子的若榦種特殊算子，併探討兩箇常用算子的大小關繫；針對屬性權重完全未知的多屬性群決策問題，構建一種基于廣義猶豫正態模糊有序加權平均算子的群決策方法。該方法利用同一屬性下所有方案屬性值間的距離求得最優權重，然後將同一方案下各屬性值集結成為綜閤屬性值，進而得到方案優劣排序。通過實例分析說明該方法的可行性和有效性。
정의료유예정태모호원급기운산법칙、득분함수、Euclidean거리등개념；제출료엄의유예정태모호유서가권평균산자，병연구기성질，해산자불부진가능다지보류결책자적편호신식，환가의거결책자적주관의원선택불동적삼수화속성권중，사득결책결과체도결책자적기망치；긴접착대속성권중화산자삼수부여불동적수치，획취엄의유예정태모호유서가권평균산자적약간충특수산자，병탐토량개상용산자적대소관계；침대속성권중완전미지적다속성군결책문제，구건일충기우엄의유예정태모호유서가권평균산자적군결책방법。해방법이용동일속성하소유방안속성치간적거리구득최우권중，연후장동일방안하각속성치집결성위종합속성치，진이득도방안우렬배서。통과실례분석설명해방법적가행성화유효성。
Hesitant normal fuzzy elements(HNFEs)as well as their operational laws, score functions and Euclidean distance are defined. Then, the generalized hesitant normal fuzzy ordered weighted averaging(GHNFOWA)operator is proposed and some desirable properties of the GHNFOWA operator are studied. The GHNFOWA operator not only preserves the decision maker’s preference information as much as possible, but also the values of the parameter and attribute weights can changes on the base on decision makers’attitude to make the results fix the expected values of decision makers. Furthermore, some special cases of the GHNFOWA operator are given when the weight vector and operator parameter takes different values, and the relationship between two common operators is studied. Finally, for multi-attribute decision making problems with the information of attribute weights is completely unknown, a method based on the GHNFOWA operator is investigated. The optimal weights are calculated by the distances of each alternative under an attribute, and then aggregate all the attribute values into the overall attribute values, which is followed by the ranking of the alternative. An example is given to demonstrate the developed method is practicality and effectiveness.
