东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (2): 298-301.DOI: -

• 论著 • 上一篇    下一篇

基于多维诱导分量的拓展IOWA算子及其应用

姚爽;郭亚军;易平涛;   

  1. 东北大学工商管理学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-02-15 发布日期:2013-06-22
  • 通讯作者: Yao, S.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(70472032)

Multi-variable induced ordered weighted averaging operator and its application

Yao, Shuang (1); Guo, Ya-Jun (1); Yi, Ping-Tao (1)   

  1. (1) School of Business Administration, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-02-15 Published:2013-06-22
  • Contact: Yao, S.
  • About author:-
  • Supported by:
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摘要： 受单一诱导分量不可排序的启发,提出了一种多维诱导分量的拓展IOWA(MIOWA)算子,它可以根据特定背景定义诱导分量的涵义,从不同侧面反映数据分量的若干特性.针对具有自主式特征的协商评价问题,定义了一个二维诱导分量的拓展IOWA算子,使得在多属性信息集结的过程中能够体现方案的相对优势和绝对优势;给出一个决定位置权向量的权数非独裁性协商规则下最大化orness水平的规划模型.最后给出一个算例说明了方法的可行性.

关键词: IOWA算子, 自主式评价, 协商评价, 协商规则, 相对优势诱导分量, 绝对优势诱导分量

Abstract: To rise above the "ties" in induced ordering with single variable taken into account, a multi-variable induced ordered weighted averaging (MIOWA) operator is proposed to define inducing variables under special conditions so as to reflect the traits of the values of the argument from different viewpoints. During the bargaining evaluation problem (BEP) characterized with autonomy, an extended two-variable induced ordered weighted averaging operator is defined so as to enable a scheme to embody both its relative and absolute predominance in the aggregation process of multi-attribute information. A programming model is thus developed to maximize a given orness level in accordance to the non-dictatorial negotiation rules of weights to determine the position weighting vector. A numerical example is given to illustrate the feasibility of the method.

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