• 人工智能及识别技术 •

### 基于离差最大化的IITFN多属性决策模型

1. (1. 安徽大学数学科学学院，合肥 230601；2. 安徽大学计算智能与信号处理教育部重点实验室，合肥 230039)
• 收稿日期:2012-04-09 修回日期:2012-05-29 出版日期:2013-02-15 发布日期:2013-02-13
• 作者简介:付亚男(1988－)，女，硕士研究生，主研方向：多属性决策，智能计算；毛军军，副教授、博士；徐丹青，硕士研究生
• 基金资助:
国家自然科学基金资助项目(61073117)；安徽省高等学校省级自然科学研究计划基金资助项目；安徽大学学术创新团队计划基金资助项目(KJTD001B)；安徽高等学校优秀青年人才基金资助项目(2011SQRL186)；安徽大学研究生学术创新基金资助项目(yfc100017, yfc100018)

### IITFN Multi-attribute Decision Making Model Based on Maximizing Deviation

FU Ya-nan 1, MAO Jun-jun 1,2, XU Dan-qing 1

1. (1. School of Mathematical Sciences, Anhui University, Hefei 230601, China; 2. Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China)
• Received:2012-04-09 Revised:2012-05-29 Online:2013-02-15 Published:2013-02-13

Abstract: For the problem of multi-attribute decision making, in which the attribute values are the Interval Intuition Trapezoidal Fuzzy Number(IITFN) and the weights are intervals, an IITFN multi-attribute decision model is presented based on the maximizing deviation. It calculates the distance between attribute and ideal solution, then the deviation between attribute and ideal solution of each scheme are defined. The optimization model of multi-attribute decision making is constructed based on the maximum deviation, by using Lingo means the weights of attribute is given. By using the weighted arithmetic average operator of the IITFN, the total deviation of each scheme is provided, and then scheme is ranked based on the total deviation. Example results show that this model needs few operation steps, and is easy to realize.