| 报告题目 | Consistency and consensus improving for pairwise comparison matrices based on Abelian linearly ordered group | ||
| 报告人（单位） | 夏梅梅（清华大学） | ||
| 点评人（单位） | 张玉林，刘新旺，李敏 | 点评人（单位） | 舒嘉 |
| 时间地点 | 时间： 2013年5月15日（周三）下午2点 地点：九龙湖经管楼B-202 | ||
| 报告内容摘要 | | ||
| 报告内容： Grasp the rule, half effort and double results. The pairwise comparison (PC) matrix is to determine the priority of a set of alternatives or attributes in decision making. To avoid a misleading solution, the consistency and consensus of the PC matrix should be measured or revised according to the specific situations. However, the basic elements of the PC matrix can be assigned different forms, such as the preference ratio or the preference indifference or the distance from the indifference. Different consistency and consensus methods should be developed for different kinds of matrices. To give a general framework, we first introduce the PC matrices over the Abelian linearly ordered group, give the consistency index by constructing the nearest consistent PC matrix from an inconsistent one, and then propose two consistency improving methods. By introducing a more general aggregation operator based on Abelian linearly ordered group, we provide the consensus index and two consensus improving methods. The proposed methods are proved to be convergence, and can provide a general framework for the consistency and consensus methods for PC matrices. 报告人简介： 夏梅梅， 2006年本科毕业于曲阜师范大学，2012年博士毕业于东南大学，管理科学与工程系，现为清华大学经济管理学院管理科学与工程系博士后，目前研究方向为行为决策。已在IEEE Transactions on Fuzzy Systems, Fuzzy sets and Systems, Information Sciences, Information Fusion 等杂志发表多篇论文。目前主持一项教育部人文社会科学研究青年基金项目，一项中国博士后科学基金面上资助项目。 | |||
