|Table of Contents|

[1] Xu Zeshui, Da Qingli,. Hybrid aggregation operator and its application to multipleattribute decision making problems [J]. Journal of Southeast University (English Edition), 2003, 19 (2): 174-177. [doi:10.3969/j.issn.1003-7985.2003.02.016]
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Hybrid aggregation operator and its application to multipleattribute decision making problems()
一种混合集结算子及其在多属性决策中的应用
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
19
Issue:
2003 2
Page:
174-177
Research Field:
Economy and Management
Publishing date:
2003-06-30

Info

Title:
Hybrid aggregation operator and its application to multipleattribute decision making problems
一种混合集结算子及其在多属性决策中的应用
Author(s):
Xu Zeshui Da Qingli
College of Economics and Management, Southeast University, Nanjing 210096, China
徐泽水 达庆利
东南大学经济管理学院, 南京 210096
Keywords:
multiple attribute decision making aggregation operator
多属性决策 集结 算子
PACS:
C934
DOI:
10.3969/j.issn.1003-7985.2003.02.016
Abstract:
By combining the advantages of the additive weighted mean(AWM)operator and the ordered weighted averaging(OWA)operator, this paper first presents a hybrid operator for aggregating data information, and then proposes a hybrid aggregation(HA)operator-based method for multiple attribute decision making(MADM)problems. The theoretical analyses and the numerical results show that the HA operator generalizes both the AWM and OWA operators, and reflects the importance of both the given argument and the ordered position of the argument. Thus, the HA operator can reflect better real situations in practical applications. Finally, an illustrative example is given.
结合加性加权平均(AWM)算子和有序加权平均(OWA)算子的特点, 提出了一种集结决策信息的混合集结(HA)算子, 并提出了一种基于混合集结(HA)算子的多属性决策方法. 理论分析和数值结果表明: 混合集结(HA)算子同时推广了加性加权平均(AWM)算子和有序加权平均(OWA)算子, 它不仅能反映所给数据自身的重要性程度, 而且还体现了数据所在位置的重要性程度. 因此, 混合集结(HA)算子在实际应用中能更好地反映现实情况. 最后进行了实例分析.

References:

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Memo

Memo:
Biographies: Xu Zeshui(1968—), male, doctor, associate professor; Da Qingli(corresponding author), male, professor, dql@publice.ptt.jas.cn.
Last Update: 2003-06-20