运筹与管理 ›› 2016, Vol. 25 ›› Issue (2): 78-82.DOI: 10.12005/orms.2016.0047

• 理论分析与方法探讨 • 上一篇    下一篇

区间粗糙数多属性决策方法

赵焕焕1,菅利荣1,刘勇2   

  1. 1.南京航空航天大学 经济与管理学院,江苏 南京 211106;
    2.江南大学 商学院,江苏 无锡 214122
  • 收稿日期:2014-04-15 出版日期:2016-04-25
  • 通讯作者: 刘勇(1985-)男,河南平舆,博士,副教授,研究方向:软技算。
  • 作者简介:赵焕焕(1988-)女,博士生,研究方向:软计算
  • 基金资助:
    国家自然科学基金资助项目(71503103);中央高校基本科研业务费专项资金资助-江苏省研究生培养创新工程(KYZZ-0096);江苏省自然科学基金资助项目(BK20150157)

Multi-attribute Decision Making Methodology with Interval Rough Number

ZHAO Huan-huan1, JIAN Li-rong1, LIU Yong2   

  1. 1.College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2. School ofBusiness Jiangnan University, Wuxi 214122, China
  • Received:2014-04-15 Online:2016-04-25

摘要: 针对属性值为区间粗糙数,属性权重部分已知和属性权重未知两种情形的多属性决策问题,本文利用灰色关联分析的思想方法,构建了一种区间粗糙数多属性决策方法。本文首先利用区间粗糙数的运算法则和期望值比较,确定最优理想方案和最劣理想方案,并基于灰色关联度分析方法构建了属性权重部分已知、属性权重未知两种情形的多目标优化模型,从而确定属性权重和属性权重表达式,进而获得各方案的综合评价值和方案排序。最后以一个实例验证模型的有效性与适用性。

关键词: 多属性决策, 区间粗糙数, 期望值, 优化模型, 灰色关联分析

Abstract: With respect to the two kinds of decision making problems that the attributes influence and interact on each other, and the attribute values are interval rough number, while the attribute weights are partly unknown or unknown, the thought of grey incidence analysis methodology is exploited to construct a novel interval rough multi attribute decision making model. The methodology, to begin with, based on the operation rules and the comparison of the expected value of the interval rough number, the positive and negative ideal schemes are determined, and then the grey incidence analysis method is used to respectively construct the multi object optimization model of the attribute weights are partly unknown or unknown, so that the attribute weights and the expression of the attribute weights can respectively be determined, and then the comprehensive evaluation values and ranking of the schemes can be acquired. Finally, an example validates the feasibility and effectiveness of the novel model.

Key words: multi-attribute decision making, interval rough number, expected value, optimization model, grey incidence analysis

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