TY - GEN
T1 - The integrated method to determine weights based on the divergence of attributes
AU - Si, Yanjie
AU - Wei, Fajie
PY - 2008
Y1 - 2008
N2 - The standard deviation or the entropy measure is usually used for quantifying divergence degree and deriving objective weights of attributes. Considering the conventional entropy weight method may create great gap among different weights, this paper proposes the improved entropy weight method to decrease the unreasonable gap. Moreover, in order to improve the sensitivity of weights, the standard deviation method based on the reciprocal judgment matrix is proposed, and the weights derived from the two methods are integrated according to the least square principle. In the end, a numerical example is used to illustrate the stability and applicability of the proposed method.
AB - The standard deviation or the entropy measure is usually used for quantifying divergence degree and deriving objective weights of attributes. Considering the conventional entropy weight method may create great gap among different weights, this paper proposes the improved entropy weight method to decrease the unreasonable gap. Moreover, in order to improve the sensitivity of weights, the standard deviation method based on the reciprocal judgment matrix is proposed, and the weights derived from the two methods are integrated according to the least square principle. In the end, a numerical example is used to illustrate the stability and applicability of the proposed method.
KW - Entropy weight method
KW - Multiple attribute decision making
KW - Objective weight
KW - The standard deviation method
UR - https://www.scopus.com/pages/publications/84888262650
M3 - 会议稿件
AN - SCOPUS:84888262650
SN - 9781627486828
T3 - 38th International Conference on Computers and Industrial Engineering 2008
SP - 671
EP - 678
BT - 38th International Conference on Computers and Industrial Engineering 2008
T2 - 38th International Conference on Computers and Industrial Engineering 2008
Y2 - 31 October 2008 through 2 November 2008
ER -