Generalized logarithmic proportional averaging operators and their applications to group decision making

  • Authors:

  • Ligang Zhou;Huayou Chen;Jinpei Liu

  • Affiliations:

  • School of Mathematical Sciences, Anhui University, Hefei, Anhui 230601, China;School of Mathematical Sciences, Anhui University, Hefei, Anhui 230601, China;School of Business, Anhui University, Hefei, Anhui 230601, China

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we present a new operator called the generalized ordered weighted logarithmic proportional averaging (GOWLPA) operator based on an optimal model, which is an extension of the generalized ordered weighted logarithm averaging (GOWLA) operator. The key advantage of the GOWLPA operator is not only that it is an aggregation operator with theoretic basis on aggregation, but also that the weighting vector of the GOWLPA operator depends on the input arguments. We analyze some properties and families of the GOWLPA operator and further develop generalizations of this operator including the generalized hybrid logarithmic proportional averaging (GHLPA) operator and the quasi ordered weighted logarithmic proportional averaging (QOWLPA) operator. To determine the GOWLPA operator weights, we propose the generalized logarithm chi-square method (GLCSM) which does not follow a regular distribution. Finally, we give a numerical example of an investment selection to illustrate the application of the GOWLPA operator to multiple attribute group decision making.