Some geometric aggregation operators based on log-normally distributed random variables

DOI

10.1080/18756891.2014.966998How to use a DOI?

Keywords

Multi-criteria decision making, log-normal distribution, information fusion, LNDWG operator, LNDOWG operator, LNDHG operator

Abstract

The weighted geometric averaging (WGA) operator and the ordered weighted geometric (OWG) operator are two of most basic operators for aggregating information. But these two operators can only be used in situations where the given arguments are exact numerical values. In this paper, we first propose some new geometric aggregation operators, such as the log-normal distribution weighted geometric (LNDWG) operator, log-normal distribution ordered weighted geometric (LNDOWG) operator and log-normal distribution hybrid geometric (LNDHG) operator, which extend the WGA operator and the OWG operator to accommodate the stochastic uncertain environment in which the given arguments are log-normally distributed random variables, and establish various properties of these operators. Then, we apply the LNDWG operator and the LNDHG operator to develop an approach for solving multi-criteria group decision making (MCGDM) problems, in which the criterion values take the form of log-normally distributed random variables and the criterion weight information is known completely. Finally, an example is given to illustrate the feasibility and effectiveness of the developed method.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xin-Fan Wang
AU  - Jian-Qiang Wang
AU  - Sheng-Yue Deng
PY  - 2014
DA  - 2014/12/01
TI  - Some geometric aggregation operators based on log-normally distributed random variables
JO  - International Journal of Computational Intelligence Systems
SP  - 1096
EP  - 1108
VL  - 7
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.966998
DO  - 10.1080/18756891.2014.966998
ID  - Wang2014
ER  -