Abstract
AbstractThe 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 variabl...
Highlights
Information aggregation operators play an important role in multi-criteria decision making (MCDM)
In this paper, based on the weighted geometric averaging (WGA) operator and the ordered weighted geometric (OWG) operator, we shall develop some new geometric aggregation operators for aggregating arguments which take the form of log-normally distributed random variables, and apply them to solve multi-criteria group decision making (MCGDM) problems in which the criterion values are in the form of log-normally distributed random variables and the criterion weight information is known completely
In what follows, based on Definition 1, we propose some new geometric aggregation operator for aggregating log-normal distribution information, such as the log-normal distribution weighted geometric (LNDWG) operator and the log-normal distribution ordered weighted geometric (LNDOWG) operator
Summary
Information aggregation operators play an important role in multi-criteria decision making (MCDM). In this paper, based on the WGA operator and the OWG operator, we shall develop some new geometric aggregation operators for aggregating arguments which take the form of log-normally distributed random variables, and apply them to solve multi-criteria group decision making (MCGDM) problems in which the criterion values are in the form of log-normally distributed random variables and the criterion weight information is known completely.
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