A method to dynamic stochastic multicriteria decision making with log-normally distributed random variables.
We investigate the dynamic stochastic multicriteria decision making (SMCDM) problems, in which the criterion values take the form of log-normally distributed random variables, and the argument information is collected from different periods. We propose two new geometric aggregation operators, such as the log-normal distribution weighted geometric (LNDWG) operator and the dynamic log-normal distribution weighted geometric (DLNDWG) operator, and develop a method for dynamic SMCDM with log-normally distributed random variables. This method uses the DLNDWG operator and the LNDWG operator to aggregate the log-normally distributed criterion values, utilizes the entropy model of Shannon to generate the time weight vector, and utilizes the expectation values and variances of log-normal distributions to rank the alternatives and select the best one. Finally, an example is given to illustrate the feasibility and effectiveness of this developed method.
School of Science, Hunan University of Technology, Zhuzhou 412007, China ; School of Business, Central South University, Changsha 410083, China.
Xin-Fan
XF
Wang
Jian-Qiang
JQ
Wang
Sheng-Yue
SY
Deng
D003657:Decision Making; D006801:Humans; D008954:Models, Biological; D011336:Probability; D013269:Stochastic Processes