A METHOD BASED ON THE DYNAMIC WEIGHTED GEOMETRIC AGGREGATION OPERATOR FOR DYNAMIC HYBRID MULTI-ATTRIBUTE GROUP DECISION MAKING

The paper investigates the dynamic hybrid multi-attribute group decision making problems, in which the decision information, provided by multiple decision makers at different periods, is expressed in real numbers, interval numbers or linguistic labels (linguistic labels can be described by triangular fuzzy numbers), respectively. We define the concepts of argument variable and dynamic weighted geometric aggregation (DWGA) operator, etc., and give an approach to determining the weights of periods based on the basic unit-interval monotonic (BUM) function, and then propose a dynamic hybrid multi-attribute group decision making method based on the hybrid geometric aggregation (HGA) operator and the DWGA operator. The method first utilizes three different TOPSISs (real-valued TOPSIS, interval-valued TOPSIS and fuzzy-valued TOPSIS) to calculate the individual closeness coefficient of each alternative to the positive and negative ideal alternatives based on the decision information expressed in real numbers, interval numbers and linguistic labels, respectively, provided by each decision maker at each period, and then employs the HGA operator to aggregate all individual closeness coefficients into the collective closeness coefficient corresponding to each alternative at each period. After doing so, the method uses the DWGA operator to fuse the collective closeness coefficients at different periods into the overall closeness coefficient corresponding to each alternative. These overall closeness coefficients are then used to rank and select the given alternatives. We can also reduce the above method to solve the dynamic multi-attribute group decision making problems, in which the decision information, provided by multiple decision makers at different periods, is expressed by means of values from the same type, either real numbers, or interval numbers or linguistic labels. Finally, the developed method is applied to multi-period investment decision making.