Approaches To Multi-Stage Multi-Attribute Group Decision Making

Multi-stage multi-attribute group decision making (MS-MAGDM) as a familiar decision activity that usually occurs in our daily life, such as multi-stage investment decision making, medical diagnosis, personnel dynamic examination, military system efficiency dynamic evaluation, etc. The aim of this paper is to investigate MS-MAGDM problems in which both the weight information on a collection of predefined attributes and the decision information on a finite set of alternatives with respect to the attributes are collected at different stages. We first propose a Poisson distribution based method to determine the weight vector associated with a time-weighted averaging (TWA) operator. Furthermore, we use a hybrid weighted aggregation (HWA) operator to fuse all individual decision information into group opinions at different stages, and then utilize the TWA operator to aggregate the derived group opinions at different stages into the complex group ones so as to rank the given alternatives. After that, we further investigate MS-MAGDM problems where all decision information at different stages cannot be given in exact numerical values, but value ranges can be obtained. An approach based on the uncertain time-weighted averaging (UTWA) operator and the uncertain hybrid weighted aggregation (UHWA) operator is developed for solving MS-MAGDM problems under interval uncertainty. Finally, a practical example is provided to illustrate the developed approaches.