A Novel Multi-Attribute Decision Making Approach: Addressing the Complexity of Time Dependent and Interdependent Data

Abstract

An innovative multi-attribute decision making (MADM) approach is put forward in this paper, which is demonstrated to be more precise than other methods currently available, especially, when the evaluations given by the decision maker(s) involve the complexity of time dependent and interdependent data. As for dynamic MADM interval-valued intuitionistic fuzzy environments with the time and attribute weights completely unknown, the new approach achieves the precision improvement by overcoming the drawbacks, the traditional geometric average operator existing: these fail to consider the relationships of the integrated data as well as the influences of attribute weights and time preference factor. The new method is systematically defined in a seven step approach; steps within the approach consist of the definition of multi-period power-weighted geometric average operators and relative optimization models to compute attribute weights and time weights. A case analysis on the option of a superior company about venture capital funding is provided to demonstrate the method proposed; validation studies present a quantitative cross-comparison study and a sensitivity analysis of the new approach.