Author(s): Moussa Larbani, ChiYo Huang
Fuzzy measure and Choquet integral are effective tools for handling complex multiple criteria decision making (MCDM) problems in which criteria are in- ter-dependent. The identification of a fuzzy measure requires the determination of 22 n − values when the number of criteria is n . The complexity of this problem increases exponentially, which makes it practically very difficult to solve. Many methods have been proposed to reduce the number of values to be determined including the introduction of new special fuzzy measures like the λ -fuzzy measures. However, manipulations of the proposed methods are difficult from the aspects of high data complexity as well as low computation efficiency. Thus, this paper pro- posed a novel fuzzy measure identification method by reducing the data complexity to (1)/2 nn − and en- hancing the computation efficiency by leveraging a relatively small number of variables and constraints for linear programming. The proposed method was developed based on the evaluation of pair-wise addi- tivity degrees or interdependence coefficients be- tween the criteria. Depending on the information be- ing provided by decision-makers on the individual density of each criterion, the fuzzy measure can be constructed by solving a si mple system of linear ine- qualities or a linear programming problem. This novel method is validated through a supplier selec- tion problem which occurs frequently in real-world decision-making problems. Validation results dem- onstrate that the newly-proposed method can model real-world MCDM problems successfully.