An Approach to Multiple Attribute Decision Making with Triangular Linguistic Information
In this paper, we investigate the multiple attribute decision making problems with triangular linguistic information. Motivated by the ideal of Bonferroni mean, we develop the aggregation techniques called the triangular linguistic Bonferroni mean (TLBM) operator for aggregating the triangular linguistic information. We study its properties and discuss its special cases. For the situations where the input arguments have different importance, we then define the triangular linguistic weighted Bonferroni mean (TLWBM) operator, based on which we develop the procedure for multiple attribute decision making under the triangular linguistic environments. Finally, a practical example for evaluating the engineer project risk is given to verify the developed approach and to demonstrate its practicality and effectiveness.