Prioritized Information Fusion Method for Triangular Fuzzy Information and Its Application to Multiple Attribute Decision Making

Abstract

This study investigates the multiple attribute decision making under triangular fuzzy environment in which the attributes and experts are in different priority level. By combining the idea of quasi arithmetic mean and prioritized weighted average (PWA) operator, we first propose two new prioritized aggregation operators called quasi fuzzy prioritized weighted average (QFPWA) operator and the quasi fuzzy prioritized weighted ordered weighted average (QFPWOWA) operator for aggregating triangular fuzzy information. The properties of the new aggregation operators are studied in detail and their special cases are examined. Furthermore, based on the QFPWA operator and QFPWOWA operator, an approach to deal with multiple attribute decision-making problems under triangular fuzzy environments is developed. Finally, a practical example is provided to illustrate the multiple attribute decision making process.