Time-based hesitant fuzzy information aggregation approach for decision-making problems

Hesitant fuzzy sets have been proposed as an extension of fuzzy sets to address situations in which decision makers exhibit variations in their alternatives' assessment values. However, in real-world problems, the decision-making process has to be accomplished under situations where these assessment values may also drastically change over time. In this paper, we propose a prioritized aggregation operator to combine a time sequence of hesitant fuzzy information, where the time-based hesitancy due to changing environment is mitigated. The proposed method is applied to the service selection problem in service-based systems, where software architects must select as a group the service that has the best combination of features based on their historical assessments. We claim that the time-based hesitant fuzzy information aggregation method addresses the hesitancy at intra- and interexpert levels obtaining more robust decisions.