A Case-Based Reasoning Decision-Making Model for Hesitant Fuzzy Linguistic Information

In some complicated decision-making problems, because of time pressure or the lack of necessary information, decision makers (DMs) infrequently select optimal alternatives, but acquire satisfactory alternatives that can be obtained by analyzing the correlation between the decision problems and past similar cases. Case-based reasoning (CBR) is an effective approach to obtain preferential information for DMs from past successful decision cases. Using the CBR approach, we aim to process hesitant fuzzy linguistic information, and classify and rank the alternatives according to past successful decision cases. We first sum the distance measures for hesitant fuzzy linguistic term sets (HFLTSs) and then propose a new axiomatic definition for HFLTSs, which are compared with existing distance measures from relationships and properties. Furthermore, based on our proposed distance measure, we propose a CBR decision model for hesitant fuzzy linguistic information to calculate the weights of criteria and classifying thresholds. We then classify and rank the alternatives according to the most satisfactory solution in past successful decision cases. Finally, we consider an example to demonstrate the effectiveness and advantages of our proposed method.