On interval‐valued fuzzy decision‐making using soft likelihood functions

Multicriteria decision‐making approaches have been studied very widely in recent years and are frequently used in many real‐life applications. To select the optimal alternative that satisfies the multicriteria, effective aggregation methods are crucial in the decision‐making process. The soft likelihood functions (SLF) developed by Yager is introduced as preliminaries of our research, which is a flexible aggregation approach of probabilistic evidence in the context of forensic crime investigations. Motivated by SLF, in this study, a novel aggregation method is proposed based on ordered weighted averaging operator under interval‐valued fuzzy environments. To improve the performance of the new aggregation method, the reliability is taken into account in the decision‐making process from two perspectives. The first is the reliability of assessment value, including certainty and compatibility, for discounting assessment information; the second is human reliability for redefining SLF. Some numerical examples are given to demonstrate the proposed decision approach. And the reliability‐based aggregation method is illustrated more reasonable than the one without considering reliability.