A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems

Abstract

Multi-expert multi-criterion decision-making problems are complicated decision issues. Different experts have different opinions with regard to multi-criterion decision-making problems, depending on their background and experience. These experts provide information that usually contains certain, uncertain, and incomplete information simultaneously. However, traditional computing methods only fully consider certain information, and ignore uncertain or incomplete information, which causes information distortion in assessment results. In order to effectively solve the above problems, this paper proposes a new flexible method for solving multi-expert multi-criterion decision-making problem. The primary purpose of the proposed method is to fully consider all information provided by experts to avoid information distortion of assessment results. Finally, an illustrative example of computer numerical control (CNC) machine tool selection is provided to prove the practicality of the proposed method. After comparing the results generated by proposed method with the results generated using linguistic ordered weighted geometric averaging operator (LOWGA) operator and induced LOWGA operator methods, the results indicate that the proposed method integrating an induced LOWGA operator and a hesitant fuzzy linguistic term set is more flexible and is able to reflect real-world situations.