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.
Highlights
Multi-expert multi-criterion decision-making (MCDM) problems are complex decision-making (DM) problems with both qualitative and quantitative characteristics
linguistic ordered weighted geometric averaging operator (LOWGA) operator methods require that the information is a single linguistic term set; these methods cannot handle hesitant information
The proposed method considers incomplete information. Both the LOWGA operator and induced LOWGA operator methods only deal with complete information in the information aggregation procedure
Summary
Multi-expert multi-criterion decision-making (MCDM) problems are complex decision-making (DM) problems with both qualitative and quantitative characteristics. Used an interval-valued hesitant fuzzy set to propose a new multi-criterion weighting ranking method to solve complex group DM issues. Sci. 2020, 10, 4582 expert-provided information usually contains uncertain information To effectively resolve this issue, Rodriguez et al [17] introduced the hesitant fuzzy linguistic term set (HFLTS) to handle situations where experts are hesitating between several possible linguistic term sets for evaluating a variable, indicator, alternative, etc. The concept of ordered weight was first introduced by Yager [31], who used an ordered weighted averaging (OWA) operator to solve multi-expert MCDM problems. DM, this study proposes a more general approach: the integration of an induced linguistic ordered weighted geometric averaging (LOWGA) operator and HFLTS.
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