Abstract
A Best-Worst multi-attribute decision-making (MADM) method based on a new possibility degree is put forward to deal with MADM problems with probabilistic linguistic evaluation information. Firstly, a new possibility degree for pairwise comparisons with probabilistic linguistic term sets (PLTSs) is defined. Secondly, starting from the new possibility degree, two different ideas of Best-Worst Method (BWM) for getting the optimal attribute weights are put forward. Thirdly, combining the new probabilistic linguistic possibility degree and the two BWM ideas, two optimization models for determining the attribute weights are constructed, respectively. Moreover, consistency ratios for two new BWM models are proposed to check the reliability of the pairwise comparisons. Meanwhile, the state of optimal solutions for the new BWM models is analyzed. Finally, a new Best-Worst MADM method under probabilistic linguistic information is presented, which is applied to a practical example of selecting optimal green enterprises. Some comparative analyses are given to show the rationality and validity of the proposed method.
Highlights
Multi-attribute decision-making (MADM) refers to the sorting and selecting of finite alternatives with multiple attributes
This paper aims to propose a MADM method based on new possibility degree to solve MADM with probabilistic linguistic information
(b) The attribute weight determining models based on two Best-Worst Method (BWM) ideas provide a simple and effective way to fully exploit the attribute weight information under the probabilistic linguistic decision-making environment
Summary
Multi-attribute decision-making (MADM) refers to the sorting and selecting of finite alternatives with multiple attributes. HFLTS-based multi-attribute decision making has received extensive attention in recent years, which is largely due to better representing the qualitative information of DMs [2] It can describe fuzzy information and hesitancy of linguistic information presented by DMs at the same time. Z. Wu et al.: Best-Worst MADM Method Based on New Possibility Degree With Probabilistic Linguistic Information satisfaction evaluation of 100 customers can be expressed by the following PLTS: {slightly good (0.3), good (0.3), very good (0.4)}. In terms of determining the weights of attributes with PLTSs, the new attribute weight determining methods have been presented by integrating the classical ones into probabilistic linguistic decision-making environments. This paper aims to propose a MADM method based on new possibility degree to solve MADM with probabilistic linguistic information.
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