Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic Information

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.