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 d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8839775/ |
| _version_ | 1811212009575809024 |
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| author | Zhangjiao Wu Shitao Zhang Xiaodi Liu Jian Wu |
| author_facet | Zhangjiao Wu Shitao Zhang Xiaodi Liu Jian Wu |
| author_sort | Zhangjiao Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T05:21:34Z |
| format | Article |
| id | doaj.art-a9baccd57092438ca7bb03e5b0f9e307 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T05:21:34Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a9baccd57092438ca7bb03e5b0f9e3072022-12-22T03:46:26ZengIEEEIEEE Access2169-35362019-01-01713390013391310.1109/ACCESS.2019.29418218839775Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic InformationZhangjiao Wu0Shitao Zhang1https://orcid.org/0000-0002-6717-6962Xiaodi Liu2Jian Wu3https://orcid.org/0000-0002-7315-9229School of Mathematics and Physics Science and Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mathematics and Physics Science and Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mathematics and Physics Science and Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mathematics and Physics Science and Engineering, Anhui University of Technology, Ma’anshan, ChinaA 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.https://ieeexplore.ieee.org/document/8839775/Multi-attribute decision-making (MADM)probabilistic linguistic term sets (PLTSs)possibility degreeattribute weightBest-Worst Method (BWM)green evaluation |
| spellingShingle | Zhangjiao Wu Shitao Zhang Xiaodi Liu Jian Wu Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic Information IEEE Access Multi-attribute decision-making (MADM) probabilistic linguistic term sets (PLTSs) possibility degree attribute weight Best-Worst Method (BWM) green evaluation |
| title | Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic Information |
| title_full | Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic Information |
| title_fullStr | Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic Information |
| title_full_unstemmed | Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic Information |
| title_short | Best-Worst Multi-Attribute Decision Making Method Based on New Possibility Degree With Probabilistic Linguistic Information |
| title_sort | best worst multi attribute decision making method based on new possibility degree with probabilistic linguistic information |
| topic | Multi-attribute decision-making (MADM) probabilistic linguistic term sets (PLTSs) possibility degree attribute weight Best-Worst Method (BWM) green evaluation |
| url | https://ieeexplore.ieee.org/document/8839775/ |
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