A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI
Multi-criteria decision-making (MCDM) assists in making judgments on complex problems by evaluating several alternatives based on conflicting criteria. Several MCDM methods have been introduced. However, real-world problems often involve uncertain and ambiguous decision-maker inputs. Therefore, fuzz...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/17/3773 |
| _version_ | 1827728038533529600 |
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| author | Anas A. Makki Reda M. S. Abdulaal |
| author_facet | Anas A. Makki Reda M. S. Abdulaal |
| author_sort | Anas A. Makki |
| collection | DOAJ |
| description | Multi-criteria decision-making (MCDM) assists in making judgments on complex problems by evaluating several alternatives based on conflicting criteria. Several MCDM methods have been introduced. However, real-world problems often involve uncertain and ambiguous decision-maker inputs. Therefore, fuzzy MCDM methods have emerged to handle this problem using fuzzy logic. Most recently, the method based on the removal effects of criteria using the geometric mean (MEREC-G) and ranking the alternatives based on the trace to median index (RATMI) were introduced. However, to date, there is no fuzzy extension of the two novel methods. This study introduces a new hybrid fuzzy MCDM approach combining fuzzy MEREC-G and fuzzy RATMI. The fuzzy MEREC-G can accept linguistic input terms from multiple decision-makers and generates consistent fuzzy weights. The fuzzy RATMI can rank alternatives according to their fuzzy performance scores on each criterion. The study provides the algorithms of both fuzzy MEREC-G and fuzzy RATMI and demonstrates their application in adopted real-world problems. Correlation and scenario analyses were performed to check the new approach’s validity and sensitivity. The new approach demonstrates high accuracy and consistency and is sufficiently sensitive to changes in the criteria weights, yet not too sensitive to produce inconsistent rankings. |
| first_indexed | 2024-03-10T23:17:24Z |
| format | Article |
| id | doaj.art-6cd3ac0c6c55455782e3b74f1f2e640a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T23:17:24Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-6cd3ac0c6c55455782e3b74f1f2e640a2023-11-19T08:31:55ZengMDPI AGMathematics2227-73902023-09-011117377310.3390/math11173773A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMIAnas A. Makki0Reda M. S. Abdulaal1Department of Industrial Engineering, Faculty of Engineering—Rabigh, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Industrial Engineering, College of Applied Sciences, AlMaarefa University, Ad Diriyah 13713, Saudi ArabiaMulti-criteria decision-making (MCDM) assists in making judgments on complex problems by evaluating several alternatives based on conflicting criteria. Several MCDM methods have been introduced. However, real-world problems often involve uncertain and ambiguous decision-maker inputs. Therefore, fuzzy MCDM methods have emerged to handle this problem using fuzzy logic. Most recently, the method based on the removal effects of criteria using the geometric mean (MEREC-G) and ranking the alternatives based on the trace to median index (RATMI) were introduced. However, to date, there is no fuzzy extension of the two novel methods. This study introduces a new hybrid fuzzy MCDM approach combining fuzzy MEREC-G and fuzzy RATMI. The fuzzy MEREC-G can accept linguistic input terms from multiple decision-makers and generates consistent fuzzy weights. The fuzzy RATMI can rank alternatives according to their fuzzy performance scores on each criterion. The study provides the algorithms of both fuzzy MEREC-G and fuzzy RATMI and demonstrates their application in adopted real-world problems. Correlation and scenario analyses were performed to check the new approach’s validity and sensitivity. The new approach demonstrates high accuracy and consistency and is sufficiently sensitive to changes in the criteria weights, yet not too sensitive to produce inconsistent rankings.https://www.mdpi.com/2227-7390/11/17/3773fuzzy MEREC-Gfuzzy RATMIfuzzy logichybridMCDM |
| spellingShingle | Anas A. Makki Reda M. S. Abdulaal A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI Mathematics fuzzy MEREC-G fuzzy RATMI fuzzy logic hybrid MCDM |
| title | A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI |
| title_full | A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI |
| title_fullStr | A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI |
| title_full_unstemmed | A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI |
| title_short | A Hybrid MCDM Approach Based on Fuzzy MEREC-G and Fuzzy RATMI |
| title_sort | hybrid mcdm approach based on fuzzy merec g and fuzzy ratmi |
| topic | fuzzy MEREC-G fuzzy RATMI fuzzy logic hybrid MCDM |
| url | https://www.mdpi.com/2227-7390/11/17/3773 |
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