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| Sources | Basic specifications | Positives (P)/negatives (N) |
|
| [15] | Direct extension of Saaty’s AHP model with triangular fuzzy numbers | (P) The judgments of multiple experts may be modeled in the reciprocal matrix |
| Lootsma’s logarithmic least square model is used to derive fuzzy weights and fuzzy performance scores | (N) Evermore there is no solution to the linear equations |
| (N) The calculational demand is great, even for a low problem |
| (N) It permits only triangular FN to be applied |
|
| [16] | Direct extension of Saaty’s AHP model with trapezoidal fuzzy numbers | (P) It is simple to extend to the fuzzy term |
| Uses the geometric mean model to derive fuzzy weights and performance scores | (P) It guarantees an alone solution to the reciprocal comparison matrix |
| (N) The calculational demand is great |
|
| [33] | Modifies van Laarhoven and Pedrycz’s model | (P) The judgments of multiple experts may be modeled |
| Shows a more robust method to the normalization of the local priorities | (N) The calculational demand is great |
|
| [32] | Synthetical degree values | (P) The calculational demand is partly low |
| Layer simple sequencing | (P) It follows the steps of crisp AHP; it does not involve additional process |
| Composite total sequencing | (N) It permits only triangular FN to be applied |
|
| [32] | Creates fuzzy standards | (P) The calculational demand is not great |
| Illustrates performance numerals by membership functions |
(N) Entropy is applied when probability distribution is known. The model is based on both probability and possibility measures |
| Uses entropy concepts to calculate aggregate weights | |
|
| Proposed method | Uses interval approximation to defuzzification | (P) It permits all FN to be applied |
| (P) It retains uncertainty of fuzzy number in itself |
| Uses goal programming method to obtain weights | (P) It is done, mainly, with a software such as Lingo |
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