Reduction in the Number of Comparisons Required to Create Matrix of Expert Judgment in the Comet Method

Open access

Abstract

Multi-criteria decision-making (MCDM) methods are associated with the ranking of alternatives based on expert judgments made using a number of criteria. In the MCDM field, the distance-based approach is one popular method for receiving a final ranking. One of the newest MCDM method, which uses the distance-based approach, is the Characteristic Objects Method (COMET). In this method, the preferences of each alternative are obtained on the basis of the distance from the nearest characteristic ob jects and their values. For this purpose, the domain and fuzzy numbers set for all the considered criteria are determined. The characteristic objects are obtained as the combination of the crisp values of all the fuzzy numbers. The preference values of all the characteristic ob ject are determined based on the tournament method and the principle of indifference. Finally, the fuzzy model is constructed and is used to calculate preference values of the alternatives. In this way, a multi-criteria model is created and it is free of rank reversal phenomenon. In this approach, the matrix of expert judgment is necessary to create. For this purpose, an expert has to compare all the characteristic ob jects with each other. The number of necessary comparisons depends squarely to the number of ob jects. This study proposes the improvement of the COMET method by using the transitivity of pairwise comparisons. Three numerical examples are used to illustrate the efficiency of the proposed improvement with respect to results from the original approach. The proposed improvement reduces significantly the number of necessary comparisons to create the matrix of expert judgment.

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Management and Production Engineering Review

The Journal of Production Engineering Committee of Polish Academy of Sciences and Polish Association for Production Management

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CiteScore 2016: 0.48

SCImago Journal Rank (SJR) 2016: 0.126
Source Normalized Impact per Paper (SNIP) 2016: 0.551

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