FUZZY-MEREC (fuzzy MEthod based on the Removal Effects of Criteria)

Saidin, M. S., Lee, L. S., Marjugi, S. M., Ahmad, M. Z., Seow, H.-V. · 2023

Method assistant

Grounded explanations: it explains the method, it does not compute.

Common pitfalls

•
Every GMIR-defuzzified value must stay below 1, otherwise ln(1 - r_ij) is undefined; a benefit criterion whose largest upper endpoint is attained with l = m = u would breach this and the run fails closed.
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A cost criterion needs strictly positive lower endpoints because Eq. (11) divides by all three components; benefit criteria may legitimately contain zero endpoints such as the Table 1 terms (0,0,1) and (0,1,2).
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The matrix must already be aggregated over experts. Eqs. (8) and (9) are not part of this manifest, so an expert cube is not accepted.
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Weights are computed on the criteria actually supplied. Feeding a subset of the criteria changes every weight, because Eq. (16) renormalises over that subset.

How to cite

Saidin, M. S.; Lee, L. S.; Marjugi, S. M.; Ahmad, M. Z.; Seow, H.-V. (2023). Fuzzy Method Based on the Removal Effects of Criteria (MEREC) for Determining Objective Weights in Multi-Criteria Decision-Making Problems. Mathematics. https://doi.org/10.3390/math11061544

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Common pitfalls

•

Every GMIR-defuzzified value must stay below 1, otherwise ln(1 - r_ij) is undefined; a benefit criterion whose largest upper endpoint is attained with l = m = u would breach this and the run fails closed.

•

A cost criterion needs strictly positive lower endpoints because Eq. (11) divides by all three components; benefit criteria may legitimately contain zero endpoints such as the Table 1 terms (0,0,1) and (0,1,2).

•

The matrix must already be aggregated over experts. Eqs. (8) and (9) are not part of this manifest, so an expert cube is not accepted.

•

Weights are computed on the criteria actually supplied. Feeding a subset of the criteria changes every weight, because Eq. (16) renormalises over that subset.