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Weight_Objective
FCILOS (Fuzzy Criterion Impact LOSs)
Podvezko, V., Zavadskas, E. K., Podviezko, A. · 2020
Method assistant
Grounded explanations: it explains the method, it does not compute.
Common pitfalls
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The point solutions of Eqs. (15) to (20) are three independent crisp weight vectors, not the (l, m, u) components of one fuzzy weight; they need not satisfy q^L <= q^M <= q^U and are never silently sorted here.
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Every TFN component must be strictly positive: both Eq. (14) and Eq. (10) divide by them.
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If a loss system loses rank (for example when one alternative is best on every criterion) the weights are undefined and the run fails closed instead of inventing a vector.
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The same source paper's F-ENTROPY branch can produce negative weights by the authors' own proposition; that branch is deliberately not implemented.
How to cite
Podvezko, V.; Zavadskas, E. K.; Podviezko, A. (2020). An Extension of the New Objective Weight Assessment Methods CILOS and IDOCRIW to Fuzzy MCDM. Economic Computation and Economic Cybernetics Studies and Research. https://doi.org/10.24818/18423264/54.2.20.04