Fuzzy Logarithm Methodology of Additive Weights (TFN)
F-LMAW (Fuzzy Logarithm Methodology of Additive Weights (TFN)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N. in 2021 crisp; 2022 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
F-LMAW returns crisp criterion weights on the simplex (Σ w_j = 1, w_j ≥ 0) from linguistic expert assessments. Use it when (i) only verbal expert judgements are available, (ii) you want a logarithmic transform that compresses extreme priorities, and (iii) you have at least one expert and at least two criteria. With multiple experts, the Bonferroni mean (p=q=1 default) aggregates expert priorities before the logarithmic transform.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- Results depend on the chosen normalisation, weights, and parameter settings.
Common pitfalls
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Sources
- 1.Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N. (2022). Modification of the Logarithm Methodology of Additive Weights (LMAW) by a Triangular Fuzzy Number and Its Application in Multi-Criteria Decision Making. Axioms
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Cite this page
ScholarGate. (2026, June 2). F-LMAW. ScholarGate. https://scholargate.app/decision-making/f-lmaw