Linguistic extension of L2T-SAW
L2T-SAW (Linguistic extension of L2T-SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E. in 2018. 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
l2t-saw extends L2T-SAW to handle Linguistic uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using 2-Tuple Linguistic Variable (2TL: (s_i, α)) algebra. The final scores are defuzzified via Δ^{-1}(s_i, α) = i + α before ranking.
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.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E. (2018). Prioritization of the launch of ICT products and services through linguistic multi-criteria decision-making (2-tuple SAW). Technological and Economic Development of Economy
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ScholarGate. (2026, June 2). L2T-SAW. ScholarGate. https://scholargate.app/decision-making/l2t-saw