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Ranking
PL-EDAS - Probabilistic Linguistic extension of EDAS
Probabilistic Linguistic outranking/ranking - Probabilistic Linguistic Term Set (PLTS: {L_k|p_k})
Pang, Q., Wang, H., Xu, Z.2016doi:10.1016/j.ins.2016.06.021 ↗
Overview
pl-edas extends EDAS to handle Probabilistic Linguistic uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Probabilistic Linguistic Term Set (PLTS: {L_k|p_k}) algebra. The final scores are defuzzified via expected linguistic value E = Σ p_k · index(L_k) before ranking.
- Output
- utility, higher is better
- Data
- Probabilistic Linguistic, uncertainty tuples complete
- Weights
- Needs a weight source
- Size
- 2+ alternatives, 3-10 criteria works best
- Used for
- Probabilistic Linguistic MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms
Look elsewhere when
- •Crisp data sufficient - use base EDAS directly (avoid unnecessary uncertainty layer)
- •Aggregation operator (PFWA/PFOWA/etc.) not specified - output ambiguous
Assumptions to verify
- Decision matrix entries are valid Probabilistic Linguistic numbers/tuples
- Underlying crisp method's compensation assumption holds in uncertain space
- All decision-maker(s) and experts use the same linguistic/uncertainty scale
Edge cases and pitfalls
Value-space violation: ensure all entries satisfy PLTS: {L_k|p_k} where L_k is linguistic label, Σ p_k ≤ 1 before computation.
Defuzzification method affects ranking: expected linguistic value E = Σ p_k · index(L_k) is the canonical choice but alternatives exist.
Works with
Commonly takes its weights from
How to cite
Pang, Q.; Wang, H.; Xu, Z. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences. https://doi.org/10.1016/j.ins.2016.06.021
System ID, as it appears in reports and the API
PL-EDAS