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Ranking
P-AROMAN - Plithogenic extension of P-AROMAN
Plithogenic outranking/ranking - Plithogenic Set (PltS: attribute values with contradiction degrees)
Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simić, V., Bacanin, N.2023doi:10.1109/ACCESS.2023.3265818 ↗
Overview
p-aroman extends P-AROMAN to handle Plithogenic uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Plithogenic Set (PltS: attribute values with contradiction degrees) algebra. The final scores are defuzzified via dominant value defuzzification; approach varies by appurtenance function before ranking.
- Output
- utility, higher is better
- Data
- Plithogenic, uncertainty tuples complete
- Weights
- Needs a weight source
- Size
- 2+ alternatives, 3-10 criteria works best
- Used for
- Plithogenic MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms
How it works
- 1
Contradiction adjustment; score extraction.
Bošković et al. 2023 DMAME 6(1); Plithogenic variant
- 2
Step-1 linear + Step-2 vector normalisation; combined value.
Bošković et al. 2023 Sec.3
- 3
AROMAN score.
Bošković et al. 2023
- 4
Rank descending by AROMAN_i.
Bošković et al. 2023
- 5
Final ranking output.
Bošković et al. 2023
Fits when / Look elsewhere when
Fits when
- •Preserves plithogenic uncertainty through the pipeline rather than premature crispification at elicitation
- •Native group-decision support (multi-DM aggregation built into the pipeline)
Look elsewhere when
- •Crisp data sufficient - use base AROMAN directly (avoid unnecessary uncertainty layer)
- •Aggregation operator (PFWA/PFOWA/etc.) not specified - output ambiguous
Assumptions to verify
- Decision matrix entries are valid Plithogenic 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 Plithogenic: each attribute has a set of values V with appurtenance degrees and contradiction degrees c(v,D) before computation.
Defuzzification method affects ranking: dominant value defuzzification; approach varies by appurtenance function is the canonical choice but alternatives exist.
Works with
Commonly takes its weights from
How to cite
Bošković, S.; Švadlenka, L.; Jovčić, S.; Dobrodolac, M.; Simić, V.; Bacanin, N. (2023). An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)-A Case Study of the Electric Vehicle Selection Problem. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3265818
System ID, as it appears in reports and the API
P-AROMAN