MCDMDecision-makingRankingMath steps
Plithogenic MABAC (with BWM weighting and Rough Number uncertainty)
PL-MABAC (Plithogenic MABAC (with BWM weighting and Rough Number uncertainty)) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D. Ćirović, G. in 2015. 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
S_i > 0 means alternative is above BAA (upper approximation area G+, better than border). S_i < 0 means below BAA (G−, worse). Rank by S_i descending.
Strengths & limitations
Strengths
- 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.
Limitations
- May exhibit rank reversal when alternatives are added to or removed from the set.
Common pitfalls
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Sources
- 1.Pamučar, D., Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using MABAC. Expert Systems with Applications
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Cite this page
ScholarGate. (2026, June 2). PL-MABAC. ScholarGate. https://scholargate.app/decision-making/pl-mabac