Combinative Distance-Based Assessment
CODAS (Combinative Distance-Based Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. in 2016. 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
H_i can be any real number. Higher H means the alternative is further from the anti-ideal solution on both Euclidean and Taxicab metrics. The Taxicab distance acts as a secondary discriminator: it is used only when Euclidean distances are nearly equal (|E_i − E_k| < τ). τ=0.02 is the seminal paper default.
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.Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research
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Cite this page
ScholarGate. (2026, June 2). CODAS. ScholarGate. https://scholargate.app/decision-making/codas