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Rough-COPRAS — Rough extension of COPRAS

OriginatorPamučar, D. Božanić, D. Lukovac, V. Komazec, N.Year2018Sources1Related methods8

ROUGH-COPRAS (Rough-COPRAS — Rough extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D. Božanić, D. Lukovac, V. Komazec, N. 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

ROUGH-COPRAS extends COPRAS to Interval Rough Number (IRN) uncertainty. Steps: (1) column-sum IRN normalization, (2) scalar weighting (w_j ⊗ IRN), (3) separate benefit S+^IRN and cost S-^IRN sums in rough domain, (4) defuzzify S+ and S- via midpoint then compute crisp COPRAS Q_i, (5) normalize to N_i(%). Note: the seminal paper (Pamučar 2018) uses full 4-tuple IRNWGBM arithmetic throughout — our implementation uses simplified 2-tuple [L,U] outer-bound IRN with scalar weights, producing different rankings.

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.
  • Assumes full compensation — a strong score on one criterion can offset a weak score on another.

Common pitfalls

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Sources

  1. 1.
    Pamučar, D., Božanić, D., Lukovac, V., Komazec, N. (2018). Normalized weighted geometric Bonferroni mean operator of interval rough numbers – application in interval rough DEMATEL-COPRAS model. Facta Universitatis Series: Mechanical Engineering
    DOI 10.22190/fume180503018p

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ScholarGate. (2026, June 2). ROUGH-COPRAS. ScholarGate. https://scholargate.app/decision-making/rough-copras