Rough-MARCOS — Rough extension of MARCOS
ROUGH-MARCOS (Rough-MARCOS — Rough extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Matić, B. Marinković, M. Jovanović, S. Sremac, S. Stević, Ž. in 2022. 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
This section is available to Pro members. Upgrade to Pro
How it works
This section is available to Pro members. Upgrade to Pro
When to use it
rough-marcos extends MARCOS to handle Rough uncertainty. All arithmetic operations (normalisation, weighting, row summation, utility ratio computation) are performed using Rough number (lower approximation L, upper approximation U) algebra. Utility degrees Y+/Y- are computed via IRN division before defuzzification. The final composite score f(Y_i) is obtained via MARCOS utility functions and ranked in descending order.
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.
- 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
This section is available to Pro members. Upgrade to Pro
Sources
- 1.Matić, B., Marinković, M., Jovanović, S., Sremac, S., Stević, Ž. (2022). Intelligent Novel IMF D-SWARA—Rough MARCOS Algorithm for Selection Construction Machinery for Sustainable Construction of Road Infrastructure. Buildings
You have read it. What now?
Cite this page
ScholarGate. (2026, June 2). ROUGH-MARCOS. ScholarGate. https://scholargate.app/decision-making/rough-marcos