Rough-SAW - Rough extension of SAW

Overview

rough-saw extends SAW to handle Rough uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Rough number (lower approximation L, upper approximation U) algebra. The final scores are defuzzified via midpoint (L+U)/2 before ranking.

Output

utility, higher is better

Data

Rough Number, uncertainty tuples complete

Weights

Needs a weight source

Size

2+ alternatives, 3-10 criteria works best

Used for

Rough MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms

Look elsewhere when

Assumptions to verify

• Decision matrix entries are valid Rough 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 Rough: L ≤ U; approximations defined by equivalence classes before computation.

• Defuzzification method affects ranking: midpoint (L+U)/2 is the canonical choice but alternatives exist.

Works with

How to cite