Rough-WASPAS - Rough extension of WASPAS

Overview

ROUGH-WASPAS (Stojić et al. 2018) extends WASPAS to rough number intervals [L,U]. Normalization uses rough interval divisor [max_L, max_U] (not scalar max). WSM and WPM operate entirely in rough interval arithmetic with rough interval weights [w^L_j, w^U_j]. The combined score A_i is kept as a rough interval via data-driven λ (itself a rough interval computed from Eq.17); defuzzification to crisp midpoint (A^L+A^U)/2 occurs only at the final ranking step. Weights must be rough AHP intervals (or equivalent rough weight source). Sensitivity on λ: setting λ=[0.5, 0.5] gives slightly lower scores but identical ranking in published case study.

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