Rough-TODIM — Rough extension of TODIM

When to use it

rough-todim extends TODIM to handle Rough uncertainty. Decision matrix entries are rough numbers [L,U] representing customer preference scores. Normalization uses global-max (all bounds divided by max upper bound across matrix). The TODIM φ_c function computes crisp scalar distances d_c = |p^L_ic - p^L_jc| + |p^U_ic - p^U_jc| and applies importance-based scaling: full scale (α=β=1) for Important/Most-Important criteria, ×0.5 for Averagely Important, Rule 3 for Low Important. All dominance computations (δ, Σδ) are crisp. The final ξ scores are crisp [0,1] values computed directly from crisp dom

Strengths & limitations