CODAS Algorithm

The Combinative Distance-based ASsessment (CODAS) method determines the desirability of alternatives by calculating their Euclidean and Taxicab distances from the negative-ideal solution.

1. Normalization:
For Benefit: \( r_{ij} = \frac{x_{ij}}{\max_i x_{ij}} \) | For Cost: \( r_{ij} = \frac{\min_i x_{ij}}{x_{ij}} \)
2. Weighted Matrix:
\[ v_{ij} = w_j \times r_{ij} \]
3. Negative-Ideal Solution (\(ns\)):
\[ ns_j = \min_i v_{ij} \]
4. Distance Measures:
Euclidean: \( E_i = \sqrt{\sum (v_{ij} – ns_j)^2} \)
Taxicab: \( T_i = \sum |v_{ij} – ns_j| \)
5. Assessment Score (\(AS_i\)):
\[ AS_i = \sum_{k=1}^m (E_i – E_k) + \psi(E_i – E_k) \times (T_i – T_k) \] Where \(\psi\) is a threshold function (usually \(\tau = 0.02\)).

Solved Example: Industrial Robot Selection

Criteria: C1 Speed (B), C2 Load (B), C3 Price (C), C4 Reliability (B).
Weights: 0.3, 0.2, 0.3, 0.2.

Step 1: Decision Matrix
AltC1 (B)C2 (B)C3 (C)C4 (B)
R1105080000.95
R2124090000.98
R396075000.90
R4114585000.92
Step 2: Solved Normalization & Weighting

Example R1-C1: \(10 / 12 = 0.833\) | Weighted: \(0.833 \times 0.3 = 0.250\)

Example R1-C3 (Cost): \(7500 / 8000 = 0.937\) | Weighted: \(0.937 \times 0.3 = 0.281\)

Step 3: Distances from Negative-Ideal (ns)

After finding \(ns = [0.225, 0.133, 0.250, 0.183]\):

AltEuclidean (E)Taxicab (T)
R10.0460.081
R20.0820.141
R30.0530.066
R40.0540.091
Step 4: Final Assessment Score (AS)

R2 has the highest Euclidean distance from the “worst” case and highest \(AS\).
Rank 1: Robot 2

Interactive CODAS Calculator

Weights
Type
A1
A2
A3
A4
Threshold (\(\tau\)):
Math Tools

Designed by: Dr. M.U. Mirza

Mathematical Researcher & Educator

Machine Learning Fuzzy Sets Computational Math Graph Theory
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