COCOSO: Combined Compromise Solution

Overview

Aggregated exponential comparison (WSM + WPM combination). Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Aggregated exponential comparison (WSM + WPM combination)

Limitations

• •Assumes: Criteria preferences are independent (no synergistic interactions)
• •Assumes: Compensation is acceptable: high score on one criterion can offset low on another
• •Assumes: Decision matrix is complete (no missing values)

Assumptions to verify

• •Criteria preferences are independent (no synergistic interactions)
• •Compensation is acceptable: high score on one criterion can offset low on another
• •Decision matrix is complete (no missing values)

When not to use

• •Criteria strongly correlated → consider DEMATEL/ANP for interdependence
• •Non-compensatory preferences → consider outranking (ELECTRE/PROMETHEE)

Edge cases

• •See F.steps and D.parameters for COCOSO-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Hatalı: 'COCOSO bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'COCOSO bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'COCOSO bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: COCOSO'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: COCOSO'yi 'Non-compensatory preferences → consider outranking (ELECTRE/PROMETHEE)' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.

Worked example

1. 1.Adım 1 (F1): Step 1: Compromise normalisation per criterion type. Formül: r_{ij} = \begin{cases} \dfrac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}} & j\in J^{+} \\ \dfrac{\max x_{ij}-x_{ij}}{\max x_{ij}-\min x_{ij}} & j\in J^{-} \end{cases} Anchor: Yazdani 2019, p.2506 Eq.(1)
2. 2.Adım 2 (F2): Step 2: Weighted Sum component S_i = Σ w_j r_ij. Formül: S_{i} = \sum_{j=1}^{n} w_{j}\,r_{ij} Anchor: Yazdani 2019, p.2506 Eq.(2)
3. 3.Adım 3 (F3): Step 3: Weighted Product component P_i. Formül: P_{i} = \sum_{j=1}^{n} (r_{ij})^{w_{j}} Anchor: Yazdani 2019, p.2506 Eq.(3)
4. 4.Adım 4 (F4): Step 4: Three appraisal scores k_a (additive), k_b (sum of ratios), k_c (balanced). Formül: k_{a,i}=\dfrac{P_{i}+S_{i}}{\sum_{k}(P_{k}+S_{k})},\ \ k_{b,i}=\dfrac{S_{i}}{\min_{k} S_{k}}+\dfrac{P_{i}}{\min_{k} P_{k}},\ \ k_{c,i}=\dfrac{\lambda S_{i}+(1-\lambda)P_{i}}{\lambda \max_{k} S_{k}+(1-\lambda)\max_{k} P_{k}} Anchor: Yazdani 2019, p.2506 Eqs.(4)-(6)
5. 5.Adım 5 (F5): Step 5: Final compromise k_i and descending ranking. Formül: k_{i} = (k_{a,i}\,k_{b,i}\,k_{c,i})^{1/3} + \tfrac{1}{3}(k_{a,i}+k_{b,i}+k_{c,i}) Anchor: Yazdani 2019, p.2507 Eq.(7)

Commonly paired with

• •AHP + COCOSO (high)
• •BWM + COCOSO (high)
• •ENTROPY + COCOSO (high)
• •CRITIC + COCOSO (high)
• •SWARA + COCOSO (high)

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