SECA: Simultaneous Evaluation of Criteria and Alternatives

Overview

Simultaneous weight derivation and ranking (objective optimisation). Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Simultaneous weight derivation and ranking (objective optimisation)

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 SECA-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Hatalı: 'SECA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'SECA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'SECA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: SECA'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: SECA'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: Linear normalisation (min-max) to [0,1]. Formül: r_{ij} = \frac{x_{ij}-x_{j}^{\min}}{x_{j}^{\max}-x_{j}^{\min}} \text{ (benefit)};\quad r_{ij} = \frac{x_{j}^{\max}-x_{ij}}{x_{j}^{\max}-x_{j}^{\min}} \text{ (cost)} Anchor: Keshavarz Ghorabaee et al. 2018, p.268 Eqs.(1)-(2)
2. 2.Adım 2 (F2): Step 2: Solve non-linear optimisation to determine weights w_j and appraisal scores A_i simultaneously by maximising a combination of total appraisal and minimising variance. Formül: \max\;f(w) = \beta\sum_{i=1}^{m}A_{i} - (1-\beta)\sum_{j=1}^{n}\sigma_{j}^{2}(w);\quad A_{i}=\sum_{j=1}^{n}w_{j}r_{ij},\quad \sigma_{j}^{2}(w)=\frac{1}{m}\sum_{i=1}^{m}(w_{j}r_{ij}-\bar{r}_{j}w_{j})^{2};\quad \text{s.t.}\;\sum_{j}w_{j}=1,\;w_{j}\geq 0 Anchor: Keshavarz Ghorabaee et al. 2018, p.269 Eqs.(3)-(6)
3. 3.Adım 3 (F3): Step 3: Rank alternatives in descending order of A_i. Formül: \text{rank}(A_i) = \text{rank by descending }A_{i} Anchor: Keshavarz Ghorabaee et al. 2018, p.270

Commonly paired with

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

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