## 3 Contributions of the Proposed Work  
The literature review in the previous section discussed relevant studies. Accordingly, certain limitations in those studies can be identified with respect to the problem models and the computational methods. As far as the problem models are concerned, several studies used simple decision models, or used selection criteria for which the required data are difficult to obtain. For example, single selection criteria were used in several studies [7; 8; 9; 10; 12; 13; 14], which made the models rather unrealistic. Furthermore, in studies where multi-criteria models were used, the decision criteria \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Method** & **Decision Approach** & **Number of Criteria** \\ \hline
[5] & 2013 & Nil & Nil & 5 \\ \hline
[6] & 2015 & GA & Multi-criteria & 5 \\ \hline
[7] & 2010 & Others & Single-criteria & 1 \\ \hline
[8] & 2013 & PSO & Single-criteria & 1 \\ \hline
[9] & 2009 & Probabilistic model & Single-criteria & 1 \\ \hline
[10] & 2014 & Weibull distribution & Single-criteria & 1 \\ \hline
[12] & 2016 & Self-proposed & Single-criteria & 1 \\ \hline
[13] & 2013 & GA & Single-criteria & 1 \\ \hline
[14] & 2012 & Self-proposed & Single-criteria & 1 \\ \hline
[15] & 2013 & GA, PSO, DE & Multi-criteria & 4 \\ \hline
[16] & 2016 & AHP & Multi-criteria & Over 30 \\ \hline
[17] & 2014 & WASPAS & Multi-criteria & 5 \\ \hline
[18; 19] & 2012 & Fuzzy logic & Multi-criteria & 3 \\ \hline
[20] & 2016 & Fuzzy logic & Multi-criteria & 6 \\ \hline
[21; 23] & 2019, 2017 & Fuzzy goal programming & Multi-criteria & 6 \\ \hline
[22] & 2017 & Weighted sum & Multi-criteria & 5 \\ \hline
[24] & 2017 & AHP & Multi-criteria & 14 \\ \hline
[25] & 2016 & Blade element momentum & Multi-criteria & 6 \\ \hline
[26] & 2017 & Hybrid AHP-TOPSIS & Multi-criteria & 4 \\ \hline
[27] & 2015 & Weibull distribution & Single-criteria & 1 \\ \hline
[28] & 2017 & Weibull distribution & Single-criteria & 1 \\ \hline
[29] & 2019 & Pareto dominance & Multi-criteria & 3 \\ \hline
[30] & 2020 & Hybrid AHP-TOPSIS & Multi-criteria & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of reviewed literature. used in the underlying decision models were complex since the data for the decision criteria were difficult to obtain [5; 7; 9; 15; 16; 17; 25; 30]. Examples of such criteria are product reliability and availability, maintenance management, system reliability indices, matching index, turbine cost index, the integrated matching index, \(CO_{2}\) emissions, turbine blade radius, tip speed ratio, blade sectional radius width (chord) and twisting angle distribution, and blade airfoil standard. With regard to the computational methods, the majority of studies used complex algorithms. For example, non-deterministic algorithms such as GA, PSO, DE, and Pareto frontier approach were used in several studies [6; 8; 11; 13; 15; 29]. The aforementioned algorithms have limitations with regard to their computational efficiency; in comparison with a deterministic algorithm (such as TOPSIS), they take a notable amount of time to generate the best solution. Similarly, methods such as the analytical hierarchy process (AHP), fuzzy logic, the weighted sum method, and goal programming have been used in several studies [16; 18; 19; 20; 21; 23; 24]. While the computational efficiencies of those methods are better than those of non-deterministic algorithms, other limitations can be observed. A practical limitation of AHP is that a high number of alternatives implies a large number of comparisons [32]. In fuzzy logic, the selection of an appropriate fuzzy operator is a challenge, as different fuzzy operators may give different results [21]. In goal programming, identifying the target (ideal solutions) is a difficult task, since incorrect targets may result in inefficient solutions [33].