## 2 Literature Review  
As mentioned in Section 1, the turbine selection problem is concerned with both the problem model and the computational model. Accordingly, the literature review on the turbine selection problem in the following paragraphs is organized considering the problem and computational models. A summary of reviewed literature is also provided in Table 1.  
[PERSON] and [PERSON] [5] carried out interviews with wind energy experts and identified product reliability and availability, production volume, cost, and maintenance management as the important factors for turbine selection. The main drawback of the study was that modeling of the problem was not provided which would be used to make the selection decisions. [PERSON] et al. [6] identified rotor diameter, generator size, hub height, pitch angle range, and rotations per minute (RPM) range as the decision criteria. A genetic algorithm (GA)-based approach was developed. The major concern of the study was the computational complexity arising from the use of GA. [PERSON] and [PERSON] [7] used system reliability indices to compare nine different wind turbine types. [PERSON] et al. [8] used energy production capacity as the only criterion for decision making, and utilized a particle swarm optimization (PSO) algorithm to solve the problem. Only one turbine type was considered in the optimization process. Furthermore the use of PSO was not an appropriate decision since the algorithm has a high time complexity. [PERSON] and [PERSON] [9] used turbine reliability as the only selection criterion, based on which a probabilistic model was developed while assuming five turbine types in the selection pool. [PERSON] et al. [10] adopted an approach based on Weibull distribution but considered capacity factor as the only decision criterion. They studied the proposed approach using 24 turbine types. [PERSON] et al. [11] adapted GA and used two decision criteria which were power output and deviation in daily power output. [PERSON] et al. [12] considered 121 varieties of turbines while using cost of energy as the decision criterion, but a limitation was the complexity of the algorithm they proposed. [PERSON] and [PERSON] [13] considered blade design as the decision factor and used GA to optimize the blade shape according to site specifications. A major limitation of the study was that their approach was to design a completely new turbine for each specific site, rather than using existing turbines from different manufacturers. [PERSON] [14] used minimum cost of energy as the decision criterion and applied their proposed approach to study various sites in Iceland while assuming 47 different turbine types. [PERSON] et al. [15] adapted several nature-inspired algorithms, namely, GA, PSO, and differential evolution (DE) for turbine selection while using matching index, turbine cost index, and the integrated matching index for selection. [PERSON] et al. [16] identified over 30 decision criteria for turbine selection, but also identified that only a subset of these criteria could be used in the selection process. The selection of these criteria was done based on the characteristics of the potential site. They used an AHP-based approach. [PERSON] et al. [17] proposed a turbine selection strategy based on WASPAS method for offshore wind farms while considering five selection criteria. These criteria were nominal power of the wind turbine, maximum power generated in the area, the amount of energy per year generated in the area, investments, and \(CO_{2}\) emissions. However, only four turbine types were considered. [PERSON] and [PERSON] [18; 19] proposed the use of fuzzy logic for turbine selection while considering hub height, zero output percentage, and rated output as the decision criteria. They also proposed an enhanced two-tier selection approach [20] based on fuzzy logic.