## 2 Theoretical Background
[34] was to evaluate the impact of the application of AHP in combination with data envelopment analysis (DEA) on the efficiency of airports. The study shows that the use of the AHP/DEA-AR (Assurance Region) model introduces an element of subjectivity in the assessment of efficiency, while the application of the DEA-AR model increases the ability to differentiate between the analyzed airports. This makes it easier to rank airports and compare their performances. The authors [PERSON] and [PERSON], in 2016 [28], emphasized that each criterion is crucial for developing and maintaining a high level of customer service at an airport, and that the presence of multiple criteria makes the task of evaluating and ranking airports complex. In this paper, a model based on the AHP fuzzy TOPSIS method was presented for evaluating ASQ enablers and ranking airports. The authors analyzed seven ASQ enablers, including accessibility, check-in process, maintenance, and others. Finally, a sensitivity analysis was conducted to check the robustness of the model. The aim of the study from 2018 by [PERSON] and [PERSON] [35] was to categorize the quality of service factors used to evaluate services at three regional airports in Thailand. The quality of service factors data were collected through a literature review and a survey of 300 passengers regarding their service satisfaction. Subsequently, an exploratory factor analysis (EFA) was conducted to analyze the survey data and categorize them into sub-factors. The AHP method was also applied to estimate the weighting factors through pairwise comparisons. The results of this study identify key quality of service factors that airport managers can use to improve the level of service of their facilities. In 2018, [PERSON] et al. [36] presented a methodology to help select aircraft type(s) that comply with the market conditions and the airline's requirements. This being a multi-criteria problem, the authors suggest the fuzzy AHP (FAHP)-based approach and a logarithmic fuzzy preference programming (LFPP) method. To evaluate the criteria and sub-criteria, experts from various airlines and universities were interviewed, resulting in pairwise comparison matrices' design. The applicability of the proposed methodology is presented on a case study of a regional airline. In 2020, [PERSON] [37] presented a model based on the fuzzy analytical hierarchy process (FAHP) for the assessment of ASQ enablers and the consequent reduction in uncertainty in the decision-making process. This model defined and evaluates indicators of service quality at airports. The aim of the study was to evaluate the ASQ enablers and identify areas for improvement with a focus on customer service quality. Finally, a sensitivity analysis was performed to check the robustness of the model. In research by [PERSON] et al. [38], in 2020, the airport selection criteria of low-cost carriers (LCCs) were identified and ranked. In total, 5 main factors comprising 16 sub-factors were defined. Experts from the aviation industry evaluated the factors. Then, 28 executives who were involved in the strategy planning and formulation of LCCs completed the survey questionnaire regarding factors' comparison. By using the fuzzy analytical hierarchy process (FAHP), the collected data were analyzed. The results showed that the main factors influencing LCCs' selection of airport were airport charges, airport performance, airport growth opportunities, catchment area, and airport infrastructure. Airport costs, demand for LCC services, and passenger throughput were shown to be the three most important sub-factors. The contribution of the study is reflected in an assessment of the main considerations influencing LCCs' selection of airport, i.e., assessment of airports that are suitable for the operation of low-cost carriers. In 2021, [PERSON] et al.