Abstract
Already faced with tight competition and low profit margins, the airline industry is going through major changes in the wake of the current pandemic resulting in travel restrictions and slump demands, prompting airlines to curtail services and investments in every aspect of business. To that end, developing a comprehensive method of improving airline performance measures is crucial. However, this type of problem is complex to solve due to a large number of factors, requiring a systematic approach. It entails taking into account a multitude of conflicting, or sometimes interrelated criteria, hence becoming an inherently multiple criteria decision making problem. This study is aimed to assess the competitiveness of airlines and evaluate their financial and operational performances in relation to such criteria. We test FAHP, TOPSIS, and a hybrid method of combining FAHP and TOPSIS methods. In particular, regarding the hybrid method, FAHP is employed to determine the influential weights of criteria that are utilized in TOPSIS for preference values among alternatives. We demonstrate the applicability of the proposed methods to solving a MCDM problem of airline performance assessments using real data sets. Further, this study focuses on examining the relationship between financial and operational performance criteria, as well as gleaning insights for airlines to build an evaluation system that would aid in understanding their strength and weakness in the performance metrics. The computational experiment results of our hybrid FAHP-TOPSIS model support the efficacy of incorporating fuzzy values concerning influential weight criteria. By judiciously distributing criteria weights that are specific to the airline industry, our proposed model captures preference scores reflective of industry-related and concurrent measures. This modeling framework can help airlines better evaluate the systematic influential relation structure among criteria in critical financial and operational dimensions.
Highlights
Faced with tight competition, low profit margins, volatile oil price, and weak demands due to the recent coronavirus pandemic, airlines are struggling to survive by curtailing services and capital investments, and adopting different fuel hedging schemes
Fuzzy AHP was applied to calculate the weights of the criteria and fuzzy TOPSIS was applied to rank the alternatives utilizing the weights obtained with fuzzy AHP
In order to reduce the effect of such subjective elements, we develop a two-stage process where Fuzzy Analytic Hierarchy Process (FAHP) is first applied to develop criteria weights that are subsequently used in the TOPSIS analysis within a combined Multi-Criteria Decision Making (MCDM) modeling framework (Ertuğrul & Karakaşoğlu, 2009)
Summary
Low profit margins, volatile oil price, and weak demands due to the recent coronavirus pandemic, airlines are struggling to survive by curtailing services and capital investments, and adopting different fuel hedging schemes. The improvement of airline financial and operational performances involves a complex decision-making process, requiring a systematic approach Making such decisions entails taking into account a number of conflicting, or sometimes interrelated, criteria (Gomes et al, 2014). Considering a number of financial measures available for airlines (see Section 2), this study adopts a Multi-Criteria Decision Making (MCDM) framework, to evaluate the airline performance using financial and operational data. We develop and apply (i) Fuzzy Analytic Hierarchy Process (FAHP); (ii) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS); and (iii) a hybrid method of combining Fuzzy AHP and TOPSIS methods to assess airline performances. Garg (2016) developed a model for selecting strategic partners in the problem of making airline alliances by applying AHP for evaluation of the criteria and fuzzy TOPSIS for the partner decision of a strategic alliance. The results showed that variables associated with airline networks size are most attributable, best indicators, to efficiency levels
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