An integrated MCDM model for improving airline operational and financial performance. (May 2018)

• Record Type: Journal Article
• Authors: Gudiel Pineda, Pedro Jose; Liou, James J.H.; Hsu, Chao-Che; Chuang, Yen-Ching
• Is Part Of: Journal of air transport management. Volume 68(2018)
• Page Start: 103
• Page End: 117
• Publication Date: 2018-05
• Subjects: Airline performance -- DRSA -- DANP -- VIKOR -- MCDM; Airlines -- Management -- Periodicals; Aeronautics, Commercial -- Management -- Periodicals; 387.7068
• DOI: 10.1016/j.jairtraman.2017.06.003
• Languages: English
• ISSNs: 0969-6997

Abstract

Abstract: The development of better methods for the improvement of airline performance is crucial, but this type of problem is difficult to solve because of the large number of complex factors involved making this inherently a multiple criteria decision making (MCDM) problem. In current studies, the factors to be evaluated are considered based upon a literature review or expert opinions. This study proposes an integrated model that combines data mining and MCDM to extract the critical factors for the improvement of airline performance. We apply the dominance-based rough set approach to extract the essential factors. The decision-making trial and evaluation laboratory method with the concepts of the analytic network process (DANP) is then used to construct the complex evaluation system. Finally, the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, meaning multicriteria optimization and compromise solution) method is applied to select the suitable improvement alternative goals with the corresponding weights provided by the DANP method. The results show that the current model can be used as the basis for a benchmark industry improvement index which can be used to evaluate each airline individually with defined planning goals to achieve financial efficiency by improving operational efficiency. Highlights: This study proposes an integrated method to identify and diagnose financial and operational performance of airlines. The model combines data mining and multiple criteria decision making techniques. A set of US airline data is applied to demonstrate the efficiency and effectiveness of the proposed method.