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    Charting the future of pilots: maximizing airline workforce efficiency through advanced analytics
    (Springer Science and Business Media Deutschland GmbH, 2024) Çankaya, Burak; Erenay, Bülent; Kibis, Eyyub; Glassman, Aaron; Delen, Dursun
    Pilots and aircraft are among the most valuable assets of an airline. Buying aircraft and hiring pilots are crucial strategic decisions companies must oversee for sustainability. The cost of buying, selling, leasing, and long production times for aircraft challenge companies in making optimal long-term decisions. Union rules, pilot shortages, pilot surplus, and the cost of employing an excessive number of pilots are factors complicating the workforce planning for airline companies worldwide. Under these volatile and conflicting circumstances, many companies cannot strategically plan for the planning of pilots to aircraft to meet short-term tactical decisions against mid/long-term company strategies. In this study, our objective is to optimize long-term crew planning by minimizing the total crew cost considering captain promotions and new hires, without compromising the pilot experience. A mixed integer programming model is developed to solve the long-term airline crew planning problem. Realistic business scenarios are used to determine the optimal pilot hiring and promotion patterns for both high-and low-demand scenarios. The results show that the proposed optimization method significantly reduces crew costs without compromising the pilot experience in various demand and cost scenarios. The mathematical model, the realistic business scenarios, and the business insights for airlines are deemed novel contributions to the pertinent literature and industry practices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents
    (Pergamon-Elsevier Science Ltd, 2023) Cankaya, Burak; Topuz, Kazim; Delen, Dursun; Glassman, Aaron
    Understanding the factors behind aviation incidents is essential, not only because of the lethality of the accidents but also the incidents' direct and indirect economic impact. Even minor incidents trigger sig-nificant economic damage and create disruptions to aviation operations. It is crucial to investigate these incidents to understand the underlying reasons and hence, reduce the risk associated with physical and financial safety in a precarious industry like aviation. The findings may provide decision-makers with a causally accurate means of investigating the topic while untangling the difficulties concerning the statisti-cal associations and causal effects. This research aims to identify the significant variables and their prob-abilistic dependencies/relationships determining the degree of aircraft damage. The value and the contri-bution of this study include (1) developing a fully automatic ML prediction-based DSS for aircraft damage severity, (2) conducting a deep network analysis of affinity between predicting variables using probabilis-tic graphical modeling (PGM), and (3) implementing a user-friendly dashboard to interpret the business insight coming from the design and development of the Bayesian Belief Network (BBN). By leveraging a large, real-world dataset, the proposed methodology captures the probability-based interrelations among air terminal, flight, flight crew, and air-vehicle-related characteristics as explanatory variables, thereby re-vealing the underlying, complex interactions in accident severity. This research contributes significantly to the current body of knowledge by defining and proving a methodology for automatically categoriz-ing aircraft damage severity based on flight, aircraft, and PIC (pilot in command) information. Moreover, the study combines the findings of the Bayesian Belief Networks with decades of aviation expertise of the subject matter expert, drawing and explaining the association map to find the root causes of the problems and accident relayed variables. & COPY; 2023 Elsevier Ltd. All rights reserved.