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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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    An interpretable decision-support systems for daily cryptocurrency trading
    (Elsevier Ltd, 2022) Dolatsara, Hamidreza Ahady; Kibis, Eyyub; Çağlar, Musa; Şimşek, Serhat; Dağ, Ali; Dolatsara, Gelareh Ahadi; Delen, Dursun
    Cryptocurrencies, especially Bitcoin (BTC), have become an important commodity for both individual and corporate investors within the last decade. The limited supply, high volatility, and random price fluctuations have increased investors' interest in BTC, especially in daily trading. Although BTC has been yielding a high rate of returns, price fluctuations and constant speculations make the investors wary of unexpected price movements. Predictive modeling suffers from the complexity of the datasets (i.e., the high number of features employed to forecast BTC movements) as well as the black-box nature of most machine learning algorithms (which is especially problematic for corporate investors since they are obligated to disclose their investment decisions to their clients). Therefore, the main goal of the current study is to assist individual and corporate investors in making transparent and interpretable daily BTC trading decisions by developing a predictive analytics framework. To address the complexities posed by the datasets, a comprehensive tri-level feature selection approach is proposed. The selected features are then, fed into the Classification & Regression Tree (C&RT) to build a highly parsimonious, transparent, and interpretable prediction model. The resultant model was not only evaluated on the test (holdout) sample but was also tested on challenging time periods, including the first half of 2020 (the start of the pandemic era) to exhibit the viability and reliability of the proposed framework. Finally, a decision support tool is developed for the practical implementation of the model. The tool can be used by short-term investors not only due to its highly simplistic, transparent, and interpretable structure, but also its higher accuracy, sensitivity, and specificity results when compared to the extant literature. © 2022 Elsevier Ltd