Kiraci, Kasım; Tanriverdi, Gökhan; Akan, Ercan

The COVID-19 pandemic increased the risk of financial distress, bankruptcy, or both, in the airline industry. Whether airlines can survive or not during and/or after the pandemic is closely related to their decisions and actions which will enable their success by increasing their resilience. In crisis periods such as COVID-19, the decisions taken by airlines are strategically important for achieving sustainable success. Thus, it is critical to understand which factors are more important for airlines to shape their actions and make correct decisions. This paper investigates the sustainable success factors on which airlines should focus to provide resilience during the COVID-19 pandemic crisis. It provides a robust model using the interval type-2 fuzzy analytic hierarchy process (IT2FAHP) and interval type-2 fuzzy Decision Making Trial and Evaluation Laboratory (IT2FDEMATEL) to identify and rank success factors. The findings indicate that financial and operational factors are extremely important to ensure resilience for airlines. In addition, the results of the study reveal that operational factors and information sharing factors have an impact on financial factors and customer satisfaction.

Özdemir, Mustafa; Dönmez, Kadir; Demirel, Soner

Pseudo-pilots work in real-time simulations in air traffic control training and carry out an important task to ensure appropriate training for the trainee controllers. They are in constant communication with controllers during the training and are responsible for fulfilling the instructions given by the controllers. Unlike real operations, pseudo-pilots are required to monitor many aircraft simultaneously on the simulator’s radar screen and perform various interventions. This situation can be challenging, and the impact of such situations on their workload is usually neglected. In the literature, various studies have revealed the factors affecting controller workload; however, the factors affecting the workload of pseudo-pilots have not been emphasized. In this study, statistical analyses were carried out using the data obtained from real-time simulations and NASA-TLX surveys. As a result, the number of aircraft, controller performance, and the interruption duration were determined as the key factors that affect the overall workload of pseudo-pilots.

Atasoy, Vehbi Emrah

This paper aims to perform accurate prediction of fuel flow (FF) by employing various models: deep learning (DL), random forest (RF), generalized linear model (GLM), and the Eurocontrol Base of Aircraft Data (BADA) model, and to examine the link between FF and different aircraft performance parameters. The flight data set used in this study is obtained from real turbofan engine narrow-body aircraft performing short-distance domestic subsonic flights, containing a total of 2,674 cruise flights between 31 city pairs. Several statistical error analyses are conducted to compare the performance of the models. Root mean square error, mean absolute error, and mean squared error values for DL are calculated to be 0.01, 0.008, and 0.0001, respectively. On the coefficient of determination ((Formula presented.)) test for validation, the DL model has the highest value of 0.94. Results of the analysis in this paper show that the DL model offers the best ability to predict FF in all statistical tests, which makes it the best-suited model to estimate FF. These findings can provide the means to make more efficient trajectory planning and forecasts in air traffic management, and more accurately predict fuel consumption of aircraft, thereby decreasing emission levels of carbon gases and of various pollutants and airline operating expenses from fuel costs.