Cockpit Crew Research Articles

Effects of Post-COVID-19 Syndrome on Quality of Life Among Airline Crew.

Background: Interest in post-coronavirus disease 2019 (COVID-19) syndrome following COVID-19 infection has been increasing. Maintaining quality of life (QoL) is vital for airline crews because they work in a special environment, where they are responsible for the passengers' safety. This study aims to closely investigate factors affecting the QoL of airline crews, including post-COVID-19 syndrome. Methods: This study was designed as a cross-sectional survey, comprising 167 crews. Findings: Age-specific significant differences were observed in social, overall, and total QoL scores. The physical domain QoL was significantly higher in the cockpit crews than that in the cabin crews. Significant differences were found in psychological and overall QoL depending on years of continuous service. Social domain and environmental QoL were lower in those who had no symptoms after being diagnosed with COVID-19 than in those who were symptomatic. Among the participants, 4.2% had post-COVID-19 syndrome, indicating significant differences in the physical domain, depending on whether they exhibit post-COVID-19 syndrome. Conclusion: It is urgent to develop measures to increase the QoL of airline crews, investigate post-COVID-19 syndrome before returning to work, and develop strategies to manage it. Application to practice: The QoL among airline crews differed not only by the demographic characteristics of the participants but also by the presence of symptoms during COVID-19 diagnosis and post-COVID-19 syndrome. Higher QoL among airline crews is associated with the safety of both airline crews and passengers. Therefore, it is necessary to establish a systematic management protocol for airline crews returning to work after following COVID-19 infection.

민간항공 부기장 팔로워십 유형에 따른 CRM의 리더십 및 팀워크 스킬 역량 비교

It has been observed that approximately 70% of aviation accidents are closely linked to human factors within the cockpit crew. To address this critical issue, the aviation industry has implemented CRM as an operational frameork. For the effective execution of CRM, it is vital for pilots to possess 5 non-technical skills. Among these, leadership and teamwork skills are crucial components, and it is not solely reliant on the captain's leadership but also influenced significantly by the quality of followership. Followership can be classified into various types, including pragmatic and exemplary followership, among others. This research hypothesized that these different followership types would have varying impacts on leadership and teamwork skill competency. The experimental results confirmed that the pragmatic followership was more conductive to enhancing leadership and teamwork skill competency compared to the exemplary followership. Furthermore, it was identified that pragmatic followership exhibited a slight tendency towards higher communication skill competencies in comparison to the exemplary followership.

COCKPIT CREW SAFETY PERFORMANCE PREDICTION BASED ON THE INTEGRATED MACHINE LEARNING MULTI-CLASS CLASSIFICATION MODELS AND MARKOV CHAIN

The main tool of cockpit crew performance evaluation is the recorded flight data used for flight operations safety improvement since all certified airlines require implementation of a safety and quality management system. The safety performance of a flight has been a challenging issue in the aviation industry and plays an important role to acquire competitive benefits. In this study, an integrated multi-class classification machine learning models and Markov chain were developed for cockpit crew performance evaluation during their flights. At the outset, the main features related to a flight are identified based on the literature review, flight operations expert’s statements, and the case study dataset (as numerical example). Afterwards, the flights’ performance is evaluated as a target column based on four multi-class classification models (Decision Tree, Support Vector Machine, Neural Network, and Random Forest). The results showed that the random forest classifier has the greatest value in all evaluation metrics (i.e., accuracy = 0.90, precision = 0.91, recall = 0.97, and F1-score = 0.93). Therefore, this model can be used by the airline companies to predict flight crew performance before the flight in order to prevent or decrease flight safety risks.

Digital twin for Electronic Centralized Aircraft Monitoring by machine learning algorithms

Electronic Centralized Aircraft Monitoring (ECAM) parameters play a vital role in the operation of an aircraft to reduce the workload of the cockpit crew. A wide-body commercial aircraft with a triple-spool turbofan engine is examined within the scope of the study. This study is focused on the estimation of the ECAM primary engine parameters: Engine Pressure Ratio, Exhaust Gas Temperature, Fuel Flow, and Shaft Speeds without any additional measurement for data continuity. The recorded flight data obtained from a commercial aircraft is processed with machine learning methods, and the most suitable estimation method is tried to be determined. Correlation analysis is carried out for each data in the study to show strong predictor candidates. The modeling process is conducted by using MATLAB. Results indicate that the Fine Decision Tree is better at memorizing data, while the Wide Neural Network is better at generalizing data. Computational results show that the developed models are outstandingly precise and accurate to estimate aircraft's ECAM data to ensure flight safety for health and performance monitoring of an engine. Thus, when an unreliable situation occurs while performing the flight in practical conditions, the cockpit crew will be able to overcome this situation by Digital Twin.

Solving a new mathematical model for the integrated cockpit crew pairing and rostering problem by meta-heuristic algorithms under the COVID-19 pandemic

This study examines the problem of crew pairing and rostering under COVID-19 conditions. To keep the cockpit crew members safe against COVID-19, the problem is investigated in such a way that each cockpit crew member spends less daily sit time period in the airports and returns to his/her home base at the end of the day to take a rest. This study aims to introduce a Mixed-Integer Linear Programming (MILP) formulation for the problem. In order to solve the problem, three meta-heuristic algorithms including Genetic Algorithm (GA), Firefly Algorithm (FA), and Particle Swarm Optimization (PSO) are applied based on a new chromosome representation. The proposed algorithms could obtain solutions with the least possible number of cockpit crew members to cover the existing flights by considering some rules and regulation related to employing cockpits. Moreover, the findings indicate that the algorithms can provide solutions near the optimal solutions (1.94%, 2.49%, and 2.43% gaps for the GA, FA, and PSO on average, respectively) for the small-scale instances extracted from the data sets. Additionally, the proposed GA can find lower-cost solutions in comparison to the FA and PSO in approximately similar CPU time for problem instances with different sizes.

Information on cosmic radiation received by Belgian aircrew: a survey.

Exposure of aircrew to cosmic radiation had already been identified as an issue of concern in the European BSS of 1996 which requested airlines to assess the exposure of the crew and to inform their workers of the health risks their work involves. These requirements have been implemented in Belgian regulations in 2001 and updated with the transposition of the 2013/59/Euratom directive. Dosimetry data show that aircrew is the group of workers, which contributes the most to the collective dose of occupationally exposed workers in Belgium. In order to verify the extent of the information received by Belgian aircrew regarding their exposure to cosmic radiation, FANC, the Belgian radiation protection authority, launched in 2019 a large survey in collaboration with the Belgian Cockpit Association (BeCA), the professional association representing airline pilots in Belgium. The survey included 8 questions regarding the information of aircrew on cosmic radiation in general, on the individual dose level and on the risk related to exposure during pregnancy. A total of about 400 responses to the survey were received. Overall, the survey reveals that aircrew members in Belgium receive too little information about the potential risks, that they are not sufficiently informed about their own exposure and that female staff members are insufficiently informed about the hazards for the unborn child in case of pregnancy, and 66% of the respondents answered that their employer has never informed them about their exposure to cosmic radiation. However, most are aware of this phenomenon, either because they have looked up information or because they have discussed the topic with colleagues and professional associations. The results also showed that 17% of female crew continued to fly while being pregnant. Finally, the survey also allowed to identify differences and similarities between different groups of workers: cockpit and cabin crew, men and women. For instance, cabin crew was even less informed about their individual exposure than cockpit crew.

Forecasting crew fatigue risk on international flights under different policies in China during the COVID-19 outbreak.

To predict the risk of fatigue for flight crews on international flights under the new operating model policy of the civil aviation exemption approach policy during the COVID-19 outbreak, and to provide scientific validation methods and ideas for the exemption approach policy. This paper uses the change in flight crew alertness as a validation indicator, and then constructs an alertness assessment model to predict flight crew fatigue risk based on the SAFTE model theory. Then, the corresponding in-flight rotation plans for the flight is designed according to the exemption approach policy issued by the CAAC, the CCAR-121 part policy and the real operational requirements of the airline, respectively, and finally the simulation results is compared by comparing the pilot alertness and cockpit crew alertness under the exemption approach policy and the CCAR-121 part policy with the flight duration. The results show that the flight crew alertness level for the flight in-flight rotation plan simulation designed under the exemption approach policy is higher or closer to the pilot alertness level for operational flights under the CCAR-121 Part policy. This validates the reasonableness and safety of the exemption approach policy issued by the CAAC to meet the requirements of epidemic prevention and control, and provides scientific support and solutions for fatigue monitoring and management.

Preventing Tragedies Through Shared Learning

In December 1995, an American Airlines (AA) flight en route from Miami, Florida, to Cali, Colombia, crashed into the side of a mountain, killing 151 of 155 passengers on board. While, like most catastrophic incidents, a number of factors led to the crash, a fault in the flight management navigation system was key to the disaster. To save time, the cockpit crew had decided to take a different route into the Cali airport and correspondingly reprogrammed the flight management system. They entered “R” into the system, intent on heading for navigational point “ROZO.” However, the “R” also stood for ROMEO, a navigational point near Bogota, 170 miles from Cali.

Individual scheduling approach for multi-class airline cabin crew with manpower requirement heterogeneity

The cabin crew pairing problem is one of the major challenges faced by airlines. Traditionally, multi-class cabin crews are scheduled on a team basis separated by aircraft types (families) as cockpit crews. However, the manpower requirements for cabin crew across aircraft types (families) are heterogeneous, which cannot be handled by the team scheduling approach. Thus, some airlines nowadays are adopting the individual scheduling approach to deal with the flight manpower requirement heterogeneity. Motivated by the emergence of the individual cabin crew pairing practice, we conduct an analytical study which aims at improving manpower utilization while reducing costs by utilizing a new individual cabin crew pairing generation approach. We also mathematically formulate crew substitution in the model which can help hedge against manpower requirement variations. The impacts of the relationship between manpower availability with requirement benchmarks on cabin crew scheduling strategies are investigated to derive deep insights. A column generation based solution approach is developed. Computational experiments based on small-scale real-world collected flight schedules are conducted to test the distinctive features of the proposed individual pairing model. In addition, a series of large hypothetical instances are employed to examine the advantages of the proposed model over the traditional team-based model in terms of manpower utilization improvement and cost reduction through a customized Genetic Algorithm.

Incidence and Mortality of Prostate Cancer in Commercial Airline Cockpit Crew: Systematic Review and Meta-Analysis

Commercial airline cockpit crew (CCC) are potentially exposed to occupational risk factors that may have detrimental health effects. However, available literature on prostate cancer (PCa) as a health outcome is conflicted. Therefore, this review of cohort studies aims to evaluate the incidence of and mortality from PCa in CCC based on studies published to date. PubMed, Medline, EMBASE and SCOPUS were searched from 1946 to April 2021. Cohort studies reporting standardized incidence ratios (SIR) and/or standardized mortality ratios (SMR) of PCa in CCC were included. Military, cabin crew and service personnel data were excluded. Independent data extraction was conducted, and study quality assessed. Standardized ratios were pooled using a fixed effects model and expressed with 95% confidence intervals. 75 studies were assessed for eligibility from which 6 involving 129 374 licensed CCC were included in the final analysis: Two reported incidence only, 1 incidence and mortality and 3 reported mortalities only. The pooled SIR for PCa in CCC was 1.41 (95% CI 1.17 to 1.71) with moderate heterogeneity (I2 = 53%) however, the pooled SMR was not statistically significant (1.08; 95% CI 0.94 to 1.24) also with moderate heterogeneity (I2 = 70%). The available evidence shows that CCC are at a higher risk of developing PCa but there is no evidence to suggest a similarly higher risk of death from the disease. The effect of early detection through PSA testing in this cohort is unclear. Occupational exposure to radiation and sleep disturbance may play a role, but clear evidence of additional risk is lacking. Our review indicates that most evidence is dated and to confidently assess contemporary health outcomes of CCC, further research is required.

Business analysis and modelling of flight delays using artificial intelligence

In the current day, with the growth of technology the number of people travelling by flights has increased. Consistently a significant number of flights are delayed or crossed out because of numerous of reasons. These delays bother travelers. These delays also cost a lot to the aircraft organization. Flight delays negatively affect carriers, air terminals and travelers. There are different methodologies used to manufacture flight delays expectation models from the Data Science point of view. The key resource of a flight includes aircraft, cockpit crew and cabin crew. For purposes of dispatching resources effectively, the three resources may be distributed independently. If the initial flight of a flight plan is delayed due to bad weather or other factors, it may result in the delays of the directly downstream flights that need to await its resources. If the delays continue to spread to the lower downstream flights, it may result in large area delay propagation. The method proposed here introduces and summarizes the initiatives used to address the flight delay prediction problem, according to scope, data and computational methods, giving special attention to an increasing usage of machine learning methods.

The Airblue Flight 202: Communication and Decision Making in Crisis

The case study on Airblue chronicles the events that led to the biggest plane crash in the aviation history of Pakistan. Airblue flight 202 destined from Karachi to Islamabad crashed while attempting to land under adverse weather conditions. The flight carried 146 passengers and 6 crew members; all died in the crash. Later investigations identified gross errors in judgment and decision making by the cockpit crew and unprofessional attitude as the sole cause of the crash. The case study documents the events that unfolded prior to the crash and identifies a number of major factors that are important while communicating and making decisions under critical situations. It touches upon the themes of superior-subordinate communication and relationships, empowerment, sense of responsibility under life-threatening situations, making sense of crises, the role of initiative, self-confidence, and reliance on others. The case is suitable to be taught on topics dealing with communication, decision making, teamwork, and leadership in crises, at the graduate level (MBA) and in executive training courses on team building, leadership and crisis management. It highlights the situations which emerge as crises and where teamwork is paramount for the effective achievement of goals. Furthermore, it focuses on the possible relapses when effective teamwork and communication are not in play.

A Data-drive Approach for Robust Cockpit Crew Training Scheduling

This work addresses the cockpit crew training scheduling problem. The objective is to produce a robust cockpit crew training schedule, including the assignment of trainees, instructors and simulators. To attain this objective, we propose a scheduling framework composed of four modules: a Training Scheduling & Assignment Model (TS&AM), a Disruption Generator (DG), a Rule-Based Recovery (RBR) algorithm, and a Neural Network (NN). The TS&AM is an integer programming model that integrates the scheduling of courses and the assignment of resources. The output roster serves as input for a data-driven DG based on Monte-Carlo Simulation. The disruptions are then solved using the RBR algorithm. Finally, The NN feedback algorithm learns the recovery costs experienced in the disruption impact simulator and updates these costs in the TS&AM to generate more robust rosters. The proposed modelling framework was calibrated, tested, and demonstrated in a simulation environment developed using four years of historical crew training data from a major European airline. The experiment showed that our approach outperformed the roster produced by the airline. The approach proposed produces rosters that reduce recovery costs by 21 percent, while still decreasing total training costs by 3 percent.

Cockpit crew pairing Pareto optimisation in a budget airline

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

A critical approach to fatigue risk factors in cockpit and cabin crew for the flight safety in aviation industry

This paper aims to develop a critical approach to flight safety by assessing theoretical and empirical studies on fatigue risk factors in cockpit and cabin crew. This paper also builds a fundamental basis for managing fatigue risk factors in the aviation industry. The main contribution of the paper demonstrates the fact that primary and secondary fatigue risk factors in cockpit and cabin crew affect the level of job satisfaction, operational efficiency, and flight security.

Examining the relationship between burnout and job satisfaction of flight crew

Fatigue as an emerging flight safety issue in the aviation industry requires an elaborate understanding and critical approach for proactive aviation management practices. The level of flight crew stress and fatigue must be critically managed to prevent flight accidents. Additionally, stress and fatigue have a negative influence on job satisfaction levels. This paper aims to examine the critical fatigue risk factors that affect the performance and safety of airline pilots and crew in the aviation industry. This paper also analyses the relationship between burnout and job satisfaction sub-dimensions. A factor analysis with a target population of 254 international flight crew has been conducted using the Minnesota Job Satisfaction Survey and Maslach Burnout questionnaire. The main findings of the study demonstrate that (i) cockpit and cabin crews’ job satisfaction and performance have been affected by stress and fatigue, (ii) psychological depression, anxiety and personal problems of the flight crew are the main causes of emotional fatigue, (iii) extensive flight hours and dealing with problematic passengers increase flight crew fatigue, (iv) personal achievements concerns and depersonalization increase flight crew fatigue.

Expert assessment of the cockpit crew information and control field

This paper discusses the method of expert assessment based on the probabilistic approach of the paired comparisons method. This mathematical apparatus is the basis for special software integrated into the universal stand for prototyping the cockpit, which allows for a systematic expert assessment at this stand according to pre-selected criteria with the control of the consistency of the answers. Using this method, the task of determining the feasibility of displaying layers of synthetic and enhanced vision on the flying frame is solved, but in general, the method allows evaluating any elements of the displays and controls.

Airline crew scheduling: Models and algorithms

The airline crew scheduling problem has become a crucial but challenging task for commercial airlines for decades. Airlines are operating with two types of air crew: cockpit crew and cabin crew. Due to the unique operating characteristics, the scheduling problems for these two crew types are very different. Besides, according to the planning stage, the airline crew scheduling problem can be classified as tactical planning problems (traditional scheduling and robust scheduling, weeks or months before the actual operations) and operational planning problems (recovery, after disruptions have occurred during the operational stage). Realizing the significance of the airline crew scheduling problems and a lack of review on the modelling and algorithmic advancements in terms of each crew type and planning stage, we develop this paper to review the related literature from four aspects: the scheduling for cabin crew, the scheduling for both cabin crew and cockpit crew, the robust scheduling for cockpit crew, and the recovery for cockpit crew. For each stream, we examine a number of prior representative studies to review the advancements in model development and solution algorithm construction to generate insights. Finally, we conclude the review by proposing a future research agenda for the airline crew scheduling problem.

Integrated Design and Verification of Civil Aircraft Cockpit Crew Alerting System Based on System Engineering

The cockpit crew alerting system is an important part of the modern cockpit and the only channel for the flight crew to obtain the status of the aircraft system. With the development of avionics, aircraft systems are becoming more and more complex, and more and more information needs to be obtained by the flight crew through the cockpit crew alerting system. So, how to make the flight crew correctly and efficiently obtain the required information becomes the design difficulty of the cockpit crew alerting system. This paper applies system engineering theory and methods to propose a forward design and verification method for the cockpit crew alerting system. This method reduces design changes due to incomplete demand capture, reduces product development costs, and provides a reference for the integrated design and airworthiness verification of the cockpit crew alerting system in the future.

Many-objective low-cost airline cockpit crew rostering optimisation

This research addresses the cockpit crew rostering problem in a low-cost airline. Through interviews with the aircrew planner, four objectives are considered vital for effective roster preparation; minimisation of nautical mile cost, balance workload among cockpit crews, maximisation of preferential requests from senior pilots and minimisation of the number of repeated flight patterns flown by individual pilots. Since the problem is NP-hard with many conflicting objectives that need to be optimised simultaneously, multi-objective evolutionary optimisation is an effective technique to solve this problem. As a result, the hybridisation between MOEA/D and HBMO algorithms, namely MOEA/D-HBMO, is developed. The proposed algorithm is compared to MOEA/D and HBMO. It is observed that MOEA/D-HBMO outperforms the counterparts in convergence related metrics and it could discover the best extreme points to meet every objective.