Search Results (64)

Departure punctuality (KPI01) and ATFM slot adherence (KPI03) have been emphasized by the International Civil Aviation Organization as key performance indicators (KPIs) in the Global Air Navigation Plan. To address the inherent conflict between these two objectives in departure scheduling, a multi-objective optimization model is proposed that aims to simultaneously enhance departure punctuality, ATFM slot adherence, and taxiing efficiency. A simulated annealing algorithm based on a resource transmission mechanism was developed to solve the model effectively. Based on full-scale operational data from Nanjing Lukou International Airport in June 2023, the empirical results confirm the model’s effectiveness in two primary dimensions: (1) Significant improvement in taxiing efficiency: The average unimpeded taxi-out time was reduced by 6.4% (from 17.2 to 16.1 min). The number of flights with taxi-out times exceeding 30 min decreased by 58%. For representative taxi routes (e.g., stand 118 to runway 6), the excess taxi-out time was reduced by 82.3% (from 5.61 to 1.10 min). (2) Enhanced operational punctuality: Departure punctuality improved by 10.7% (from 67.9% to 78.7%), while ATFM slot adherence increased by 31.2% (from 64.6% to 95.8%). This study presents an innovative departure scheduling approach and offers a practical framework for improving collaborative operational efficiency among airports, air traffic management units, and airlines. Full article

This study evaluates the feasibility and benefits of introducing battery-powered hybrid electric aircraft (HEA) into regional airline operations. Using 2019 U.S. domestic flight data, the ERJ175LR is selected as a representative aircraft, and several HEA variants are designed to match its mission profile under different battery technologies and power management strategies. These configurations are then tested across over 800 actual daily flight sequences flown by a regional airline. The results show that well-designed HEA can achieve 3–7% fuel savings compared to conventional aircraft, with several variants able to complete all scheduled missions without disrupting turnaround times. These findings suggest that HEA can be integrated into today’s airline operations, particularly for short-haul routes, without the need for major infrastructure or scheduling changes, and highlight opportunities for future co-optimization of aircraft design and operations. Full article

This study applies data envelopment analysis (DEA) to evaluate the economic sustainability of U.S. airlines by examining labor efficiency as a pivotal component of cost management and long-term sustainability. Focusing on five key employee categories—pilots, flight attendants, ground staff, maintenance staff, and management—the analysis uses data from the MIT Airline Data Project spanning 2007 to 2020 to calculate relative efficiency scores for fifteen major airlines. The findings reveal significant disparities in labor efficiency across different airline sectors, particularly highlighting challenges in managing cost-intensive roles, such as ground, maintenance, and management staff. Notably, Southwest Airlines consistently demonstrates strong economic sustainability through its efficient labor practices, while carriers including United, jetBlue, Alaska, and Hawaiian Airlines exhibited marked inefficiencies in 2020, indicating a critical need for operational improvements. This research contributes to the field of airline management by linking labor efficiency metrics with broader economic sustainability objectives, thereby offering strategic insights for enhancing cost-effectiveness and ensuring the long-term financial health of the industry. Full article

This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they are needed most. It also shows how precise forecasting is not just about saving costs, it is about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike. Full article

The airline industry is currently navigating a pivotal period characterized by rapid development and increasing global pressure to reduce carbon emissions. Airlines, as the first to be significantly impacted, must actively manage their carbon footprints, adopt carbon abatement technologies, and address the inherent risks in this transformation. This paper examines the risk factors correlated with the technology transformation of carbon abatement and proposes effective abatement strategies. Using panel data of China Southern Airlines from 2009 to 2023 and applying the Logarithmic Mean Divisia Index (LMDI) method based on the Kaya identity, we analyze the differential impacts of various factors on unit carbon emissions. Multiple scenarios, derived from the influences of these factors, are constructed, and the Monte Carlo algorithm is employed to simulate the impact and volatility of correlated risks in the technology transformation for the abatement of carbon emissions. The findings are as follows: on the one hand, carbon emissions are strongly driven by energy consumption (0.99), flight volume (0.941), flight hours (0.931), transportation turnover (0.923), and take-off frequency (0.833). On the other hand, technology (56%) and scale (54.74%) significantly reduce unit carbon emissions, while take-off frequency negatively impacts emissions (−35.19%). Technology-related risks are controllable and relatively stable, whereas scale-related risks are highly uncertain. Additionally, operation-related risks can be partially hedged to ensure a certain level of risk controllability. Full article

Airport slot allocation is a key short-term solution to address airport capacity constraints, and it has long been a focus of research in the field of air traffic management. The existing studies primarily consider constraints such as airport capacity and flight operations, optimizing the slot allocation of arrival and departure flights to maximize the utilization of airport resources. This study proposes an airline fairness index based on a demand-side value system and addresses the problem of flight slot allocation by developing a tri-objective model. The model simultaneously considers the maximum slot deviation, total slot deviation, and airline fairness. Additionally, dynamic capacity constraints using rolling time windows and constraints on slot migration during peak periods are incorporated. The $\epsilon$-constraint method is employed in conjunction with a large-neighborhood search heuristic to solve a two-stage optimization process, yielding an efficient allocation scheme. The experimental results show that the introduction of rolling capacity constraints effectively resolves the issue of continuous overcapacity that arises when only a fixed capacity is considered. Additionally, the proposed airline fairness index, based on a demand-side value system, can significantly improve fairness during the slot allocation process. By sacrificing at most 16% of the total displacement, it is possible to reduce the unfairness index by nearly 80%. Full article

The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time and historical data from diverse sources, including aircraft sensors, maintenance logs, and operational records. The proposed ecosystem enables predictive analytics and anomaly detection, enhancing decision-making processes for airlines, maintenance, repair, and overhaul providers, and regulatory bodies. Key elements of the ecosystem include a modular design with feedback loops, scalable AI models for predictive maintenance, and robust data-sharing frameworks. This paper outlines the architecture of a unified aviation maintenance ecosystem built around multiple data sources, including aircraft sensors, maintenance logs, flight data, weather data, and manufacturer specifications. By integrating various components and stakeholders, the system achieves its full potential through several key use cases: monitoring aircraft component health, predicting component failures, receiving maintenance alerts, performing preventive maintenance, and generating compliance reports. Each use case is described in detail and supported by illustrative dataflow diagrams. The findings underscore the transformative impact of such an ecosystem on aviation maintenance practices, marking a significant step toward safer, more efficient, and sustainable aviation operations. Full article

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport terminals. By simulating different scenarios, the model aims to provide valuable insights into how to effectively handle crowd dynamics and enhance overall terminal efficiency, safety, and sustainability. This case study was conducted at Henri Coanda International Airport, ARENA 12 simulation software being used in order to model the passenger flows within the airport terminal. Two scenarios were considered: The first one involves maintaining a fixed number of security and check-in desks for the two airline groups. In contrast, the second scenario allows for a variable number of security and check-in desks for the same airline groups. By optimizing resource allocation and minimizing waiting time, this model contributes to more sustainable airport management operations. Three measures of performance (MOPs) were selected to assess the system activity: the average passenger waiting time, the average passenger number queue length, and the average utilization rate. Comparing the results, we concluded that the second scenario shows a relative improvement in almost all performance measures when compared to the first scenario. Full article

Environmental, social, and governance (ESG) factors are crucial in evaluating a company’s value. High ESG scores reflect ethical practices, social responsibility, and effective governance. This paper examines the impact of ESG criteria on firm value within the airline industry, focusing on their influence on operational efficiency, risk reduction, and financial performance. Using panel data analysis, the study evaluates ESG scores from 32 airline companies over the period of 2018–2023, with an explanatory power of 36.5%. The research explores how integrating environmental, social, and governance factors into strategic management can foster sustainable competitive advantage. It focuses on utilizing internal resources, meeting the needs of various interested parties, and balancing financial, social, and environmental performance. The findings indicate that while ESG practices enhance firm value through improved efficiency and risk management, they do not always lead to higher short-term firm value. Moreover, the study underscores the significance of governance in the airline industry, where robust governance structures can mitigate risks but may also increase costs. This research contributes to the literature by providing empirical evidence of the link between ESG performance and firm value in the airline industry, emphasizing the importance of integrating ESG principles into strategic management for long-term sustainability and financial success. Full article

The Airbus A380 was initially expected to replace existing aircraft due to its remarkable fuel efficiency on long-haul routes when operating with a full passenger load. However, recent changes in the commercial aviation environment have resulted in a decrease in demand for four-engine aircraft. Rising fuel prices have pushed airlines to focus on more efficient operations, while manufacturers prioritize producing advanced twin-engine aircraft. The debate over the long-term economic viability of A380 operations remains ongoing. This study compares and evaluates the fuel efficiency of the Airbus A380 and the Airbus A350 using actual flight data. The analysis employs a fuel efficiency prediction model to compare scenarios based on identical payload and load factor. Results indicate that the A350 is approximately twice as fuel efficient as the A380 under the same payload and about 1.34 times more efficient under the same load factor. The A380’s economic viability is analyzed by considering the balance between revenue per available ton-kilometer (RASK) and cost per available ton-kilometer (CASK). If the A380’s RASK is significantly higher than 1.34 times the A350’s or exceeds its own CASK, it can sustain operations. Achieving a balance between RASK and CASK is essential for the economic sustainability of A380 operations. Full article

The global air transportation system continues to be greatly impacted by operational changes induced by the COVID-19 pandemic. As air traffic management (ATM) focuses on balancing system capacity with demand, many facets of ATM and system operations more broadly were subjected to dramatic changes that deviate from pre-pandemic procedures. Since the start of the COVID-19 pandemic when air travel became one of the first transport modes to be impacted by lockdown procedures and travel restrictions, a geographically diverse cohort of researchers began investigating the impacts of the COVID-19 pandemic on air navigation service providers, airline and airport operations, on-time performance, as well as airline network structure, connectivity, crew scheduling, and service impacts due to pilot and crew shortages. In this study, we provide a comprehensive review of this aforementioned body of research literature published during one of the most tumultuous times in the history of aviation, specifically as it relates to air traffic management and air traffic control. We first organize the reviewed literature into three broad categories: strategic air traffic management and response, air traffic control and airport operational changes, and air traffic system resilience. Then, we highlight the main takeaways from each category. We emphasize specific findings that describe how various aspects of the air transportation systems could be improved in the domestic and global airline industry post-COVID. Lastly, we identify specific changes in operational procedures due to the COVID-19 pandemic and suggest future industry trends as informed by the literature. We anticipate this review article to be of interest to a broad swath of aviation industry and intercity transportation audiences. Full article

The consequences of new airspace entrants, such as novel aircraft concepts with innovative propulsion systems, on air traffic management operations need to be carefully identified. This paper aims to assess the impact of future aircraft with different performance envelopes on the European air traffic network from a flow-based perspective. The underlying approach assumes that all certification-related questions concerning airworthiness have been resolved and do not take into account any economic factors related to airline operations. For example, for an innovative propulsion system, a short range all-electric aircraft is considered in this study. Aircraft trajectory calculations are based on the dataset of base of aircraft data (BADA), which are developed and maintained by EUROCONTROL. The new design concept is integrated into BADA as well, resulting in a new set of coefficients for the all-electric aircraft. In addition to the adjusted parameters which affect airborne performances, ground-related aspects are also taken into account. This includes assumptions on operational procedures, charging capacities and adaptions in infrastructure. Investigations are carried out at the trajectory level as well as at the airport and the entire network. Full article

In recent years, the rapid growth of air traffic has intensified pressure on the air transport system, leading to congestion problems in airports and airspace. The projected increase in demand exacerbates these issues, necessitating immediate attention. Additionally, there is a growing concern regarding the environmental impact of the aviation sector. To tackle these challenges, the adoption of advanced methods and technologies shows promise in expanding current airspace capacity and improving its management. This paper presents an overview of sustainable aviation, drawing on publications from academia and industry. The emphasis is on optimizing both flight and ground operations. Specifically, the review delves into recent advancements in airline operations, airport operations, flight operations, and disruption management, analyzing their respective research objectives, problem formulations, methodologies, and computational experiments. Furthermore, the review identifies emerging trends, prevailing obstacles, and potential directions for future research. Full article

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This attitude towards maximizing throughput calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays at peak hours. The optimization model is used to make better scheduling intervention decisions considering airport resource constraints and safety of operation. A simulation algorithm is also developed to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those of the optimization model. Computational results from a case study demonstrate that a reduction of 52.6% and 61% in total delay times for arrival and departure flights, respectively, can be achieved. The optimization model also facilitates the implementation of a collaborative decision-making system for better coordination of airport traffic flow management with commercial airlines. Full article

As aviation technology advances, numerous new aircraft enter the market. These not only offer airlines technological and fuel efficiency advantages but also present the challenge of how to conduct pilots’ aircraft-type transition training efficiently and economically. To address this issue, this study designed a methodology to quantitatively assess the similarity in panel display control design and standard operating procedures (SOPs) between aircraft types. Then, by combining the results of a questionnaire survey on A320, A330, B737, and B777 transition training and training cost data, it was verified quantitatively that inter-aircraft similarity has a positive impact on reducing the difficulty and cost of transition training. Taking the similarity in aircraft types as a feature, the KNN algorithm was used to successfully construct a difficulty prediction model for the training program of aircraft-type transition training. To overcome the limitation of insufficient training cost data volume, this study adopts the transfer learning method to construct a prediction model of the transition training cost, and the final significant prediction accuracy proves the effectiveness of the method. The research in this paper not only provides strong data support for the resource planning and cost management of airlines’ aircraft-type transition training but also provides new research perspectives and methodological guidance for the field of aviation training. Full article