Search Results (148)

Effective predictive maintenance is crucial for ensuring aircraft reliability, reducing operational disruptions, and supporting spare part inventory management in airline operations. However, maintenance data is often sparse, with irregular observations, missing records, and imbalanced failure distributions, making accurate forecasting a significant challenge. This study proposes a data-driven framework for maintenance prediction under sparse observational data. We implement and compare two distinct methodologies: survival analysis via DeepHit for time-to-event prediction, and a latent space classifier with autoencoder backbone. Each method is evaluated on historical aircraft maintenance logs and component installation records, addressing challenges posed by limited and imbalanced datasets. Both models are trained and tested on ten years of maintenance logs and component installation records sourced from an airline MRO (Maintenance, Repair and Overhaul) company that services a fleet of more than 500 aircraft, offering a realistic and scalable setting for fleet-wide maintenance analysis. The latent space classifier demonstrates superior overall performance and consistency across diverse components and prediction horizons compared to DeepHit, which is constrained by its sensitivity to probability thresholds. The encoder-based method effectively transfers knowledge from high-data components to those with sparse maintenance histories, enabling reliable maintenance forecasting and enhanced inventory planning for large-scale airline operations. Full article

The growing dominance of low-cost carriers (LCCs) in global air transport has intensified the need for airport terminal facilities that reflect their simplified, efficiency-driven operating principles. Traditional Level of Service (LOS) standards, based on International Air Transport Association’s Airport Development Reference Manual (IATA ADRM), were primarily designed for traditional air carriers or full-service network carriers (FSNCs) and may lead to over-dimensioned or misaligned airport terminal facilities when applied to airports with dominance of LCCs. This study presents the first newly developed computational tool called Airport Terminal Facilities Software (ATFS) as a methodological and conceptual advance in airport terminal planning, that integrates LOS guidelines differentiated by airline business models. The methodology integrates spatial–temporal LOS parameters, specific facility capacity formulas, and peak-hour demand calculations of airport terminal facilities. Results from the case study conducted at Pula Airport show substantial differences between IATA and LCC LOS outcomes, i.e., applying LCC LOS guidelines can significantly reduce required areas for the several airport terminal facilities. Findings confirm that new LCC LOS guidelines and the ATFS tool can optimize airport terminal facilities, reduce or reconfigure excessive or empty space, and improve passenger flow efficiency at LCC-dominant airports. Full article

This study presents an integrated optimization framework for fleet assignment, flight scheduling, and aircraft routing under uncertainty, addressing a core challenge in airline operational planning. A three-stage stochastic mixed-integer nonlinear programming model is developed that, for the first time, simultaneously incorporates station purity constraints, codeshare agreements, and overbooking decisions. The formulation also includes realistic operational factors such as stochastic passenger demand and non-cruise times (NCT), along with adjustable cruise speeds and flexible departure time windows. To handle the computational complexity of this large-scale stochastic problem, a Sample Average Approximation (SAA) scheme is combined with two tailored metaheuristic algorithms: Simulated Annealing and Cuckoo Search. Extensive experiments on real-world flight data demonstrate that the proposed hybrid approach achieves tight optimality gaps below 0.5%, with narrow confidence intervals across all instances. Moreover, the SA-enhanced method consistently yields superior solutions compared with the CS-based variant. The results highlight the significant operational and economic benefits of jointly optimizing codeshare decisions, station purity restrictions, and overbooking policies. The proposed framework provides a scalable and robust decision-support tool for airlines seeking to enhance resource utilization, reduce operational costs, and improve service quality under uncertainty. Full article

Designing cost-effective, reliable diagnostic sensor suites for complex assets remains challenging due to conflicting objectives across stakeholders. A holistic framework that integrates the Normalised Diagnostic Contribution Index (NDCI)—which scores sensors by separation power, severity sensitivity, and uniqueness—with a Multi-Objective Sensor Optimisation Framework (MOSOF) is presented. Using a high-fidelity virtual aircraft model coupling engine, fuel, electrical power system (EPS), and environmental control system (ECS), NDCI against minimum Redundancy-maximum Relevance (mRMR) is benchmarked under a rigorous nested cross-validation protocol. Across subsystems, NDCI yields more compact suites and higher diagnostic accuracy, notably for engine (88.6% vs. 69.0%) and ECS (67.7% vs. 52.0%). Then, a multi-objective optimisation reflecting an airline use-case (diagnostic performance, cost, reliability, and benefit-to-cost) is executed, identifying a practical Pareto-optimal ‘knee’ solution comprising 12–14 sensors. The recommended suite delivers a normalised performance of ≈0.69 at ≈USD36k with ≈145 kh MTBF, balancing the cross-subsystem information value with implementation constraints. The NDCI-MOSOF workflow provides a transparent, reproducible pathway from raw multi-sensor data to stakeholder-aware design decisions, and constitutes transferable evidence for model-based safety and certification processes in Integrated Vehicle Health Management (IVHM). The limitations (simulation bias, cost/MTBF estimates), validation on rigs or in-service fleets, and extensions to prognostics objectives are discussed. Full article

Flight delay serves as a pivotal metric for assessing service quality in the aviation industry. Accurately estimating flight delay duration is increasingly acknowledged as a cornerstone of aviation management, with significant implications for operational efficiency, passenger satisfaction, and economic outcomes. Most existing approaches often focus on single airports or airlines and overlook the complex interdependencies within the broader aviation network, limiting their applicability for system-wide planning. To address this gap, this study proposes a novel integrated framework that combines deep learning and complex network theory to predict flight arrival delay duration from a multi-airport and multi-airline perspective. Leveraging Bayesian optimization, we fine tune hyperparameters in the XGBoost algorithm to extract critical aviation network features at both node (airports) and edge (flight routes) levels. These features, which capture structural properties such as airport congestion and route criticality, are then used as inputs for a deep kernel extreme learning machine to estimate delay duration. Numerical experiment using a high-dimensional flight dataset from the U.S. Bureau of Transportation Statistics reveals that the proposed framework achieves superior accuracy, with an average delay error of 3.36 min and a 7.8% improvement over established benchmark methods. This approach fills gaps in network-level delay prediction, and the findings of this research could provide valuable insights for the aviation administration, aiding in making informed decisions on proactive measures that contribute to the sustainable development of the aviation industry. Full article

This study examines the structural relationships among rapport, trust, psychological capital (PsyCap), and organizational citizenship behavior (OCB) in the emotionally demanding work context of airline cabin crews. Grounded in the job demands–resources (JD-R) model and social exchange theory (SET), we propose and test a sequential mediation model in which rapport is positively associated with trust, trust is positively associated with PsyCap, and PsyCap is positively associated with OCB. Based on survey data from 248 South Korean flight attendants, structural equation modeling (SEM) demonstrates that rapport is indirectly associated with OCB through the sequential mediation of trust and PsyCap, rather than displaying a significant direct association. The findings indicate that rapport functions not merely as an immediate behavioral driver but as a relational asset that is linked to the psychological capacities essential for sustainable organizational behavior. This study contributes to the theory by integrating JD-R and SET perspectives to explain how relational and psychological resources are jointly related to OCB. While the primary focus was on mediation, future research should test potential moderation effects, such as how job demands or emotional labor may shape the strength of these pathways—aligning with the JD-R model’s interactive assumptions. Practically, the results suggest that airline organizations and other service industries can promote sustainable human resource management by cultivating trust-based relational capital and strengthening employees’ PsyCap through targeted training, mentoring, and supportive leadership practices. These insights extend beyond aviation to other service sectors characterized by high emotional labor demands, offering a pathway to strengthen human resource sustainability and organizational social sustainability. Full article

The aviation sector faces increasing pressure to address climate change as its contribution to global CO₂ emissions continues to rise. This study investigates how passengers’ awareness of environmental issues and perceptions of sustainable airline practices affect their Green Air Travel Behavior (GTB). Drawing upon the Theory of Planned Behavior (TPB) and extending it with constructs such as Environmental Awareness (EA), Perceived Service Quality (PSQ), and Green Trust (GT), the research examines their impact on GTB. Using a quantitative design, data were collected from 300 airline passengers and analyzed with Structural Equation Modeling (SEM). Results reveal that EA strongly influences PSQ, GT, Attitude (ATT), and Intention (ITN), highlighting its role as a key antecedent. PSQ significantly enhances GT, while both GT and ATT directly predict GTB. However, the effect of ITN on GTB was not significant, indicating an intention–behavior gap. The findings underscore the importance of awareness, trust, and service quality in promoting sustainable air travel, while also pointing to barriers that hinder intentions from becoming actions. Theoretically, the study extends TPB within green aviation, and practically, it provides guidance for airlines and policymakers seeking to advance SDG 13: Climate Action through sustainable air travel strategies. Full article

In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article

As low-cost carriers (LCCs) continue expanding their networks and enhancing profitability through connecting services, passenger demand has become a critical factor in flight connection planning. However, demand is inherently uncertain due to economic cycles, seasonal fluctuations, and external disruptions, creating challenges for network design. This study proposes a flight connection planning model tailored to LCC operations that explicitly accounts for demand uncertainty. The model determines the optimal set of connecting itineraries to introduce over the existing network of flights, identifies promising transfer airports, and provides passenger allocation strategies across flights. We apply the model to Spring Airlines’ real-world network to evaluate its effectiveness. Results show that the proposed model outperforms the deterministic benchmark in feasibility and stability under varying demand scenarios. Specifically, under the same constraint of selecting up to 10 transfer airports, our model increases the number of connecting itineraries by 59.5% compared to the deterministic model and achieves a more balanced passenger distribution. Across 10 representative demand scenarios, the average standard deviation of load factors is reduced by 26.1% compared to the deterministic benchmark. Moreover, the deterministic solution yields a 22.9% failure rate for planned connections, while our model maintains 100% feasibility. These findings highlight the model’s value as a resilient, practical decision-support tool for airline planners. Full article

Flight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes scorecards as an innovative and simplified approach to forecast flight delays. Historical flight data from the United States were used, incorporating variables such as departure and arrival times, flight routes, aircraft types, and other factors related to delay. Exploratory data analysis identified key variables influencing delays, and scorecards were constructed by assigning weights, normalizing, and scaling variables to improve interpretability. The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. The results indicate that scorecards provide accurate and interpretable predictions of flight delays. This method facilitates the identification of critical factors that contribute to delays and allows for an estimation of their likelihood and duration. Scorecards offer a practical tool for airlines and airport operators, potentially enhancing decision-making processes, reducing delay-related costs, and improving service quality. Future research should explore the integration of scorecards into operational systems and the inclusion of additional variables to increase model robustness and generalizability. 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

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

This paper introduces a token-based digital currency (TBDC) model for standardizing service delivery in an aviation technical support as a service (ATSaaS) platform. The model addresses the challenges of service standardization and valuation by integrating cost, time, and quality parameters into a unified framework. Unlike traditional cryptocurrencies, this specialized digital currency incorporates intrinsic service valuation mechanisms that dynamically reflect the worth of aviation technical support services. The research presents a mathematical formulation for token value calculation, including a Service Passport framework for comprehensive documentation and a systematic approach for service integration. The model is validated through a numerical case study focusing on maintenance, repair, and overhaul services, demonstrating its effectiveness in generating fair token values across diverse service types. The study introduces optimization techniques using machine learning to enhance token calculations, successfully standardizing heterogeneous services while maintaining flexibility and transparency. Implementation challenges and future developments are identified. The TBDC model provides a foundation for transforming aviation technical support services, particularly benefiting small airlines through improved efficiency, standardization, and accessibility. Full article

This research introduces a novel framework that allows the comparative evaluation of airlines based on passengers’ flight experiences. The proposed framework combines a typical and a simulation-based extension of the AHP in a group decision-making environment to elicit rankings of various airlines. The first option (T-AHP) generates rankings by combining individual passengers’ preferences using the geometric mean synthesis rule. The second option (S-AHP) simulates the stochastic characteristics of the responses, aiming to handle the inherent uncertainty and the variety of preferences obtained by the customers. The rankings are derived by mapping the decision space according to the evaluation criteria implemented and passengers’ preference dimensions. The proposed options are illustrated through a case study where four airlines are evaluated using 51 satisfaction dimensions (sub-criteria). Although the derived results indicate similar rankings, those obtained by the S-AHP option are more stable and robust, with greater discriminatory capacity compared to those of its typical counterpart (T-AHP). Full article

In this paper, developed in the context of the Clean Sky 2 project SIENA (Scalability Investigation of hybrid-Electric concepts for Next-generation Aircraft), an extensive analysis is carried out to identify and accelerate the development of innovative propulsion technologies and architectures that can be scaled across five aircraft categories, from small General Aviation airplanes to long-range airliners. The assessed propulsive architectures consider various components such as batteries and fuel cells to provide electricity as well as electric motors and jet engines to provide thrust, combined to find feasible aircraft architectures that satisfy certification constraints and deliver the required performance. The results provide a comprehensive analysis of the impact of key technology performance indicators on aircraft performance. They also highlight technology switching points as well as the potential for scaling up technologies from smaller to larger aircraft based on different hypotheses and assumptions concerning the upcoming technological advancements of components crucial for the decarbonization of aviation. Given the considered scenarios, the common denominator of the obtained results is hydrogen as the main energy source. The presented work shows that for the underlying models and technology assumptions, hydrogen can be efficiently used by fuel cells for propulsive and system power for smaller aircraft (General Aviation, commuter and regional), typically driven by propellers. For short- to long-range jet aircraft, direct combustion of hydrogen combined with a fuel cell to power the on-board subsystems appears favorable. The results are obtained for two different temporal scenarios, 2030 and 2050, and are assessed using Payload-Range Energy Efficiency as the key performance indicator. Naturally, introducing such innovative architectures will face a lack of applicable regulation, which could hamper a smooth entry into service. These regulatory gaps are assessed, detailing the level of maturity in current regulations for the different technologies and aircraft categories. Full article