Search Results (31)

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

Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction. 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

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

This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience. Full article

Based on the projected growth in passenger air traffic and the need for better utilization of existing capacities, the level of service (LOS) concept in the design and planning of airport terminal facilities is crucial. By monitoring and quickly responding to expected changes in passengers’ and airlines’ needs, better utilization of airport terminal facilities in the passenger terminal can be achieved. The factors that influence the level of service (LOS) from the passenger perspective were evaluated in order to improve the user experience. Definitions of the level of service, key indicators of customer satisfaction, and a decision-making process using the analytical hierarchy process (AHP) method are described. A survey questionnaire was developed, passengers’ preferences were collected, and an analysis of the results was conducted. A hierarchical AHP decision-making model with associated criteria and sub-criteria was developed to determine the optimal level of service for low-cost carriers. Finally, by using the AHP model, new spatial–temporal parameters for the optimal level of service (LOS) for low-cost carriers (LCCs) are proposed, developed, and presented. The main objective is to adjust the existing LOS concept considering the business characteristics of low-cost carriers, in order to improve the efficiency of airport terminal facilities. Full article

Flight delays continue to pose a substantial concern in the aviation sector, impacting both operational efficiency and passenger satisfaction. Existing systems, while attempting to predict delays, often lack accurate predictive capabilities due to poor modeling setups, insufficient feature engineering, and inadequate feature selection processes, leading to suboptimal predictions and ineffective decision-making. Precisely forecasting flight arrival delays is essential for improving airline scheduling and resource allocation. The aim of our research is to create a superior prediction model that surpasses current modeling approaches. This study aims to forecast airline arrival delays by examining data from five prominent U.S. states in 2023—California (CA), Texas (TX), Florida (FL), New York (NY), and Georgia (GA). Our proposed modeling approach involves feature engineering to identify significant variables, followed by a novel feature selection algorithm (CFS) designed to retain only the most relevant features. Delay forecasts were generated using our proposed Deep Feed Forward Regression Network (DFFRN), a five-layer deep learning approach designed to enhance predictive accuracy by incorporating extensively selected features. The findings indicate that the DFFRN model substantially outperformed conventional models documented in the literature. The DFFRN had the highest R² score (99.916%), indicating exceptional predictive efficacy, highlighting the efficacy of the DFFRN model for predicting flight delays and establishing it as a significant asset for improving decision-making and minimizing operational delays in the aviation sector. Full article

The aim of this research is to examine the impact of bystander appraisal components, specifically congruence and relevance, on the intention to share negative experiences resulting from disruptive passengers on a flight. The investigation focuses on the mediating factors of negative emotions, satisfaction with service recovery, and trust in the airline. Employing a scenario-based approach, the detailed perceptions of passengers who may not have directly encountered disruptive behavior were explored. A total of 368 questionnaires were collected, and a structural equation model (SEM) was utilized to assess the relationship between bystanders’ appraisal and their intention to share. The results revealed that relevance positively correlated with negative emotions, and congruence significantly affected negative emotions. Moreover, negative emotions had a negative impact on both satisfaction with service recovery (SSR) and airline trust. Both SSR and airline trust, influenced by negative emotions, were found to negatively affect the intention to share. The results will help inform strategies to manage and mitigate in-flight disturbances, so that a high-quality cabin service can be maintained and the overall airline reputation does not suffer. Full article

This study aimed to investigate the level of client satisfaction among airline passengers and other users of Huambo’s airport in Angola. A quantitative method was used, based on a questionnaire addressed to airline passengers on their trips to Huambo and their use of Huambo’s airport. This sample comprises 619 questionnaire answers. As a result of the study, it is possible to relate client satisfaction with the size of the aircraft in question and with the ease of booking a trip. On the contrary, clients become more dissatisfied when the cost of the trip is higher. An analysis of the degree of client satisfaction among airline passengers shows three categories: the waiting time and service at the airline office, the comfort during the trip, and the empathy of the cabin staff. This study is expected to be useful to show the preferences of the clients of this African airport. Full article

While the cabin waste of an aircraft has been recognized and criticized, there is not enough research that explores its gravity in airlines and ways to reduce its total amount. This research aimed to highlight the importance of promoting online meal reservations for airline passengers as a way to minimize food waste and for airlines to offset the cost of tailoring inflight food options by revealing the inflight meal types and subcategories for which passengers are willing to pay an upgrade fee to support environmental and economic sustainability. A sample of 192 students from higher education in the U.S. completed an online questionnaire survey. Two groups with distinctive preferences for inflight meal upgrades were identified using exploratory factor analysis and cluster analysis. Participants were more likely to reserve inflight meals online when various inflight meal options were provided. They presented a higher willingness to pay more when a variety of foods was provided, with a lower willingness for healthy or religious foods. The highly educated participants in a younger age group and Asian/Pacific Islanders presented a significantly higher willingness to reserve meals online than the rest. The study results highlight the potential benefits of offering an online reservation option for inflight meals to reduce cabin waste and ensure passenger satisfaction. Full article

Monitoring customer satisfaction in the airline service industry is critical for improving service quality and meeting consumer expectations. Modern and comprehensive quality of service measurement tools offer firms critical information about how consumers perceive quality and their service quality expectations. It is vital to assess service quality in airline transportation, which is becoming more popular in comparison to other modes of transportation, resulting in increased competition. Businesses should know their clients well and make adjustments by properly analyzing their expectations if they want to compete in the market and enhance the quality of their services. As a consequence of this, we decided to utilize a model called the analytical hierarchy process (AHP) in order to determine how passengers in Saudi Arabia evaluate the level of service that is offered by airlines. Using the analytic hierarchy process (AHP) approach to model the five SERVQUAL dimensions and 22 sub-criteria, the purpose of the study effort was to determine the criteria for improving airline services. For the purpose of the study, the service from three different airlines was chosen and assessed based on their overall quality performance. Systematically, the AHP-based approach is presented for rating the airlines according to the Saudi aviation services. According to the observations, airlines should focus more on reliability, assurance, responsiveness, and empathy and less on tangibles. The sub-criteria also state that the airlines’ top aim should be to deliver accurate services on the first try. According to the AHP analysis, Saudi Airlines ranked first, followed by flynas and then flydeal. The findings of this study have consequences for decisions about the effective monitoring of the total airline system in order to enhance the delivery of high-quality services that would increase customers’ pleasure, which is the aim of airline services. Full article

Most previous studies on airline fleet planning have focused solely on economic considerations, neglecting the impact of carbon reduction. This paper presents a novel method for green fleet planning, using a bi-level programming model to balance conflicts among stakeholders while considering uncertain parameters such as demand and operating costs. The upper model aims to reduce carbon emissions by taking into account government constraints, such as carbon allowances and carbon prices, as well as airline satisfaction. The lower model seeks to maximize airline revenue using a space-and-time network model based on given airline flight schedules. To verify the game model, a case study utilizing randomly generated scenarios is employed within the context of China’s aviation-specific emissions trading scheme. Results show that: (1) compared to the scenario without a policy aiming at reducing carbon emissions, this method reduces carbon emissions by 23.03% at the expense of a 6.96% reduction in terms of the airline’s operating profit; (2) when passenger demand levels increase to 160%, the profitability of the proposed fleet increases by 50.83%, while there were only insignificant changes in carbon emissions; (3) the proposed methodology can assist the airlines systematically to reduce carbon emissions and provide valuable strategic guidance for policy makers. Full article

The rapid progress in the competitive business environment has increased customer expectations and demands, thus leading to a situation where many companies are competing fiercely, especially low-cost airline companies or low-cost carriers (LCCs). The company carries out a strategy of providing satisfaction for its passengers to generate great loyalty. The purpose of this study is to find out what factors can affect passenger loyalty, such as service quality (SQ), price (P), customer satisfaction (S), and perceived value (PV) on LCC airlines in Indonesia. This study used quantitative methods through surveys which were conducted on 313 respondents in Indonesian, via online questionnaire data collection in July–August 2021. To test the relationship between variables, this study used statistical analysis with LISREL structural equation modelling (SEM). The present study was conducted concerning LCC companies in Indonesia and refers only to Indonesian consumers. There are 7 hypotheses in this study, of which 4 hypotheses are confirmed and 3 are invalidated. Some results obtained show that, in this case and with reference to Indonesian consumers only for the substitution service offered by LCCs, we have to discuss atypical manifestations of consumer behaviour (good SQ does not necessarily lead to increased S and lower price does not necessarily lead to increased S, etc.). At the same time, this study shows that consumer loyalty remains the essential factor for the survival of LCCs in the market. Full article

The aviation sector is constantly expanding and it is strongly influenced by external factors, which gives rise to significant competition between airlines. One of the most effective ways for an airline to gain a competitive advantage is to keep its customers as loyal users of the provided services, able to provide airlines with a stable revenue for the services they sell. The purpose of this paper was to identify the factors that have the greatest impact on airline passengers’ loyalty. This study analyzed the data from passengers of two classes: prestige (business) and economy. Prestige (business) passengers pay more attention to service quality (beverage and food selection, service and quality, especially accessibility, etc.), privacy, individual attention, and airline reliability, while economy class passengers focus on price and basic perception of service quality. Data were collected from 409 passengers who travelled from Lithuania airports (Vilnius, Kaunas, Palanga) to other countries. The results suggest that there are different factors of in-flight service quality that are important according to the customer seat class. The results indicate that 71% of passenger loyalty is explained by image and image is explained by satisfaction. In addition, service quality and price are found to have positive effects on satisfaction. Compared with price, service quality is found to be a stronger determinant of satisfaction. Full article

Airlines’ major adoption of digital technology during the COVID-19 crisis may have changed how customers experience the services and may affect passengers’ perceptions compared to the past. However, there is a lack of studies systematically examining the adoption of new technology in the airline industry from a passenger satisfaction-centric perspective. This study investigates passengers’ perceptions and satisfaction with digital technology adoption by airlines during the COVID-19 pandemic. An online questionnaire survey was conducted to examine Chinese passengers’ perceptions and satisfaction with 11 digital technology-based services offered by airlines. A total of 365 valid responses were analyzed using ANOVA tests and stepwise multiple linear regression analysis. The analysis indicates that most passengers have a positive attitude towards airlines’ new technology adoption. In the final selected regression model, six technologies offered by the airlines are statistically significant and have impacted passenger satisfaction. They are artificial intelligence (AI) customer service, e-luggage tag, cleaning robot, ultraviolet light and antimicrobial cabin cleaning, an app-controlled in-flight entertainment system, and e-library. The facial recognition service, digital documentation and AI Customer service are the least favorable among the 11 technologies offered by the airlines. There is an opportunity for airlines to improve these services further to gain the trust of the passengers. Full article