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Integrating AI support into a framework for collaborative decision-making (CDM) for airline disruption management
Abstract
Airlines are regularly confronted with disruptions that interfere with their flight operations, resulting in financial losses and lower operational performance. While collaborative decision-making (CDM) is a commonly used approach to mitigate these airline disruptions, it is unclear how artificial intelligence (AI) can support CDM to manage airline disruption. This study's purpose is to identify how the adoption of AI can support CDM to mitigate operational flight disruptions. Using a theory building approach, this conceptual paper advances the literature in aviation management by delineating the relationship between AI and CDM in the context of airline disruption management. First, we propose an AI–CDM framework illustrating the factors that influence disruption management in airlines. Second, we highlight the implications of AI-supported CDM for disruption management in and for airlines. We found that to effectively use AI-supported CDM for disruption management, airlines need to a) introduce data-driven CDM, b) enable AI management of complex systems, and c) transform disruption management into AI-supported performance management. As one of the first studies linking AI and CDM, the framework provides a structured recognition of the role of AI in CDM and its implications for airline disruption management.
... Artificial Intelligence (AI) has become a cornerstone of Intelligent Transportation Systems (ITS). By harnessing the power of AI, ITS can process and analyze massive volumes of data from various sources, including traffic sensors, GPS, and surveillance cameras, for traffic signal control [1], car driver surveillance [2], autonomous driving [3], routing optimization [4], activity recognition [5], mode choice prediction [6], and multi-modal UAV classification [7], collaborative decision making [8] and airline efficiency [9]. Traditional AI approaches, while effective in many scenarios, often rely on structured data and require extensive feature engineering and domain-specific knowledge to address specific problems. ...
Large Language Models (LLMs), based on their highly developed ability to comprehend and generate human-like text, promise to revolutionize all aspects of society. These LLMs facilitate complex language understanding, translation, content generation, and problem-solving, enabled by vast historical data processing and fine-tuning. Throughout the past year, with the initial release of ChatGPT to the public, many papers have appeared on how to exploit LLMs for the ways we operate and interact with intelligent transportation systems. In this study, we review more than 130 papers on the subject and group them according to their major contributions into the following five categories: autonomous driving, safety, tourism, traffic, and others. Based on the aggregated proposals and findings in the extant literature, this paper concludes with a set of challenges and research recommendations, hopefully contributing to guide research in this young, yet extremely active research domain.
Airline schedules are easily affected by disruptions, leading to flight delays or (and) cancellations, causing significant financial losses to airline companies and inconvenience for passengers. When making recovery decisions, airlines need to simultaneously consider various entities, including flights, aircraft, and crew. This paper examines the integrated recovery policies for airlines to help re-schedule flights, re-route aircraft, and reassign crew members. To realize quick responses upon the occurrence of disruptions, an attention-based end-to-end deep reinforcement learning approach is proposed to learn a parameterized stochastic policy for the integrated airline recovery problem. Numerical experiments based on randomly generated disruption instances demonstrate that the proposed method outperforms the existing approaches and is applicable in realistic situations. The key insights obtained from our analyses are summarized as follows: (1) traditionally, among all disruption sources, it is most challenging and time-consuming to determine the recovery policies in reaction to aircraft delays and airport closures. However, the new approach developed in this study overcomes this difficulty and can provide high-quality recovery policies for aircraft delays and airport closures quickly. Thus, our work is especially valuable for airports and regions that suffer from frequent flight delays and closures, and can significantly improve their operational efficiency and service quality; (2) when traditional approaches are applied, the adoption of the well-known schedule robustness enhancement strategy ‘crew follow aircraft’ generally leads to high operations costs. Differently, our proposed approach can apply this strategy without encountering a significant cost growth. Therefore, airlines can fully leverage this strategy to gain additional advantages; (3) our developed new approach demonstrates high generality to accommodate various disruptions, which can benefit airlines and airports in the highly-volatile environment with various unpredictable events.
Despite the claim that artificial intelligence (AI) has the potential to increase efficiency in and for airlines, current literature is limited concerning models and frameworks to assess AI applications and their implications for airline efficiency. In response, we a) conceptualize and propose an AI-Airline-Efficiency-Model (AAEM) that allows for a more structured management approach for a systematic review and analysis of existing literature, and b) present a framework explicating the identified areas of AI application for airline efficiency based on a the AAEM model. In particular, using the four AI elements Machine Learning, Deep Learning, Reinforcement Learning and Natural Language Processing and their applications within six identified airline departments, we systematically review and analyze key attributes and characteristics of both AI and airline efficiencies to critically assess current research efforts. We found that AI applications are built around four overarching improvement areas predictive analytics, resource optimization, safety & autonomous processes and passenger experience, but lack a cross-department and inter-organizational focus and are often theoretical in nature. This study provides insight into most prevalent AI applications and the less popular applications applied in and for passenger transport, thereby presenting the dominating AI techniques that are covered by existing literature as well as highlighting a wide range of emerging AI techniques with the potential to become more influential for future studies. We discuss theoretical and managerial implications and offer avenues for future research.
Air transport management research, concerned with all facets of aviation operations, policies, and strategies, is an essential element of making our aviation system more sustainable and preparing it for the challenges inherent to the present and future. Based on a data-driven categorization of almost 2,000 papers published on the subject, we discuss the status quo in air transport management research. Through our data-driven categorization we have identified 15 broad topics. For each topic, we provide a description of the state of the art and propose 2-3 challenges , respectively. Overall, our study provides a set of 35 challenges to the research community. Accordingly, we hope and believe that our study makes a valuable contribution, mainly by guiding the air transport management research community towards a delineated work plan on the research landscape of air transport as well as the present challenges, ultimately helping to improve the global air transport system.
In today’s competitive marketplace, businesses face the ongoing challenge of meeting evolving customer demands while maintaining sustainable practices. For airlines, sustainability is a critical consideration that involves optimizing resource usage. This study addresses the crew recovery problem, an essential aspect of building sustainable business models for airlines. The primary objective is to minimize costs associated with crew disruptions while considering constraints, including flight time limitations. Recovery strategies, realized through actions known as recovery actions, play a pivotal role in addressing crew disruptions. Leveraging historical data, learning-based approaches have the potential to enhance algorithms for large-scale optimization problems. They provide insights that may be overlooked through traditional methods, improving the success of the recovery process. This study presents a column generation-based solution approach for the crew recovery problem, utilizing a customized deep learning model to provide recovery actions as inputs. The methodology is applied to a major European airline company. The results indicate that the model, supported by deep learning outputs, outperforms traditional methods in terms of solution quality and efficiency.
In Summer 2022, after a lean COVID-19 spell of almost three years, many airlines reported profits and some airlines even outperformed their pre-pandemic records. In context of the perceived recovery, it is interesting to understand how different markets have gone through the pandemic challenges. In this study, we perform a spatial and temporal dissection of the recovery process the global aviation system went through since May 2020. At the heart of this study, we investigate the patterns underlying market entry decisions during the recovery phase. We identify a rather heterogeneous type of recovery as well as its underlying drivers. We believe that our work is a timely contribution to the research on COVID-19 and aviation, complementary to the existing studies in the literature.
Purpose
Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.
Design/methodology/approach
Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.
Findings
The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.
Originality/value
By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.
Data-driven decision-making has garnered growing interest as a result of the increasing availability of data in recent years. With that growth many opportunities and challenges have sprung up in the areas of predictive and prescriptive analytics. Often, optimization can play an important role in tackling these issues. In this paper, we review some recent advances that highlight the difference that optimization can make in data-driven decision-making. We discuss some of our contributions that aim to advance both predictive and prescriptive models. First, we describe how we can optimally estimate clustered models that result in improved predictions. Next, we consider how we can optimize over objective functions that arise from tree ensemble models in order to obtain better prescriptions. Finally, we discuss how we can learn optimal solutions directly from the data allowing for prescriptions without the need for predictions. For all these new methods, we stress the need for good performance but also the scalability to large heterogeneous datasets.
Collaboration means in substance that several entities such as humans, computers, robots, enterprises and so on jointly perform a certain task instead of working individually so that a better result could be obtained. Decision-making is a specific form of activity, commonly carried out by human agents, which is meant to eventually select a certain course of action which is expected to result in attaining a desired result. The chapter is meant to present a concise and balanced view of the basic concepts and main classes of supporting information and communication tools and systems regarding decision-making processes carried out by several collaborating human agents called participants. The reasons for collaboration are briefly explained followed by an exposure of collaboration application in the multi-participant decision-making settings. Having presented the classification of decision problems and decision-making units, the main phases of a specific multi-participant form of Herbert Simon’s decision process model are described followed by the presentation of two main forms of close and soft collaboration, namely consensus building and crowdsourcing, respectively. The need for technology support offered to collaborating participants is justified and two main classes of decision supporting systems, namely Decision support systems and the ever more largely used platforms, are addressed. A practical example of an open ended and evolving platform is presented. Open questions about the further role the information and communication tools in multi-participant decision-making processes are eventually formulated from two perspectives, digital humanism and dataism, respectively.
The Airline Group of the International Federation of Operational Research Societies (AGIFORS) held four conferences during May to July 2021 that focused on how COVID-19 was impacting and reshaping the airline industry. Dozens of airline representatives from around the world spoke about how fundamental changes in passenger demand and booking patterns are reshaping the airline industry and driving innovation and research needs. Customers are booking much closer to departure and are canceling or exchanging their tickets more frequently than before COVID-19. Volatility in demand has increased as travel restrictions change and borders reopen. Consequently, greater uncertainty in demand forecasts used as inputs to optimization algorithms is motivating the need for new approaches. Revenue management and scheduling departments are innovating how they predict market sizes and exploring ways to use new data sources or historic bookings in forecasting models. Scheduling and operations departments are making many more flight-cancellation and equipment-swap decisions one to three days from flight departure, which is changing the role of recovery planning. New urgency exists within crew to design duties and rosters that are robust to ever-evolving schedules. Across functional areas, the increasing emphasis is to develop integrated solutions that jointly optimize schedules, crew pairings, and crew rosters for demand forecasts that are uncertain at the time rosters are published. This paper describes how these changes are reshaping the airline industry during COVID-19, explains why short lead times for bookings and uncertainty in demand volumes are expected to remain after COVID-19, and describes how the airline industry is innovating and developing new operations research approaches for handling uncertain and volatile demand.
Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems, operations, and infrastructure, to the system. To this end, historical data on airline scheduling and operations recovery are used to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, this paper provides a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.
Purpose: Airline strategy relies on the competitive environment analysis and the management of resources. Artificial Intelligence (AI) algorithms are being increasingly deployed throughout several industries. COVID-19 has further stressed a sector where firms have historically struggled to sustain profitability.
The purpose is to explore the potential of AI applications regarding strategic decision-making in airlines in times of crisis and to depict a roadmap to encourage scholars and practitioners to jointly implement these tools within corporations.
Design/methodology/approach: This study firstly reviews the state-of-the-art regarding transport organization trends with focus on airline strategy and finance as well as AI tools, supported by the collaboration of a former airline digitalization strategist. Secondly, the potential of the latter to be applied in those functions is analyzed, considering different Machine Learning (ML) methods and algorithms.
Findings: Some applications or pathways are identified as of particular interest for the airlines’ strategic decision-making process. Most of them are based on ML algorithms and training methods that are currently underused or disregarded in certain business areas, such as Neural Network models for unsupervised market analysis or supervised cost estimation.
Research limitations/implications: Focus is on airline strategy and finance, keeping engineering or operational applications out of the scope.
Practical implications: Proposed guidance may promote the deployment of AI tools which currently lack practical implementation in certain business areas.
Social implications: Showcased guidance may revert into a closer collaboration between business and academia.
Originality/value: Comprehensive review of current airlines’ strategic levers and identification of promising AI pathways to be further explored.
Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.
Nowadays, the market is getting competitive in all aspects of the survival of the company. Companies, especially in the service industry, realize that in order to win the market, they need to explore new ways of delighting customers. The airline industry has played a vital role in Indonesia's modern development and competitiveness. Fierce competition and shrinking profits have impelled the airlines to stress upon improving the quality of the services being provided to the passengers. Passengers have become very specific about their service needs and often tend to shift to others that provide better services. Service quality has emerged as a critical consideration for airlines in the competitive global market, coinciding with Low-Cost Carriers' rapid proliferation (LCCs), bringing a paradigm shift in airline business strategy. Twenty-five service quality attributes identified through extensive literature review and results obtained through five modified AIRQUAL dimensions are fruitful for airline managers to address service quality issues. This paper aims to evaluate LCCs' service quality in Indonesia using the Extenics innovation theory. The Extenics innovation theory is a kind of method with the combination of formalization, quantification, and logicalization. It is effectively applied to put forward creative ideas of new services of airlines. The concepts and principles of service attributes classification based on Extenics are also discussed. To consider interdependence and to calculate the relative importance of each criterion, the AHP method is applied. The study demonstrates and signifies that the Extenics theory and AHP method are promising and pragmatic evaluation model for customer-oriented airline strategic planning. This study has a number of practical implications for LCC airlines, its policy makers and managers. Findings of this study suggest that LCC airlines should focus on appropriate strategies for improving their customers' satisfaction. Airlines should concern on service failures such as delays by providing extra customer care to supplement required material compensation and provide valuable objective feedback and information. A theoretical implication of this study is that Extenics innovation theory can classify and prioritize the service attributes of airlines. Further, the scope of future research works has been discussed at the end to conclude the paper.
With more and more passengers choosing to fly by air, how to improve customer in-flight experience becomes an important issue for airlines. Digitalization has changed the way people enjoy their lives and it also has impacted on airline\x92s business strategy. Thus, it is worth to investigate how digitalization impacts on passengers’ in-flight preference. Due to the different strategy, inflight entertainment (IFE) system has different application in low-cost airlines and full-service airlines. In order to balance the cost and revenue of airlines, a customer satisfied survey about IFE has been conducted to investigate how passengers value IFE when they travel by air. Since more than 1/3 of respondents thought IFE was an important element when they taking flights, it is meaningful to provide passengers with IFE systems to improve their satisfaction. Thus, based on passengers\x92 needs, more types of movies and TV programs, personality functions, earphones, and other elements could be added to IFE systems to enhance customer in-flight experience.
In the airline industry’s very competitive landscape, being traveler-centric and offering an adapted set of products and services for different customer segments is crucial for airlines to resist and keep growing. By correctly designing and pricing product offers, airlines will reinforce their travelers’ loyalty and open upsell opportunities and strengthen their competitive position and revenue. This paper presents a new approach to design and optimizing airline offers, aiming to better consider travelers’ needs and preferences and improve airline revenue. The proposed method is based on market research and conjoint analysis techniques, combined with a revenue simulation framework. To better understand travelers, airline historical bookings data are used to cluster them in different segments based on their trip characteristics and booking behavior. In addition to the segmentation, a large sample of several thousands of travelers is surveyed using an adapted questionnaire per segment. The survey required participants to select one option among an assortment of a few products with different features. The data collected are used to build a choice model and evaluate the price elasticity. Travelers’ segments, estimated utilities, and choice modeling combined with the price elasticity and product preference shares are used to design the new product offer. A revenue simulation framework is developed to evaluate the revenue impact of introducing these new products in a competitive landscape. It simulates the flight booking life cycle from the flight’s opening for booking until the day of departure. Several scenarios of demand and willingness to pay are evaluated. Our approach is tested on a mid-size, full-service carrier. It allowed a better understanding of the travelers’ segments and behavior and resulted in a revenue improvement ranging from 1.6 to 4%, depending on the cabin and simulation scenario. Following the obtained results, the study recommendations’ are being implemented in production with an airline partner.
Industry insiders and analysts see dynamic offering—a highly responsive and customer-centric approach to offer design mirroring the practices of online retailers—as a viable instrument in airlines’ quest to regain profitability and maintain relevance in the digitalizing environment. While promising, adopting dynamic offering would also require a major overhaul of airlines’ technologies-in-use, as well as their organizational structures and processes in order to overcome the rigidities and limitations from today’s pre-defined fare-based offer creation. This paper synthesizes relevant concepts of offer management, outlines airlines’ key challenges to offer management adoption and suggests guidelines for airlines’ transition from pure flight providers to ‘airline retailers’ along six imperatives.
With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems. In order to fully report BDA for intelligent manufacturing systems, this paper provides a comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies. The framework, development, key technologies, and applications of BDA for intelligent manufacturing systems are discussed. The challenges and opportunities for future research are highlighted. Through this work, it is hoped to spark new ideas in the effort to realize the BDA for intelligent manufacturing systems.
Air transportation systems are an important part of the critical infrastructure in our connected world. Accordingly, a better understanding and improvements in the resilience of the overall air transportation system are essential to the well-functioning of our society and overall sustainability of human beings. In the literature, network science is increasingly used to better understand the resilience dynamics of air transportation. Given the wide application of tools for network science and the importance of designing resilient air transportation systems, a rich body of studies has emerged in recent years. This review paper synthesizes the related literature that has been published throughout the last 15 years regarding the robustness of air transportation systems. The contributions of this work consist of two major elements. The first part provides a comprehensive discussion and cross-comparison of the reported results. We cover several major topics, including node importance identification, failure versus attack profiles, recovery and improvement techniques, and networks of networks approaches. The second part of this paper complements the review of aggregated findings by elaborating on a future agenda for robust air transportation research. Our survey-style overview hopefully contributes toward a better understanding of the state of the art in this research area, and, in turn, to the improvement of future air transportation resilience and sustainability.
In Europe’s airline industry, the market exit of one of its largest industry players, the Air Berlin Group, has led to significant changes in the level of competition within the DACH-region (Germany, Austria and Switzerland). Existing research, however, is limited to provide insights into the short- and long-term im-plications of such market changes on the level of competition, particularly on price and quality. In response, this paper investigates how the bankruptcy of Air Berlin influences the level of price and quality airlines are offering to its passengers. Using a qualitative approach, airline-related professional and experts have been interviewed about their perception of the levels of quality, price and competition. The findings show that the Air Berlin bankruptcy resulted in a shortage of capacity on the market leading to increasing prices and short-term competition is likely to increase as various airlines try to replace Air Berlin on the market. However, in the long term, prices for airline tickets are expected to bounce back to a similar level like before the Air Berlin bankruptcy, as more competitors will penetrate the market, which leads to a more challenging market environment. In terms of quality, it was found that other Low-Cost Carriers (LCCs) offered similar products compared to Air Berlin, thus no or only minor changes in quality are expected.
Disruptions to airline schedules precipitate flight delays/cancellations and significant losses for airline operations. The goal of the integrated airline recovery problem is to develop an operational tool that provides the airline with an instant and cost-effective solution concerning aircraft, crew members and passengers in face of the emerging disruptions. In this paper, we formulate a decision recommendation framework which incorporates various recovery decisions including aircraft and crew rerouting, passenger reaccommodation, departure holding, flight cancellation and cruise speed control. Given the computational hardness of solving the mixed-integer nonlinear programming (MINP) model by the commercial solver (e.g., CPLEX), we establish a novel solution framework by incorporating Deep Reinforcement Learning (DRL) to the Variable Neighborhood Search (VNS) algorithm with well-designed neighborhood structures and state evaluator. We utilize Proximal Policy Optimization (PPO) to train the stochastic policy exploited to select neighborhood operations given the current state throughout the Markov Decision Process (MDP). Experimental results show that the objective value generated by our approach is within a 1.5% gap with respect to the optimal/close-to-optimal objective of the CPLEX solver for the small-scale instances, with significant improvement regarding runtime. The pre-trained DRL agent can leverage features/weights obtained from the training process to accelerate the arrival of objective convergence and further improve solution quality, which exhibits the potential of achieving Transfer Learning (TL). Given the inherent intractability of the problem on practical size instances, we propose a method to control the size of the DRL agent’s action space to allow for efficient training process. We believe our study contributes to the efforts of airlines in seeking efficient and cost-effective recovery solutions.
The emergence of digital platform start-ups poses a threat to disrupt the logistics industry with new business models. Digital freight forwarders offer platforms that challenge the service offerings of the traditional incumbent logistics providers, yet it is not clear whether these digital platforms have the potential to truly ‘disrupt’ the current industry. In this paper, we debate a more comprehensive view on the notion of disruption and disruptive innovation in the context of logistics start-ups leveraging digital platforms. We propose a Digital Start-up Disruption (DSD) framework – grounded on the existing literature – that allows characterizing digital platforms and their disruptive potential using four antecedents: initial target market, ecosystem framing, value creation, and regulatory agenda. Applying the framework of the four antecedents to a case study of a digital freight forwarder reveals important insights pertaining to the dynamics of disruptive and sustaining technologies. These findings can help investors and funding organizations to identify opportunities for potential disruptive innovations.
The COVID-19 pandemic has hit the airline industry hard, leading to heterogeneous epidemiological situations across markets, irregular flight bans, and increasing operational hurdles. Such a melange of irregularities has presented significant challenges to the airline industry, which typically relies on long-term planning. Given the growing risk of disruptions during epidemic and pandemic outbreaks, the role of airline recovery is becoming increasingly crucial for the aviation industry. This study proposes a novel model for airline integrated recovery problem under the risk of in-flight epidemic transmission risks. This model recovers the schedules of aircraft, crew, and passengers to eliminate possible epidemic dissemination while reducing airline operating costs. To account for the high uncertainty with respect to in-flight transmission rates and to prevent overfitting of the empirical distribution, a Wasserstein distance-based ambiguity set is utilized to formulate a distributionally robust optimization model. Aimed at tackling computation difficulties, a branch-and-cut solution method and a large neighborhood search heuristic are proposed in this study based on an epidemic propagation network. The computation results for real-world flight schedules and a probabilistic infection model suggest that the proposed model is capable of reducing the expected number of infected crew members and passengers by 45% with less than 4% increase in flight cancellation/delay rates. Furthermore, practical insights into the selection of critical parameters as well as their relationship with other common disruptions are provided. The integrated model is expected to enhance airline disruption management against major public health events while minimizing economic loss.
This study envisages assessing the effects of the COVID-19 on the on-time performance of US-airlines industry in the disrupted situations. The deep learning techniques used are neural network regression, decision forest regression, boosted decision tree regression and multi class logistic regression. The best technique is identified. In the perspective data analytics, it is suggested what the airlines should do for the on-time performance in the disrupted situation. The performances of all the methods are satisfactory. The coefficient of determination for the neural network regression is 0.86 and for decision forest regression is 0.85, respectively. The coefficient of determination for the boosted decision tree is 0.870984. Thus boosted decision tree regression is better. Multi class logistic regression gives an overall accuracy and precision of 98.4%. Recalling/remembering performance is 99%. Thus multi class logistic regression is the best model for prediction of flight delays in the COVID-19. The confusion matrix for the multi class logistic regression shows that 87.2% flights actually not delayed are predicted not delayed. The flights actually not delayed but wrongly predicted delayed are12.7%. The strength of relation with departure delay, carrier delay, late aircraft delay, weather delay and NAS delay, are 94%, 53%, 35%, 21%, and 14%, respectively. There is a weak negative relation (almost unrelated) with the air time and arrival delay. Security delay and arrival delay are also almost unrelated with strength of 1% relationship. Based on these diagnostic analytics, it is recommended as perspective to take due care reducing departure delay, carrier delay, Late aircraft delay, weather delay and Nas delay, respectively, considerably with effect of 94%, 53%, 35%, 21%, and 14% in disrupted situations. The proposed models have MAE of 2% for Neural Network Regression, Decision Forest Regression, Boosted Decision Tree Regression, respectively, and, RMSE approximately, 11%, 12%, 11%, respectively.
Benchmarking different aspects of air transport, and especially airport operations, is a research topic both important and complex, as proposed solutions are usually limited by data availability and underlying hypotheses. We here propose the use of two well-known statistical physics concepts, the Hurst exponent and the irreversibility, to measure the presence of interactions between landing flights from a macro-scale perspective. We firstly present a synthetic model of landings at an airport, showing how the two metrics are able to detect interactions arising from high volumes of traffic. Real landing data for twelve major European airports and ten Chinese ones are then analysed, showing that more interactions are present than what expected for the corresponding traffic. Results are nevertheless not homogeneous, with some airports (most notably Milano Malpensa, Madrid and Paris Charles de Gaulle) yielding statistically significant high values; and with Frankfurt airport having been especially impacted by the COVID-19 pandemics, which resulted in an increase in interactions in spite of a reduction in traffic. Some possible reasons behind these results, and their operational significance in terms of efficiency and safety, are finally discussed.
Purpose
Existing literature is limited in its ability to consider start-ups as a knowledge broker to trigger innovation in a supply chain ecosystem (SCE). In a traditional SCE, start-ups are relatively isolated, leading to structural holes that limit knowledge sharing among members. This paper aims to overcome that limitation and to build frameworks that help to illustrate the interaction between knowledge management and sharing, start-up innovation and an ecosystem from a supply chain perspective.
Design/methodology/approach
Following a qualitative approach, this study theorizes about the role of start-ups as knowledge brokers and the implications of knowledge management and sharing with members in an SCE concerning innovation. Conceptual analysis is used for examination, and this study uses a set of qualitative tactics to interpret and generate meaning from the existing literature.
Findings
This study develops two frameworks to provide insight into how start-ups can trigger innovation as knowledge brokers in an SCE. The first framework shows how start-ups, and their knowledge base, influence supply chain members and the overall ecosystem, highlighting the isolated function of start-ups and the issue of structural holes in a traditional SCE. The authors propose a model that illustrates how structural holes can be bridged within an SCE, thereby demonstrating how start-ups redefine the ecosystem architecture according to their knowledge broker position in the SCE.
Originality/value
By expanding insight into the concepts of how start-ups can trigger innovation as knowledge brokers in an SCE, this paper extends the so-far neglected area of start-ups and knowledge brokers. This study clarifies the conceptual and theoretical components and processes in an SCE and links the different roles of start-ups as knowledge brokers to the respective supply chain members to better understand the implications on the entire SCE.
Volatility and disruptions have always been two main characteristics of aviation, even though the causes for short-term disruptive events and long-term uncertainty shocks have changed as the industry became more mature over the years.Surprisingly, disruption planning has mainly focused on short-term events. Uncertainty shocks were neglected, although those rare occurrences hit with a high impact and far-reaching consequences on a supra-regional level, taking the aviation industry a significant period to return to the original state.But even without a proper preparation, the aviation industry was able to recover from those shocks in the past through flexibility, fast adaption to the new reality, and by relying on the lessons learned from previous crises.Accordingly, initially the aviation industry tried to resort to proven actions and learnings from past crises to mitigate the impact of COVID-19. But it soon became apparent that this was an unprecedented event and only relying on earlier shocks is insufficient.The major impact of COVID-19 resulted in a reprioritization of resources, both on the industry’s side and in the research community. It enabled the necessary shift of focus on long-term resilience measures to become economically more sustainable.
Air traffic disruptions result in flight delays, cancellations, passenger misconnections, and ultimately high costs to aviation stakeholders. This paper proposes a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. The approach forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruptions. It formulates a dynamic stochastic integer programming framework to minimize network-wide expected disruption recovery costs. Specifically, our Stochastic Reactive and Proactive Disruption Management (SRPDM) model combines a stochastic queuing model of airport congestion, a flight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop a solution procedure based on look-ahead approximation and sample average approximation, which enables the model’s implementation in short computational times. Experimental results show that leveraging even partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1%–2%, as compared with a myopic baseline approach based on realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, flight cancellations, and aircraft swaps.
Currently, digitalization is shaping all fields in a manner that is comparable to any other significant events that have positive influences in the economy. The airline industry is participating actively in digital innovation due to its cost structure, security dependence and competition intensity, to improve customer experience and financial performance. Therefore, this paper investigates passengers' experience on current and latest digital infrastructures applied in different stages of a flight by using the survey questionnaire. The paper separates the methodology and analyzes the survey results from qualitative and quantitative methods. Then the survey data is used to discuss how passengers are influenced based on different indicators, such as age groups and annual flight time. According to survey respondents’ evaluation, relevant problems in the airline digitalization process were pointed out and relevant suggestions were made.
Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two problem dimensions concurrently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another dimension. In addition, existing approaches for airline disruption management include human specialists who decide on necessary corrective actions for airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information imperative for making robust decisions that simultaneously address all problem dimensions during disruption management. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst all dimensions in airline disruption management, and provide objective insights to the specialists in the airline operations control center. To that effect, we provide a discussion and demonstration of an agnostic and systematic paradigm for enabling expeditious simultaneously-integrated recovery of all problem dimensions during airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology. Results indicate that our paradigm for simultaneously-integrated recovery executes in polynomial time and is effective when all the flights in the airline route network are disrupted.
Standardized, collaborative decision-making processes have already been implemented at some network-relevant airports, and these can be further enhanced through data-driven approaches (e.g., data analytics, predictions). New cost-effective implementations will also enable the appropriate integration of small and medium-sized airports into the aviation network. The required data can increasingly be gathered and processed by the airports themselves. For example, Automatic Dependent Surveillance-Broadcast (ADS-B) messages are sent by arriving and departing aircraft and enable a data-driven analysis of aircraft movements, taking into account local constraints (e.g., weather or capacity). Analytical and model-based approaches that leverage these data also offer deeper insights into the complex and interdependent airport operations. This includes systematic monitoring of relevant operational milestones as well as a corresponding predictive analysis to estimate future system states. In fact, local ADS-B receivers can be purchased, installed, and maintained at low cost, providing both very good coverage of the airport apron operations (runway, taxi system, parking positions) and communication of current airport performance to the network management. To prevent every small and medium-sized airport from having to develop its own monitoring system, we present a basic concept with our approach. We demonstrate that appropriate processing of ADS-B messages leads to improved situational awareness. Our concept is aligned with the operational milestones of Eurocontrol’s Airport Collaborative Decision Making (A-CDM) framework. Therefore, we analyze the A-CDM airport London–Gatwick Airport as it allows us to validate our concept against the data from the A-CDM implementation at a later stage. Finally, with our research, we also make a decisive contribution to the open-data and scientific community.
This article presents a novel approach to incorporate the aircraft turnaround, which has recently been identified as one of the major contributors to airline delay, into existing concepts for integrated aircraft, crew, and passenger recovery. We aim to fill the research gap on how to holistically model network delay propagation as tactical decision support for airline schedule recovery. Our model introduces a heterogeneous vehicle routing problem with time windows for the assignment of aircraft to flight routes and integrates it with an extended version of the resource-constrained project schedule problem for the allocation of scarce resources to turnarounds at the central hub airport, such that we can proactively estimate delay propagation in an airline network. Passenger and crew itineraries are modelled as links between flights, such that needed transfer times influence the stand allocation and resource assignment. These links may only be broken if reserve capacities are available and the related rebooking and compensation costs are more efficient than accepting departure delays to maintain transfers. With this approach, we are able to calculate flight-specific delay cost functions and find substantial dependencies about the time of the day, the number of succeeding flight legs and particular downstream destinations.
The integrated recovery model is implemented into a rolling horizon algorithm and applied to a case study setting to analyse its performance in comparison to the individual turnaround and aircraft recovery models. Within different delay scenarios, we find that the incorporation of turnaround recovery options significantly improves the resilience of the airline network. Especially in low and moderate delay situations, we achieve a full recovery of the flight schedule simply by rebooking passengers, reallocating aircraft among stands and accelerating ground operations. Thus, often considered recovery options, such as aircraft swaps and flight cancellations, are not required for delays around 30 minutes in our case study. This reduces total costs in comparison to the conventional aircraft recovery model by 49%. Despite the lower efficiency of turnaround recovery in medium and high delay scenarios, the combination of flexible aircraft assignments and ground operations still generates additional cost savings of at least 21% and helps to reduce the necessary amount of optimal recovery options.
The history of aviation and the dream of flight dates back many centuries and includes pioneers such as Leonardo da Vinci, Otto Lilienthal and the Wright Brothers, who contributed to this quest. The last century brought along a remarkable growth of the aviation sector and resulted in considerable economic importance of the industry. The aviation industry can be projected along the aviation value chain and comes with its own special characteristics. Whilst aviation creates a high value for customers and other stakeholders, the profit margins are typically low due to high fixed costs and its dependence on external factors. The benefits of aviation to the economy as well as other drivers create positive effects for many stakeholders who are directly or indirectly involved in the system. The aviation system is surrounded by different environments: the economic, ecological, social, technological and political environments. Each environment exerts influence on the aviation system and is simultaneously affected by it.
The aviation value chain is made up of different sub-industries that range from aircraft manufacturers over technical support to airlines. Each of these sub-industries face different pull and push effects which are interdependent and influence their decisions and actions. Coupled with the interrelations of the different spheres within the aviation value system, and the market environment, a system approach is useful to map the aviation industry. Each sub-industry faces different degrees of profitability which depend on the entry barriers and the natural market power. This explains why airlines see narrower profit margins while airports or leasing companies face higher profits.
Trust and distrust can play an important role in a healthy supply chain collaborative relationship, and both carry potential shortcomings. Little attention has been paid to understanding and explaining the development process of trust and distrust in supply chain collaborations, especially in an international context. Using the Transaction Cost Economics theory, this study begins by discussing expressions of trust and distrust within the context of a supply chain collaboration dyad. Then, we explore how trust and distrust interact at a network level. Using a novel, longitudinal, multi-case-study approach, this paper provides new empirical evidence of the complementary roles of trust and distrust in supply chain collaboration, exploring how these concepts work together across different stages of the relationship and in different contexts. This study distinguishes between 'competence trust' and 'integrity trust' concerning collaboration contracts which typically create distrust. Finally, this paper offers unique insights into the influence of culture on the interpretation and performance of trust and distrust in international supply chain collaboration, grounded in the context of the Chinese automotive industry.
Highlights: • We challenge the idea that trust and distrust substitute for each other as ends of a continuum. • We provide a more nuanced platform on which to deploy a TCE-trust lens to understand productive and unproductive supply chain collaboration. • We provide insights into more nuanced forms of trust predicting the conditions under which supply chain collaborations develop healthy versus unhealthy levels of trust, and the forms of that trust. • We offer unique insights into the influence of culture on the interpretation and performance of trust and distrust in international SCC. 2 • We present longitudinal, multi-case data of international supply chain collaboration in the Chinese automotive industry.
This paper presents a systematic review of studies related to artificial intelligence (AI) applications in supply chain management (SCM). Our systematic search of the related literature identifies 150 journal articles published between 1998 and 2020. A thorough bibliometric analysis is completed to develop the past and present state of this literature. A co-citation analysis on this pool of articles provides an understanding of the clusters of knowledge that constitute this research area. To further direct our discussions, we develop and validate an AI taxonomy which we use as a scale to conduct our bibliometric and co-citation analyses. The proposed taxonomy consists of three research categories of (a) sensing and interacting, (b) learning, and (c) decision making. These categories collectively establish the basis for present and future research on the application of AI methods in SCM literature and practice. Our analysis of the primary research clusters finds that learning methods are slowly getting momentum and sensing and interacting methods offer an emerging area of research. Finally, we provide a roadmap into future studies on AI applications in SCM. Our analysis underpins the importance of behavioral considerations in future studies.
This book chronicles airline revenue management from its early origins to the last frontier. Since its inception revenue management has now become an integral part of the airline business process for competitive advantage. The field has progressed from inventory control of the base fare, to managing bundles of base fare and air ancillaries, to the precise inventory control at the individual seat level. The author provides an end-to-end view of pricing and revenue management in the airline industry covering airline pricing, advances in revenue management, availability, and air shopping, offer management and product distribution, agency revenue management, impact of revenue management across airline planning and operations, and emerging technologies is travel. The target audience of this book is practitioners who want to understand the basics and have an end-to-end view of revenue management.
Most airlines have established integrated hub and operations control centers for the monitoring and adjustment of tactical operations. However, decisions in such a control center are still elaborated manually on the basis of expert knowledge held by several agents representing the interests of different airline departments and local stakeholders. This article studies a concept which incorporates the situational awareness gained by airport-collaborative decision making into an airline-internal decision support system, such that it integrates all available schedule recovery options during aircraft ground operations. The developed mathematical optimization model is an adaptation of the Resource-Constrained Project Scheduling Problem (RCPSP) and incorporates key features from turnaround target time prediction, passenger connection management, tactical stand allocation and ground service vehicle routing into the airline hub control problem. The model is applied in a case study consisting of 20 turnarounds during a morning peak at Frankfurt airport. Schedule recovery performance (resilience) is analyzed for a set of key performance indicators within multiple scenario instances which contain different resource availability and aim at solving various arrival delay situations. Results highlight that a minimization of tactical cost concurrently reduces average departure delay for flight and passengers while recovery performance is substantially affected when some options are not included in the evaluation process. Thus, our concept provides airlines with an optimization approach for constrained airport resources so that total cost and delay resulting from schedule deviations are reduced, which may benefit strategic schedule planning and improve predictability of operations for local collaborators, such as airport, ground handlers and ATM performance.
We address an airline-driven flight rescheduling problem within a single airport in which a series of ground delay programs (GDPs) are considered. The objective of the problem is to minimize an airline’s total relevant cost (TRC) consisting of delay costs, misconnection costs, and cancellation costs that would result from flight rescheduling. We introduce three solution approaches—the greedy approach, the stochastic approach, and the min-max approach—that revise the daily flight scheduling whenever the schedule is affected by a GDP or further GDP changes. The greedy approach simply searches for a solution using currently updated static GDP information, and the other two approaches provide a solution by considering possible scenarios for changes of the GDP. Using real-world data in existing literature and some generated scenarios, we present extensive computational results to assess the performance of the approaches. We also report the values of information on GDP the solution approaches refer to. Deliberating various cost parameter settings an airline might consider, we discuss the value of information in implementing the proposed solution approaches.
This paper presents an extensive theoretical and empirical analysis of the choice of schedule buffers by airlines. With airline delays a continuing problem around the world, such an undertaking is valuable, and its lessons extend to other passenger transportation sectors. One useful lesson from the theoretical analysis of a two-flight model is that the mitigation of delay propagation is done entirely by the ground buffer and the second flight’s buffer. The first flight’s buffer plays no role because the ground buffer is a perfect, while nondistorting, substitute. In addition, the apportionment of mitigation responsibility between the ground buffer and the second flight's buffer is shown to depend on the relationship between the costs of ground- and flight-buffer time. The empirical results show the connection between buffer magnitudes and a host of explanatory variables, including the variability of flight times, which simulations of the model identify as an important determining factor.
Collaboration plays a critical role in fostering innovation and value creation in the aviation sector. However, how factors and connections relate to the achievement of innovative outcomes in aviation require further investigation. This study investigates the key factors that create a conceptual framework by conducting a literature review and an archival analysis of news articles. The model proposed involves factors such as strategic decision-making; networking and partner choice; cultural context, values, behaviour and compatibilities; collaboration configuration; issues and risks shared; skills, capacities and experience; infrastructure and resources available; engagement activities; knowledge transfer, absorption and appropriation; collaboration management; communication flows; external environment and demand; and expectations and outcomes. Promising collaborations are also indicated in areas where the framework could be adopted to increase partnerships and outcomes. Also, we highlight best practice examples from leading organizations, such as International Airlines Group (IAG), Emirates Airline, Singapore Airlines, Boeing and JetBlue, to provide insights into existing collaborations that have led to innovation and value creation in this sector.
One of the major challenges within the airline industry is to keep pace with the changing customer perception towards their service quality. This paper demonstrates how sentiment analysis of User-Generated big data can be used in research on airline service quality, as a more comprehensive alternative to other survey-based models, by investigating real-time passenger insights.
The present research uses the case of Indigo airlines, by studying passenger’s Trip Advisor reviews regarding the low-cost commercial airline service. The authors analyzed 1,777 passenger reviews, which were classified, to uncover sentiments for five dimensions of Airline Service Quality (AIRQUAL).
The findings of the study demonstrate the need for harnessing the brand-related user-generated content, shared on online platforms to identify the critical attributes for airline service quality. Further, through the application of sentiment analysis, the paper provides much needed clarity in the processing of the user-generated content. It illustrates the investigation of passenger interactions as a reflection of their satisfaction, expectation, intention, and overall opinion towards the airline service quality.
The analytical framework adopted in the study for examining the UGC, can be functional for the marketing managers and equip them for handling the large scale data readily available in action oriented interactive marketing research
This paper demonstrates how sentiment analysis of user-generated data can be used in research on airline service quality, as a more comprehensive alternative to other survey-based models. The study supplements the methodological advances in the field of UGC analysis, and adds to the existing knowledge domain.