Airline Operations Control Research Articles

AI-Driven FlightOps: Revolutionizing Airline Operations Control with Predictive Analytics and NLP

Abstract: Flight operations control centers (OCCs) are the nerve centers of airline operations, tasked with managing a wide range of activities such as flight scheduling, crew assignments, maintenance coordination, and dealing with disruptions. While traditional approaches have relied heavily on human decision-making and manual analysis of operational data, the increasing complexity of modern airline operations demands more efficient solutions. This paper presents FlightOps AI, a cutting-edge AIpowered system that integrates advanced natural language processing (NLP), deep neural networks (DNNs), reinforcement learning (RL), and computer vision to provide OCC staff with actionable insights and predictive capabilities. The system processes massive amounts of structured and unstructured data in real time, enabling smarter decision-making and automation of routine tasks. By adopting these technologies, FlightOps AI transforms the operational landscape, optimizing airline performance while minimizing human errors

Fuel consumption prediction for pre-departure flights using attention-based multi-modal fusion

Improper fuel loading decision results in carrying excessive dead weight during flight operation, which will burden the airline operation cost and cause extra waste emission. Existing works mainly focused on the post-event fuel consumption based on flight trajectory. In this work, a novel deep learning model, called FCPNet, is proposed to achieve the fuel consumption prediction (FCP) before the flight departure. Considering the influential factors for aircraft performance, the multi-modal information sources, including the planned route, weather information, and operation details, are selected as the model input to predict fuel consumption. Correspondingly, three modules are innovatively proposed to learn embedding features from multi-modal inputs. Based on the planned route, the graph convolutional network is proposed to mine the spatial correlations in the non-Eulerian route network. Considering the grid attributes of the weather information, the ConvLSTM is applied to learn abstract representations from both the temporal and spatial dimensions, in which the three-dimensional convolution neural networks are also designed to fine-tune intermediate feature maps. The fully connected layer is also proposed to learn informative features from operation details. Finally, an attention-based fusion network is presented to generate the final embedding by considering the unique contributions of the multi-modality sources, which are further applied to predict flight fuel consumption. A binary encoding representation is proposed to formulate the FCP task as a multi-binary classification problem. The proposed model is validated on a real-world dataset, and the results demonstrate that it outperforms other baselines, i.e., achieving a 6.50% mean absolute percentage error, which can practically support the airline operation and global emission control before flight departure.

Formal modelling and verification of a multi-agent negotiation approach for airline operations control

This paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.

Airline Disruption Management: A Review of Models and Solution Methods

The normal operation of aircraft and flights can be affected by various unpredictable factors, such as severe weather, airport closure, and corrective maintenance, leading to disruption of the planned schedule. When a disruption occurs, the airline operation control center performs various operations to reassign resources (e.g., flights, aircraft, and crews) and redistribute passengers to restore the schedule while minimizing costs. We introduce different sources of disruption and corresponding operations. Then, basic models and recently proposed extensions for aircraft recovery, crew recovery, and integrated recovery are reviewed, with the aim of providing models and methods for different disruption scenarios in the practical implementation of airlines. In addition, we provide suggestions for future research directions in these topics.

Research on airline operational risk management and control method

Starting from the reality of airlines, this paper studies the operational risk and control methods of airlines, establishes relevant risk index system, and formulates corresponding control methods. Based on the four aspects of human, aircraft, environment and management, the risk index system is established. The weight of the index and the score of each system are determined by using the analytic hierarchy process and the fuzzy comprehensive evaluation respectively, and the airline risk evaluation model is established. In the formulation of airline management and control methods, genetic algorithm is used to compare different flight task combinations, and the advantages of this method are obtained.

Enhancement of the diversion airport selection methodology

The paper deals with the airlines diversion management as this entails a complex decision-making process that comes into action during the adverse events, preventing the execution of the regular flight operations. Diverting from the intended route can be caused by several reasons both when the safety of the flight or the wellbeing of the passengers is concerned. The most frequent reasons to divert a flight are the repugnant weather, medical emergency or the technical fault. A flight diversion can severely disrupt the passenger’s comfort, jeopardize the fleet or the crew planning and is generally accompanied with the significant expenses. The intention of every airline in a case of a diversion is to assure the fastest turnaround time and/or provide the passengers with the highest level of services to mitigate the negative effects. A wrong selection of an alternate airport, that is not having enough capacity, personnel or material resources to handle a flight diversion in a timely manner can negatively influence the airline in several ways. Both strategic and tactical diversion management process is a collaborative decision-making procedure, heavily dependent on the base of the information available, where the main actors are not only flight crew and the airline Operational Control Centre but also the Air Traffic Controller, airport operators and the local service providers. Airline strategic diversion planning shall comprehend a well-defined data set and its data quality procedures shall ensure the information accuracy in the pre-tactical phase. The consequent real time data processing can enhance the decision-making process in a tactical phase of the flight diversion. The data and information required comprise of available airport capacities and other relevant ground resources needed to handle a diverted flight. As stated, such data and information are mostly tactically critical, therefore continuous monitoring and updating shall be ensured. The paper suggests that the overall data management is managed via a local Airport Operations Center, which will assure provision of actual airport diversion capabilities (incl. weather situation) needed for accommodation of an affected flight.

Evaluation of a Multi-Agent System approach to airline disruption management

Each day, airlines face disturbances that disrupt their carefully planned operations. Events like adverse weather conditions, sick crew members, or damaged aircraft often result in delays in the airline's schedule. An airline recovers from such disruptions through the role played by its Airline Operations Control (AOC). A Multi-Agent System (MAS) approach to airline disruption management was recently proposed under the acronym MASDIMA (Multi-Agent System for Disruption Management in AOC). The purpose of this paper is to evaluate this MAS supported AOC approach on its performance and its practical introduction. This is done using a scenario-based analysis to compare the MAS supported policy to human-team based AOC policies. A task-based analysis identifies how well AOC is able to cover a set of tasks using the MAS supported policy. The scenario-based analysis shows that the MAS supported AOC is able to find the optimal solution, and to do this significantly faster. The task-based analysis identified two main challenges for implementing the MAS supported AOC policy: i) to overcome the loss of experience that is caused by significantly automating humans roles in AOC, and ii) to reduce the workload for people that remain in AOC after its introduction. The paper concludes that implementing the MAS supported AOC policy leads to both better and faster resolutions, though the replacement of human roles also poses novel challenges that remain to be resolved: a potential increase in workload for the remaining human role and loss of experience in handling exceptional situations.

Airline disruption management: yesterday, today and tomorrow

Abstract Adverse weather conditions, strikes, political reasons and other different causes can impact the long-term success of an airline. An effective Disruption Management System can predict the occurrence of these events and assist by reducing the impact on the operations. The paper offers an overview on what is considered a disruption for airlines and analyses the state-of-the-practice of airline operation control centers. Traditionally, Operations Control Centers (OCCs) are mainly composed by aircraft dispatchers, maintenance operators, and other operational personnel. Currently, there is a tendency to integrate into this control centers other departments critical for the decision making, such as commercial department, social media, ground operations, etc. However, we propose an analysis of what other data is required to provide fact-based decisions during Irregular Operations (IROPS). The need for implementing these decisions, requires a promptly access to real time data by frontline and operational staff. The paper also offers an understanding of this required data. Furthermore, one of the indicator to measure the long-term success of an airline is the number of passenger flown by year and their engagement to continue flying with the airline in the future. This aspect is not always considered during disruption, therefore decisions are taken based on previous operational experience without considering real long-term impact on value of passengers. The paper explores some of the solutions to personalize passenger preferences in case of disruptions. Specifically, we look into self-serve options to provide the passenger with the sense of control of the situation. Lastly, the complexity of Disruption Management in airlines is motivated by the lack of resources (aircraft, crew, etc.) and limited transportation options. An assessment is conducted on the Meta-CDM project which incorporates rails and buses as an alternate mode of transportation.

Agent-Based Modeling and Simulation of Coordination by Airline Operations Control

This paper implements and compares four coordination policies through agent-based modeling and simulation (ABMS), motivated by the need to understand and further optimize coordination processes in the highly complex socio-technical air transportation system. Three policies are based on established practices, whereas a fourth is based on the joint activity coordination theory from the psychology research domain. For each of these four policies, the relation with the literature on coordination is identified. The specific application of the four policies concerns airline operations control (AOC), which core’s functionality is one of coordination and taking corrective actions in response to a large variety of airline operational disruptions. In order to evaluate the four policies, an agent-based model of the AOC and crew processes has been developed. Subsequently, this agent-based model is used to assess the effects of the four AOC policies on a challenging airline disruption scenario. For the specific scenario considered, the joint-activity coordination-based AOC policy outperforms the other three policies. More importantly, the simulation results provide novel insight in the operational effects of each of the four AOC policies, which demonstrate that the ABMS allows to analyze the effectiveness of different coordination policies in the complex socio-technical air transportation system.

A decision support tool for evaluating decision options for out-bound flight delays considering high-valuable passengers

Though studies show that arriving on-time is the service characteristic most valued by the air passengers and passenger service coordinators are consulted in the operation disruptions, passengers rarely drive operational decision-making in the disruption management of an airline. Taking into consideration airlines’ seeking the prevention of losing passengers’ goodwill in situations where the level of service quality (SQ) performed is not as high as the promoted one, this research is focused particularly on an influence of delayed connecting (or in-bound) high-fare passengers on making decisions on onward delays. Operationally seen, connecting passengers are of a particular importance for airlines, since they are already involved in their operations-flow, while becoming in this way the third most important airlines’ aspect of operations, succeeding aircraft and crew. On the other hand, exclusive travellers such as first class and elite-status, contracted corporate members, and business passengers are considered as the high-valuable passengers, who are enough (financially) worth to the airline, particularly for to be waited for if they are delayed in arriving with an in-bound flight. Based upon an examination made from the airlines’ operational point of view and for determined prioritization strategy, a Decision Support System (DSS) tool, named DEVOTED DSS Tool, for use in the disruption management of the Airline Operation Control Centre (AOCC) is presented. For assisting the airline operation controllers in decisions on whether to delay the departure of out-bound flights in order to wait for arriving-delayed high-fare passengers from an in-bound flight, an influence of these passengers’ satisfaction on making decisions on onward delays, the passenger segmentation per flight, associated consequences in terms of the Level of Service (LOS) performed by the carrier and the one perceived by the passengers, as well as the accompanying costs are taken into account. The designed tool incorporates the key elements of Human-Centred Design (HCD) relying on a multi-criteria algorithm making-up its decision making process for evaluation of decision options and making suggestion. Being scenario-driven and knowledge-based the tool implements the airline prioritisation policies. Hereby, the level of service quality delivered by the air carrier and the level of service quality expected and perceived by the high-fare passengers are determined quantitatively by employing a created LOS-model which relies on the basic categorization rules of the Kano’s quality model. For testing of the tool, two scheduled airlines with two different prioritization strategies and operating on the same flight route between the same origin-destination airports (i.e. city-pairs) are taken. Though the testing is real-world data based, in order to expose a tangible aim and capabilities of the designed tool, the testing scenarios are constructed in such a way to reflect specific borderline operational situations, while emphasizing occurring conflicting decision key criteria. The tool output consists of two components – the LOS quality delivered by the carrier including the delay-costs (LOS Airline) and the level of service perceived by the high-fare passengers on both flights (LOS Passenger) – which are reflected on the user interface in form of two bars, each consisting of three colours indicating an option as good (green), neutral (yellow) or bad (red). Although the operation controller may take the opposite decision as recommended, the tool is enabled to display the evaluation of consequences of any decision made, while being visualized as a scale-value positioned somewhere on the three- colour-bar. Investigating the impact of a passenger-structure on decisions on onward-delays in the every-day flight operations execution, a decision support real-time tool, De laying V IPs O rien ted D ecision S upport S ystem (DEVOTED DSS) was created, implementing (pre-specified) airline prioritisation policy in accordance with the rating of the high-fare passengers-classes importance. The decision solutions’ consequences displayed in the designed form are practical in terms of user-friendly utilization of DEVOTED and are simple and easy to deal with, since being relieved of any digits, data and/or calculations. Aiming at accurately evaluating the impact of operational disruption-decisions on the high-fare passengers, DEVOTED processes the LOS quality which is to be delivered to these passengers, SQ-attributes required/expected by these passengers, number of passengers in each defined passenger-group and the ticket prices purchased, as well as expected costs. For the first time, an introduced juxtaposition of the in-bound and out-bound high-fare passengers of the same cabin-class within connecting flights has been employed as an influencing factor in the decision making process of the airline disruption management. When it is about to make the choice between a monetary benefit and the retention of the reputation of a reliable service provider, the use of the designed tool affords rather objective instead the still occurring intuitive decision making in such disruptions.

Assessing the structure of non-routine decision processes in Airline Operations Control

Unfamiliar severe disruptions challenge Airline Operations Control professionals most, as their expertise is stretched to its limits. This study has elicited the structure of Airline Operations Control professionals’ decision process during unfamiliar disruptions by mapping three macrocognitive activities on the decision ladder: sensemaking, option evaluation and action planning. The relationship between this structure and decision quality was measured. A simulated task was staged, based on which think-aloud protocols were obtained. Results show that the general decision process structure resembles the structure of experts working under routine conditions, in terms of the general structure of the macrocognitive activities, and the rule-based approach used to identify options and actions. Surprisingly, high quality of decision outcomes was found to relate to the use of rule-based strategies. This implies that successful professionals are capable of dealing with unfamiliar problems by reframing them into familiar ones, rather than to engage in knowledge-based processing.Practitioner Summary:We examined the macrocognitive structure of Airline Operations Control professionals’ decision process during a simulated unfamiliar disruption in relation to decision quality. Results suggest that successful professionals are capable of dealing with unfamiliar problems by reframing them into familiar ones, rather than to engage in knowledge-based processing.

Enhancing Safety at Airline Operations Control Centre

In recent years a new term of Safety Management System (SMS) has been introduced into aviation legislation. This system is being adopted by airline operators. One of the groundbased actors of everyday operations is Operations Control Centre (OCC). The goal of this article has been to identify and assess risks and dangers which occur at OCC and create a template for OCC implementation into SMS.

A new approach for disruption management in airline operations control

As a sequel of a recent Ph.D. thesis at the University of Porto and LIACC research lab, a new book appeared at Springer, Series: Studies in Computational Intelligence, Vol. 562, 2014, XII, 242 p., entitled A New Approach for Disruption Management in Airline Operations Control by Antonio J. M. Castro, Ana Paula Rocha, and Eugénio Oliveira.

Based on the State of Emergency Safety Assessment Method of Airlines Dispatch Operation

The research of this paper is based on the domestic airline operational control that exist variety of factors under the state of emergency, such as emergent meteorological conditions, flight flow and alternate airport security conditions, the potential human factors and organizational management operation, in order to establish the correct hierarchy model, we analyzing the inherent correlation of these explicit and implicit factors which impact on the dispatch release work, then apply these factors to the Bayesian network structure and use Gaussian normal distribution to solve mean value and mean square error to determine the weight for influence factors so that run a aviation safety assessment for dispatch operation, it can help the company to propose targeted security operating strategy.

Research on Airline Operational Control Model Based on Analytic Hierarchy Process

With expansion of airlines fleet capacity in China, airlines pay more attention to airline profit, service evaluation, etc., besides flight safety. Therefore it is important to study on operational control approaches that can promote operational efficiency, guarantee flight on-schedule rate and reduce operational cost. In this paper, an Analytic Hierarchy Process is employed to build a related-factor model mainly influencing dispatchers making decision in the operational control process of airlines. This process makes the complicated decision-making system hierarchic and analyzes the importance of all related factors by comprising their importance in a hierarchical way. Through an algorithmic example and data analysis, the operational control decision-making model of an assisting nature based on hierarchic analysis in this paper can provide quantitative basis for final decision making.

Real-time aircraft turnaround operations manager

Aircraft turnaround operations are the activities conducted to prepare an inbound aircraft at an airport for a following outbound flight that is scheduled for the same aircraft. The activities of aircraft turnaround operations include both the inbound and outbound exchanges of passengers, crew, catering services, cargo and baggage handling. Hence, appears the importance of following up on all activities done during the aircraft turnaround stage to deliver the true picture to the airline operation control centre (OCC) automatically. This enables the OCC to take quick and proper decisions regarding how to face any obstacles to flight punctuality, determine responsibilities and evaluate performances in regard to all activities. These control and evaluation activities occur, at the present, using manual methods and telephone communications, which lead to unreliable data and delays. The data become more doubtful due to interference of the human factor and the necessity to monitor flights preparation. Our solution to these problems is to present the airline flight preparation management process during the aircraft turnaround and design and implement a rule-based system called Flight Activities Progressions System that is used to manage and monitor the flights preparation during the turnaround. Mobile computing devices, portable handsets and the wireless network technology General Packet Radio Service are used to implement the real-time system for an airline. The Project Evaluation and Review Technique (PERT) and Critical Path Method are modelled in the system. The goals of using PERT are to evaluate and improve the efficiency of airline operational procedures and to improve the efficiency of airline ground activities allocation. The system implementation results indicate that real-time operation can potentially reduce delays occurring from airline operations and optimise the aircraft ground stop time.

A New Concept for Disruption Management in Airline Operations Control

The Airline Operations Control Centre (AOCC) of an airline company is the organization responsible for monitoring and solving operational problems. It includes teams of human experts specialized in solving problems related with aircrafts, crewmembers, and passengers, in a process called disruption management or operations recovery. In this article, the authors propose a new concept for disruption management in this domain. The organization of the AOCC is represented by a multi-agent system (MAS), where roles that correspond to the most frequent tasks that could benefit from a cooperative approach, are performed by intelligent agents. The human experts, represented by agents that are able to interact with them, are part of this AOCC-MAS supervising the system and taking the final decision from the solutions proposed by the AOCC-MAS. The authors show the architecture of this AOCC-MAS, including the main costs involved and details about how the system takes decisions. They tested the concept, using several real airline crew-related problems and using four methods: human experts (traditional way), the AOCC-MAS with and without using quality-costs, and the integrated approach presented in this article. The results are presented and discussed.

The airline schedule optimization model: validation and sensitivity analysis

The Airline Schedule Optimization (ASO) Advanced decision support system (DSS) provides a solution as a proposal of a new flight schedule, in situations when an airline's flight scheduled is disturbed, which would minimize the negative effects of those disturbances. The ASO DSS was successfully tested and validated in Airline Operation Control Centre. Further, the sensitivity analysis was done where the main task was to analyze how the values of the penalties defined in the DSS influence on generated solutions by the ASO Advanced. The results of the model validation as well as the sensitivity analysis are presented in this paper.

Modeling Work for Cognitive Work Support System Design in Operational Control Centers

The design of cognitive work support systems for operational control centers is an emerging challenge for human factors practitioners. This paper presents an example of applying techniques derived from the interactive design tradition to a context-of-use analysis, including both knowledge elicitation and modeling, to inform the design of a cognitive work support system. The paper integrates insights from the Cognitive Control Model—specifically, contextual control modes—into the cognitive analysis of work. The inclusion of COCOM in contextual inquiry helps to identify important contextual factors and brings meaning and organization to the different patterns of activity observed. Specifically, the paper presents the results of a contextual inquiry for airline operation control centers in the form of work models that have been extended to account for contextual control modes.

Flight operations recovery: New approaches considering passenger recovery

The sources of disruption to airline schedules are many, including crew absences, mechanical failures, and bad weather. When these unexpected events occur, airlines recover by replanning their operations. In this paper, we present airline schedule recovery models and algorithms that simultaneously develop recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. The objective is to minimize jointly airline operating costs and estimated passenger delay and disruption costs. This objective works to balance these costs, potentially increasing customer retention and loyalty, and improving airline profitability. Using an Airline Operations Control simulator that we have developed, we simulate several days of operations, using passenger and flight information from a major US airline. We demonstrate that our decision models can be applied in a real-time decision-making environment, and that decisions from our models can potentially reduce passenger arrival delays noticeably, without increasing operating costs.