Airline disruption management--Perspectives, experiences and outlook (original) (raw)

Related papers

Disruption management in the airline industry--Concepts, models and methods

2010

This paper provides a thorough review of the current state-of-the-art within airline disruption management of resources, including aircraft, crew, passenger and integrated recovery. An overview of model formulations of the aircraft and crew scheduling problems is presented in order to emphasize similarities between solution approaches applied to the planning and recovery problems.

Disruption management for commercial airlines: methods and results for the ROADEF 2009 Challenge

European J. of Industrial Engineering, 2012

A disruption management problem for commercial airlines, has been presented by Amadeus for the ROADEF 2008/2009 Challenge, an international competition organized by the French Operational Research and Decision Support Society (ROADEF). This paper presents this industrial large scale optimization problem and underlines its difficulties compared to previously tackled problems in the area. We review the most prominent methods proposed by the candidates and provide the official results and participant ranking. Last, as lessons learned from this experience, we draw guidelines for further research.

A New Concept for Disruption Management in Airline Operations Control

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2011

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 c...

A Distributed Approach to Integrated and Dynamic Disruption Management in Airline Operations Control

PhD Thesis, 2013

"Airline companies make a huge effort to maximize their revenue while keeping their costs at a minimum. Unfortunately, any operational plan has a strong probability of being affected, not only by large disruptions like the one that happened in April 2010 due to the eruption of the Iceland Eyjafjallajökull volcano but, more frequently, by smaller daily disruptions caused by bad weather, aircraft malfunctions and crew absenteeism, for example. These disruptions affect the original schedule plan, delaying the flights, and cause what is called an Irregular Operation. Studies have estimated that irregular operations can cost between 2% and 3% of the airlines' annual revenues and that a better recovery process could result in cost reductions of at least 20% of its irregular operations. In this thesis, we have studied the AOCC of TAP Portugal as well as the work of other researchers in this field in order to propose a distributed and decentralized general approach to integrated and dynamic disruption management in airline operations control, based on the Multi-Agent System (MAS) paradigm. The approach is distributed because it allows the functional, spatial and physical distribution of the intervening agent roles and the environment; it is decentralized because some decisions are made in different nodes of the agents' network; it is integrated because it includes the main dimensions of the problem: aircraft, crew and passengers; and it is dynamic because, in real time, several agents are performing in the environment, reacting to constant change. The results show that our proposal, not only corroborates existing studies regarding the possible cost reductions that could result from a better disruption management process but, also, gives the possibility of reaching solutions that balance the utility of the three dimensions of the problem: aircraft, crew and passengers."

A New Approach for Disruption Management in Airline Operations Control

Studies in Computational Intelligence, 2014

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, selforganizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.

Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn

Transportation Science, 2016

In this paper we present a novel approach addressing airline delays and recovery. Airline schedule recovery involves making decisions during operations to minimize additional operating costs while getting back on schedule as quickly as possible. The mechanisms used include aircraft swaps, flight cancelations, crew swaps, reserve crews and passenger rebookings. In this context, we introduce another mechanism, namely flight planning, which enables flight speed changes. Flight planning is the process of determining flight plan(s) specifying the route of a flight, its speed and its associated fuel burn. Our key idea in integrating flight planning and disruption management is to adjust the speeds of flights during operations, trading off flying time and fuel burn, and combining with existing mechanisms such as flight holds; all with the goal of striking the right balance of fuel costs and passenger-related delay costs incurred by the airline. We present models for integrated aircraft and passenger recovery with flight planning, both exact and approximate. From computational experiments on data provided by a European airline, we estimate approximately that reductions in passenger disruptions on the order of 66-83%, accompanied by small increases in fuel burn of 0.152-0.155% and total cost savings of 5.7-5.9% for the airline, may be achieved using our approach. We discuss the relative benefits of two mechanisms studied-specifically, flight speed changes and intentionally holding flight departures, and show significant synergies in applying these mechanisms. The results, compared to recovery without integrated flight planning, are increased swap possibilities during recovery, decreased numbers of flight cancelations, and fewer disruptions to passengers.

Analyzing Passenger Disruptions in the National Air Transportation System. Under Review

Many of the existing methods for evaluating an airline's on-time performance are based on flight-centric measures of flight delay. However, recent research has demonstrated that as much as 50% of passenger delays are caused by passenger travel disruptions, either flight cancelations or missed connections. The propensity for disruptions varies significantly across airports and carriers, based on key factors such as scheduling practices, network structures, and passenger connections. In this paper, we analyze the causes and costs of U.S. passenger travel disruptions by applying data analysis and statistical modeling to historical flight and passenger data. The passenger travel and delay data we use for our analysis is estimated from publicly available data sources using a methodology previously developed to disaggregate passenger demand data. We find that cancelations, which are the largest cause of disruption-related passenger delays, vary substantially across carriers, even when accounting for baseline variability across airports. Passenger and operational considerations also play a significant role in cancelation decisions. Regarding missed connections, much of the variability can be explained just by flight delays for the airport and carrier, though flight schedule construction is also a critical factor. Highly peaked (or banked) flight schedules tend to reduce connection times and therefore increase the risk of missed connections. Last, we demonstrate the importance of a variety of factors on the ease of reaccommodating disrupted passengers.

Airline Schedule Disruption Management. The impact of flight delays on connection loss

MATEC Web of Conferences, 2017

Air travel demand is important and many travellers choose to drive to larger airports instead of flying from a small airport for many reasons, especially availability of non-stop flights. Another reason is perceived reliability of service. Consultants have pointed to a large number of delays and cancellations as reasons for low passenger. However, the effect of these flight delays on actual travel times is less clear. Because connections are usually necessary when traveling from small airports, departure delays may lead to missed connections. In the case of a cancellation, need to wait several hours (often overnight) for the next flight due to the small number of daily departures. This paper evaluate the impact of delays and cancellations on the profit earned through the seats captured on new opened routes. This aspect of decision-making comes in the form of multi-objective problem by testing the impact of a new opened route in terms of flight delays costs, financial gain and the quality of the service provided to a target customer. The NSGA-II algorithm is adopted to generate a front of Paretooptimal compound of a number of optimal departure times to the new destination while ensuring the best fill rate, and a minimum flight delays. The experiences are based on the flights of the Royal Air Maroc Company on the Casablanca hub.

Disruption Management in Airline Operations Control – An Intelligent Agent-Based Approach

Web Intelligence and Intelligent Agents, 2010

Web Intelligence and Intelligent Agents 108 mechanisms, including costs criteria and negotiation protocols and (iv) examples of the problem solving algorithms used. In Section 5 we present the experimental setup and, in Section 6, we evaluate our approach, presenting and discussing the results. Finally, in Section 7, we conclude and give some insights on the future work. 2. Related Work and Current Tools and Systems The goal of this section is threefold. In Section 2.1 we present the related work regarding operations recovery. Research in this area has been made, mainly, through Operations Research (OR) techniques. Barnhart et al., (Barnhart et al., 2003) gives an overview of ORbased applications in the air transport industry. In Section 2.2 we describe and classify the current tools and systems in use at some worldwide airlines and in Section 2.3 we present some interesting examples of how agents are used in other applications domains and problems.

Analyzing passenger travel disruptions in the National Air Transportation System

mit.edu

Many of the existing methods for evaluating an airline's on-time performance are based on flight-centric measures of flight delay. However, recent research has demonstrated that as much as 50% of passenger delays are caused by passenger travel disruptions, either flight cancelations or missed connections. The propensity for disruptions varies significantly across airports and carriers, based on key factors such as scheduling practices, network structures, and passenger connections. In this paper, we analyze the causes and costs of U.S. passenger travel disruptions by applying data analysis and statistical modeling to historical flight and passenger data. The passenger travel and delay data we use for our analysis is estimated from publicly available data sources using a methodology previously developed to disaggregate passenger demand data. We find that cancelations, which are the largest cause of disruption-related passenger delays, vary substantially across carriers, even when accounting for baseline variability across airports. Passenger and operational considerations also play a significant role in cancelation decisions. Regarding missed connections, much of the variability can be explained just by flight delays for the airport and carrier, though flight schedule construction is also a critical factor. Highly peaked (or banked) flight schedules tend to reduce connection times and therefore increase the risk of missed connections. Last, we demonstrate the importance of a variety of factors on the ease of reaccommodating disrupted passengers.