Disruption management in the airline industry - Concepts, models and methods (original) (raw)

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...

Operational Problems Recovery in Airlines – A Specialized Methodologies Approach

Disruption management is one of the most important scheduling problems in the airline industry because of the elevated costs associated, however this is relatively new research area comparing for example with fleet and tail assignment. The major goal to solve this kind of problem is to achieve a feasible solution for the airline company minimizing the several costs involved and within time constraints. An approach to solve operational problems causing disruptions is presented using different specialized methodologies for the problems with aircrafts and crewmembers including flight graph based with meta-heuristic optimization algorithms. These approaches were built to fit on a multi-agent system with specialist agents solving disruptions. A comparative analysis of the algorithms is also presented. Using a complete month real dataset we demonstrate an example how the system handled disruption events. The resulting application is able to solve disruption events optimizing costs and respecting operational constraints.

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."

An integrated decision support tool for airlines schedule recovery during irregular operations

European Journal of Operational Research, 2008

This paper presents a decision support tool for airlines schedule recovery during irregular operations. The tool provides airlines control centers with the capability to develop a proactive schedule recovery plan that integrates all flight resources. A rolling horizon modeling framework, which integrates a schedule simulation model and a resource assignment optimization model, is adopted for this tool. The schedule simulation model projects the list of disrupted flights in the system as function of the severity of anticipated disruptions. The optimization model examines possible resource swapping and flight re-quoting to generate an efficient schedule recovery plan that minimizes flight delays and cancellations. A detailed example that illustrates the application of the tool to recover the schedule of a major US air-carrier during a hypothetical ground delay program scenario is presented. The results of several experiments that illustrates overall model performance in terms of solution quality and computation experience are also given. Published by Elsevier B.V.

Aircraft schedule recovery problem – a dynamic modeling framework for daily operations

EWGT, 2015

In this paper we present an innovative dynamic modeling framework to the aircraft schedule recovery problem (ASRP). The ASRP can be defined as the problem of modifying the flight and aircraft schedules to compensate the presence of irregular operations that result in the temporary or permanent unavailability of aircraft. Previous works on this topic often make use of static disruption test scenarios, simulating a set of disrupted events in a single time evaluation. The modeling framework here presented, named Disruption Set Solver (DSS), is innovative because it tackles aircraft schedule disruptions in a dynamic way (i.e., the recovery problem is solved as disruptions happen, involving the solutions of new disruption but also considering decision the incumbent solution) and because it is the first time that parallel time-space networks are used to track individual aircraft in the fleet. The framework relies on the combined usage of an efficient aircraft selection algorithm and a linear-programming model based on parallel aircraft specific time-space networks. The goal of the optimization model used to solve the ASRP is to minimize costs, including operational, passengers delay and cancellation costs. The decision variables involve the cancellation of flights, the delay of flights and the swap of aircraft between flights. The validation of the framework is done applying it to a set of real disruptive days in the operation of a major African airline. The results suggest two conclusions: (1) that the traditional static approach can lead to unreliable solutions, neglecting practical challenge and underestimating the disruption costs; and (2) that the proposed dynamic DSS framework can solve real aircraft schedule disruption problems within a time-window suitable for real-time operations.

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.

Airline disruption management--Perspectives, experiences and outlook

2007

Since the deregulation of many markets, airlines have become more concerned with developing an optimal flight schedule, allowing little slack to accommodate variations from the optimal solution. During operation, the planned schedules often have to be revised because of disruptions caused by severe weather, technical problems and crew sickness. Thus, airline disruption management techniques have emerged.

OPTIMIZATION APPROACHES TO AIRLINE INDUSTRY CHALLENGES: Airline Schedule Planning and Recovery

2009

The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems. These problems have several challenging characteristics, the two most challenging of which include: 1) they span long- and short-term horizons, from strategic planning of flight schedules operated several months into the future, to real-time operations in which strategies must be developed and implemented immediately to recover scheduled operations from disruptions; and 2) they include multiple resources that must be coordinated, such as aircraft, crews, and passengers. While optimization approaches have been essential to the airline industry and effective in developing aircraft and crew schedules, historical models and approaches often fail to capture the complexity of airline operations. For example, approaches, often by necessity, involve a sequential, rather than an integrated process to develop schedules for aircraft and crews, and moreover, the pro...

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.

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.