A Rolling Horizon Based Algorithm for Solving Integrated Airline Schedule Recovery Problem

Airline disruption incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated airline schedule recovery problem, which considers flight recovery, aircraft recovery and crew recovery simultaneously. First we built an integer programming model which is based on traditional set partitioning model but including flight copy decision variables. Then a rolling horizon based algorithm is proposed to efficiently solve the model. Our algorithm decomposes the whole problem into smaller sub-problems by restricting swapping opportunities within each rolling period. All the flights are considered in each sub-problem to circumvent ‘myopic’ of traditional rolling horizon algorithm. Experimental results show that our method can provide competitive recovery solution in both solution quality and computation time.published_or_final_versio

Disruption Management - Impact assessment on the Operational Processes

Aquesta tesi vol identificar que és una alteració en les operacions de vol d'una companyia aèria i perquè la seva implementació és fonamental. S'han analitzat els processos més importants, ja que, gràcies a ells, indiferentment de la magnitud de la situació, l'aerolínia pot recuperar l'estabilitat en les operacions. Tenint en compte dues situacions de magnitud diferent, es vol cercar les opinions d'experts, en referència als processos operacionals i a les alteracions en la programació, per poder concloure així, si aplicant sempre els mateixos processos, indiferentment de la magnitud del esdeveniment, es pot aconseguir una estabilitat exitosa a la programacióEsta tesis pretende identificar qué es una alteración en las operaciones de vuelo de una compañía aérea y porque su implementación es primordial. Se han analizado los procedimientos más importantes, puesto que gracias a ellos, indiferentemente de la magnitud de un incidente, la aerolínea puede recuperar su estabilidad en la programación. Teniendo en cuenta dos situaciones de distinta magnitud, se ha querido averiguar las opiniones de diferentes expertos, tanto en procesos operacionales como en alteraciones en la programación. Para poder concluir así, si siempre aplicando los mismos procesos, indistintamente de la magnitud de la perturbación, se consigue un resultado exitoso.The aim of this thesis is to identify and understand what disruption management is and why in-place implementation strategies are important for airline operations. This is achieved by analysing the most important processes to ensure a successful operational recovery regardless of the scale of the scenario in place. Taking into consideration two different scale scenarios, the author inquires to a panel of experts their point of view about operational processes and disruption management, to be able to determine if the accuracy of those will make the disruption management process successful for the airline, regardless the dimension of it

Solving airline operations problems using specialized agents in a distributed multi-agent system

An airline schedule very rarely operates as planned. Problems related with aircrafts, crew members and passengers are common and the actions towards the solution of these problems are usually known as operations recovery. The Airline Operations Control Center (AOCC) tries to solve these problems with the minimum cost and satisfying all the required rules. In this paper we present the implementation of a Distributed Multi-Agent System (MAS) representing the existing roles in an AOCC, This MAS deals with several operational bases and for each type of operation problems it has several specialized software agents that implement different algorithms (heuristic, AI, OR, etc.), competing to find the best solution for each problem. We present a real case study taken from an AOCC where a crew recovery problem is solved. Computational results using a real airline schedule are presented, including a comparison with a solution for the same problem found by the human operators in the AOCC. We show that, even in simple problems and when comparing with solutions found by human operators, it is possible to find valid solutions, in less time and with a smaller cost

Minimizing airport peaks problem by improving airline operations performance through an agent based system

Airports are important infra-structures for the air transportationbusiness. One of the major operational constraints is the peak of passengers inspecific periods of time. Airline companies take into consideration the airportcapacity when building the airline schedule and, because of that, the executionof the airline operational plan can contribute to improve or avoid airport peakproblems. The Airline Operations Control Center (AOCC) tries to solveunexpected problems that might occur during the airline operation. Problemsrelated to aircrafts, crewmembers and passengers are common and the actionstowards the solution of these problems are usually known as operationsrecovery. In this paper we propose a way of measuring the AOCC performancethat takes into consideration the relation that exists between airline scheduleand airport peaks. The implementation of a Distributed Multi-Agent System(MAS) representing the existing roles in an AOCC, is presented. We show thatthe MAS contributes to minimize airport peaks without increasing theoperational costs of the airlines

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to things going right in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human success data generated by the aviation system, from personnel and within the supporting infrastructure

Human Fatigue Predictions in Complex Aviation Crew Operational Impact Conditions

In this last decade, several regulatory frameworks across the world in all modes of transportation had brought fatigue and its risk management in operations to the forefront. Of all transportation modes air travel has been the safest means of transportation. Still as part of continuous improvement efforts, regulators are insisting the operators to adopt strong fatigue science and its foundational principles to reinforce safety risk assessment and management. Fatigue risk management is a data driven system that finds a realistic balance between safety and productivity in an organization. This work discusses the effects of mathematical modeling of fatigue and its quantification in the context of fatigue risk management for complex global logistics operations. A new concept called Duty DNA is designed within the system that helps to predict and forecast sleep, duty deformations and fatigue. The need for a robust structure of elements to house the components to measure and manage fatigue risk in operations is also debated. By operating on the principles of fatigue management, new science-based predictive, proactive and reactive approaches were designed for an industry leading fatigue risk management program Accurately predicting sleep is very critical to predicting fatigue and alertness. Mathematical models are being developed to track the biological processes quantitatively and predicting temporal profile of fatigue given a person’s sleep history, planned work schedule including night and day exposure. As these models are being continuously worked to improve, a new limited deep learning machine learning based approach is attempted to predict fatigue for a duty in isolation without knowing much of work schedule history. The model within also predicts the duty disruptions and predicted fatigue at the end state of duty