Optimising airline maintenance scheduling decisions

Airline maintenance scheduling (AMS) studies how plans or schedules are constructed to ensure that a fleet is efficiently maintained and that airline operational demands are met. Additionally, such schedules must take into consideration the different regulations airlines are subject to, while minimising maintenance costs. In this thesis, we study different formulations, solution methods, and modelling considerations, for the AMS and related problems to propose two main contributions. First, we present a new type of multi-objective mixed integer linear programming formulation which challenges traditional time discretisation. Employing the concept of time intervals, we efficiently model the airline maintenance scheduling problem with tail assignment considerations. With a focus on workshop resource allocation and individual aircraft flight operations, and the use of a custom iterative algorithm, we solve large and long-term real-world instances (16000 flights, 529 aircraft, 8 maintenance workshops) in reasonable computational time. Moreover, we provide evidence to suggest, that our framework provides near-optimal solutions, and that inter-airline cooperation is beneficial for workshops. Second, we propose a new hybrid solution procedure to solve the aircraft recovery problem. Here, we study how to re-schedule flights and re-assign aircraft to these, to resume airline operations after an unforeseen disruption. We do so while taking operational restrictions into account. Specifically, restrictions on aircraft, maintenance, crew duty, and passenger delay are accounted for. The flexibility of the approach allows for further operational restrictions to be easily introduced. The hybrid solution procedure involves the combination of column generation with learning-based hyperheuristics. The latter, adaptively selects exact or metaheuristic algorithms to generate columns. The five different algorithms implemented, two of which we developed, were collected and released as a Python package (Torres Sanchez, 2020). Findings suggest that the framework produces fast and insightful recovery solutions

Railway Crew Rescheduling with Retiming

Railway operations are disrupted frequently, e.g. the Dutch railway network experiences about three large disruptions per day on average. In such a disrupted situation railway operators need to quickly adjust their resource schedules. Nowadays, the timetable, the rolling stock and the crew schedule are recovered in a sequential way. In this paper, we model and solve the crew rescheduling problem with retiming. This problem extends the crew rescheduling problem by the possibility to delay the departure of some trains. In this way we partly integrate timetable adjustment and crew rescheduling. The algorithm is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computational time, retiming is allowed only for a limited number of trains where it seems very promising. Computational experiments with real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming.

Multi-agent system for an airline operations control centre

Estágio realizado na TAP Portugal e orientado pelo Eng.º António CastroTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Railway Crew Rescheduling with Retiming

Railway operations are disrupted frequently, e.g. the Dutch railway network experiences about three large disruptions per day on average. In such a disrupted situation railway operators need to quickly adjust their resource schedules. Nowadays, the timetable, the rolling stock and the crew schedule are recovered in a sequential way. In this paper, we model and solve the crew rescheduling problem with retiming. This problem extends the crew rescheduling problem by the possibility to delay the departure of some trains. In this way we partly integrate timetable adjustment and crew rescheduling. The algorithm is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computational time, retiming is allowed only for a limited number of trains where it seems very promising. Computational experiments with real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming

Aircraft Maintenance Routing Problem – A Literature Survey

The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch

Dynamic passenger recovery model for airline disruption management

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunication and Innovation at (MSc.MTI) at Strathmore UniversityAirline operations experience schedule disruptions every day. These schedule disruptions require intervention from the airline operations controllers through schedule recovery. In a hub and spoke airline network model, a disruption such as a flight cancellation can affect passenger itineraries in multiple fight legs, making it hard for airlines to re-accommodate disrupted passengers within a short time period. The current airline recovery solutions do not explicitly consider passenger recovery. This dissertation investigates the passenger recovery process by considering the challenges faced by passengers during a schedule disruption, the current solutions used to recover disrupted passengers and how a suitable solution can be designed, developed, tested and validated to ensure that it solves these challenges. Data was collected from existing records of flight schedules and passenger bookings. The data collected was used as input to an optimisation model for passenger recovery. Scrum Agile Development methodology was adopted as the software methodology for developing the solution. A proof of concept web application was developed to make passenger recovery easier and reduce operational cost and passenger delay time. An optimization model was developed based on IBM ILOG CPLEX optimiser to help solve disruptions faster. Testing was conducted by both the developer and a selected sample of airline industry users

Evolutionary Computation methods applied to Operational Control Centers

Durante a execução de um plano operacional, existe a possibilidade do mesmo sofrer rupturas causadas por eventos não esperados. As rupturas afetam pelo menos três dimensðes sobre as quais as companhias aéreas e os centros de controlo operacional devem ter em conta, nomeadamente os passageiros, a tripulação e os aviðes. Normalmente, um ruptura é um estado durante o qual uma operação que esteja a ser executada é afetada por um desvio (que é grande o suficiente para causar uma mudança) do plano original e, por vezes, levando a que o plano não seja execuível. Exem- plos de eventos que podem causar rupturas são condiçðes meteorológicas, ameaças ou ataques terroristas e avarias nos aviðes.Disruption Management, pode então ser definido como o processo que começa após detectar o desvio do plano original. Depois da ruptura, o plano é mudado e nunca mais vai estar tão perto da solução ótima quanto estava antes da ruptura, sendo que pode mesmo vir a ser impossível a continuação do plano. De qualquer maneira existe a necessidade de rever o plano e de minimizar o impacto causado pela ruptura.O MASDIMA é uma grande ajuda para os Centros de Controlo Operacionais das companhias aéreas encontrarem soluçðes para rupturas durante a execução de um plano operacional, e para melhorar quer o tempo de computação quer a qualidade das soluçðes, existe a necessidade de melhorar o sistema. Isto traduz-se em minimizar o impacto quer a ao nvel dos custos quer ao nvel dos atrasos.Para lidar com este problema serão implementados três agentes, sendo que cada um representa um algoritmo evolutivo diferente (Particle Swarm Optimisation, Ant Colony Optimisation e Ge- netic Algorithms) e estarão relacionados com a dimensão avião relativa ao problema. Estes três agentes serão implementados num Sistema Multi-Agente chamado de MASDIMA que represen- tará o Centro de Controlo Operacional.During the execution of an operational plan, there is the likelihood of this plan being affected by some disruptions caused by unexpected events. The disruptions affect at least three dimensions that airline companies and the operational control centers must take into account which are pas- senger, crew and aircraft. Usually, a disruption is a state during which the current operation being executed is affected by a deviation (which is large enough to cause a change) from the original plan, and sometimes unfortunately it leads to an unfeasible plan. Examples of events that might cause disruptions are bad weather, threats or terrorist attacks and aircraft malfunctions.Disruption Management, can be defined as the process that starts after the deviation from the original plan is detected. After the disruption, the plan is changed and it will no longer be as close as it was from an optimal plan or it can even turn into an unfeasible plan. Either way there is a need to review the plan and try to minimize the impact caused by the disruption.MASDIMA is useful to help Airline Operation Control Centers finding a solution to disrup- tions during an operational plan, and in order to improve both computing time and the quality of solutions, there is a need to improve the system. This will translate in minimising the impact both in terms of costs or delays.To deal with that problem three agents will be implemented that will reflect, each one, different evolutionary computation algorithms (Particle Swarm Optimisation, Ant Colony Optimisation and Genetic Algorithms) and are related to the aircraft dimension of the problem. These agents will be implemented on a Multi-Agent System named MASDIMA that represents an Operation Control Center

The daily tail assignment problem under operational uncertainty using look-ahead maintenance constraints

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordThe tail assignment problem is a critical part of the airline planning process that assigns specific aircraft to sequences of flights, called lines-of-flight, to satisfy operational constraints. The aim of this paper is to develop an operationally flexible method, based upon the one-day routes business model, to compute tail assignments that satisfy short-range—within the next three days—aircraft maintenance requirements. While maintenance plans commonly span multiple days, the methods used to compute tail assignments for the given plans can be overly complex and provide little recourse in the event of schedule perturbations. The presented approach addresses operational uncertainty by using solutions from the one-day routes aircraft maintenance routing approach as input. The daily tail assignment problem is solved with an objective to satisfy maintenance requirements explicitly for the current day and implicitly for the subsequent two days. A computational study will be performed to assess the performance of exact and heuristic solution algorithms that modify the input lines-of-flight to reduce maintenance misalignments. The daily tail assignment problem and the developed algorithms are demonstrated to compute solutions that effectively satisfy maintenance requirements when evaluated using input data collected from three different airlines