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

강화학습을 이용한 공항 임시폐쇄 상황에서의 항공 일정계획 복원

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 문일경.An airline scheduler plans flight schedules with efficient resource utilization. However, unpredictable events, such as the temporary closure of an airport, disrupt planned flight schedules. Therefore, recovering disrupted flight schedules is essential for airlines. We propose Q-learning and Double Q-learning algorithms using reinforcement learning approach for the aircraft recovery problem (ARP) in cases of temporary closures of airports. We use two recovery options: delaying departures of flights and swapping aircraft. We present an artificial environment of daily flight schedules and the Markov decision process (MDP) for the ARP. We evaluate the proposed approach on a set of experiments carried out on a real-world case of a Korean domestic airline. Computational experiments show that reinforcement learning algorithms recover disrupted flight schedules effectively, and that our approaches flexibly adapt to various objectives and realistic conditions.항공사는 보유하고 있는 자원을 최대한 효율적으로 사용하여 항공 일정계획을 수립하기 위해 비용과 시간을 많이 소모하게 된다. 하지만 공항 임시폐쇄와 같은 긴급 상황이 발생하면 항공편의 비정상 운항이 발생하게 된다. 따라서 이러한 상황이 발생하였을 때, 피해를 최대한 줄이기 위해 항공 일정계획을 복원하게 된다. 본 연구는 강화학습을 이용하여 공항 임시폐쇄 상황에서 항공 일정계획 복원 문제를 푼다. 본 연구에서는 항공기 복원 방법으로 항공편 지연과 항공기 교체의 두 가지 방법을 채택하였으며, 항공 일정계획 복원 문제에 강화학습을 적용하기 위해서 마르코프 결정 과정과 강화학습 환경을 구축하였다. 본 실험을 위해 대한민국 항공사의 실제 국내선 항공 일정계획을 사용하였다. 강화학습 알고리즘을 사용하여 기존의 연구에 비해 항공 일정계획을 효율적으로 복원하였으며, 여러 현실적인 조건과 다양한 목적함수에 유연하게 적용하였다.Abstract i Contents iv List of Tables v List of Figures vi Chapter 1 Introduction 1 Chapter 2 Literature Review 7 Chapter 3 Problem statement 11 3.1 Characteristics of aircraft, flights, and flight schedule requirements 11 3.2 Definitions of disruptions and recovery options and objectives of the problem 13 3.3 Assumptions 16 3.4 Mathematical formulations 19 Chapter 4 Reinforcement learning for aircraft recovery 24 4.1 Principles of reinforcement learning 24 4.2 Environment 27 4.3 Markov decision process 29 Chapter 5 Reinforcement learning algorithms 33 5.1 Q-learning algorithm 33 5.2 Overestimation bias and Double Q-learning algorithm 36 Chapter 6 Computational experiments 38 6.1 Comparison between reinforcement learning and existing algorithms 39 6.2 Performance of the TLN varying the size of delay arcs 46 6.3 Aircraft recovery for a complex real-world case: a Korean domestic airline 48 6.4 Validation for different objectives 54 6.5 Managerial insights 57 Chapter 7 Conclusions 59 Bibliography 61 국문초록 69Maste

Impact of the organizational structure on operations management : the airline operations control centre case study

Documento confidencial. Não pode ser disponibilizado para consultaTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Methods for Improving Robustness and Recovery in Aviation Planning.

In this dissertation, we develop new methods for improving robustness and recovery in aviation planning. In addition to these methods, the contributions of this dissertation include an in-depth analysis of several mathematical modeling approaches and proof of their structural equivalence. Furthermore, we analyze several decomposition approaches, the difference in their complexity and the required computation time to provide insight into selecting the most appropriate formulation for a particular problem structure. To begin, we provide an overview of the airline planning process, including the major components such as schedule planning, fleet assignment and crew planning approaches. Then, in the first part of our research, we use a recursive simulation-based approach to evaluate a flight schedule's overall robustness, i.e. its ability to withstand propagation delays. We then use this analysis as the groundwork for a new approach to improve the robustness of an airline's maintenance plan. Specifically, we improve robustness by allocating maintenance rotations to those aircraft that will most likely benefit from the assignment. To assess the effectiveness of our approach, we introduce a new metric, maintenance reachability, which measures the robustness of the rotations assigned to aircraft. Subsequently, we develop a mathematical programming approach to improve the maintenance reachability of this assignment. In the latter part of this dissertation, we transition from the planning to the recovery phase. On the day-of-operations, disruptions often take place and change aircraft rotations and their respective maintenance assignments. In recovery, we focus on creating feasible plans after such disruptions have occurred. We divide our recovery approach into two phases. In the first phase, we solve the Maintenance Recovery Problem (MRP), a computationally complex, short-term, non-recurrent recovery problem. This research lays the foundation for the second phase, in which we incorporate recurrence, i.e. the property that scheduling one maintenance event has a direct implication on the deadlines for subsequent maintenance events, into the recovery process. We recognize that scheduling the next maintenance event provides implications for all subsequent events, which further increases the problem complexity. We illustrate the effectiveness of our methods under various objective functions and mathematical programming approaches.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91539/1/mlapp_1.pd

Integrating the fleet assignment model with uncertain demand

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.One of the main challenges facing the airline industry is planning under uncertainty, especially in the context of schedule disruptions. The robust models and solution algorithms that have been proposed and developed to handle the uncertain parameters will be discussed. Fleet assignment models (FAM) are used by many airlines to assign aircraft to fights in a schedule to maximize profit. In the context of FAM, the goal of robustness is to produce solutions that perform well relative to uncertainties in demand and operation. In this thesis, we introduce new FAMs (i.e. DFAM1 and DFAM2) that tackles the common problem associated with aircraft utilization. Subsequently, stochastic programming (SP) is presented as a method of choice for the research. Through the use of a two-stage SP with recourse technique, the DFAMs are extended to SP-FAMs (SP-FAM1 and SP-FAM2). The main distinction of the SP-FAM compared with other FAMs is that, given a stochastic passenger demand, it gives a strategic fleet assignment solution that hedges against all possible tactical solutions. In addition, we have a tactical solution for every scenario. In generating the demand scenarios, we use a network-simulation model embedded with a time-series engine that gives a snapshot of one week that is representative of any other week of the scheduling season. We later outline the approach of solving the SP-FAMs where the schedule is compacted through several preprocessing steps before inputting it into SAS-AMPL converter. The SAS-AMPL converter prepares all the data into readable AMPL format. Finally, we execute the optimizer using a FortMP solver (integrated in AMPL) that invokes branch-and-bound algorithm. We give a proof of concept using real data from a Middle East airline. Our investigations establish clear benefits of the recourse FAM compared to alternative models. Finally, we propose areas of future research to improve SP-FAM robustness through solution algorithms, revenue management (RM) effects, calibration of network-simulation models and system integration

Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules

For a number of years, the United States Federal Government has been formulating the Next Generation Air Transportation System plans for National Airspace System improvement. These improvements attempt to address air transportation holistically, but often address individual improvements in one arena such as ground or in-flight equipment. In fact, air transportation system designers have had only limited success using traditional Operations Research and parametric modeling approaches in their analyses of innovative operations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be deployed with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed

Approximate Algorithms for the Combined arrival-Departure Aircraft Sequencing and Reactive Scheduling Problems on Multiple Runways

The problem addressed in this dissertation is the Aircraft Sequencing Problem (ASP) in which a schedule must be developed to determine the assignment of each aircraft to a runway, the appropriate sequence of aircraft on each runway, and their departing or landing times. The dissertation examines the ASP over multiple runways, under mixed mode operations with the objective of minimizing the total weighted tardiness of aircraft landings and departures simultaneously. To prevent the dangers associated with wake-vortex effects, separation times enforced by Aviation Administrations (e.g., FAA) are considered, adding another level of complexity given that such times are sequence-dependent. Due to the problem being NP-hard, it is computationally difficult to solve large scale instances in a reasonable amount of time. Therefore, three greedy algorithms, namely the Adapted Apparent Tardiness Cost with Separation and Ready Times (AATCSR), the Earliest Ready Time (ERT) and the Fast Priority Index (FPI) are proposed. Moreover, metaheuristics including Simulated Annealing (SA) and the Metaheuristic for Randomized Priority Search (Meta-RaPS) are introduced to improve solutions initially constructed by the proposed greedy algorithms. The performance (solution quality and computational time) of the various algorithms is compared to the optimal solutions and to each other. The dissertation also addresses the Aircraft Reactive Scheduling Problem (ARSP) as air traffic systems frequently encounter various disruptions due to unexpected events such as inclement weather, aircraft failures or personnel shortages rendering the initial plan suboptimal or even obsolete in some cases. This research considers disruptions including the arrival of new aircraft, flight cancellations and aircraft delays. ARSP is formulated as a multi-objective optimization problem in which both the schedule\u27s quality and stability are of interest. The objectives consist of the total weighted start times (solution quality), total weighted start time deviation, and total weighted runway deviation (instability measures). Repair and complete regeneration approximate algorithms are developed for each type of disruptive events. The algorithms are tested against difficult benchmark problems and the solutions are compared to optimal solutions in terms of solution quality, schedule stability and computational time

Influences on aircraft target off-block time prediction accuracy

With Airport Collaborative Decision Making (A-CDM) as a generic concept of working together of all airport partners, the main aim of this research project was to increase the understanding of the Influences on the Target Off-Block Time (TOBT) Prediction Accuracy during A-CDM. Predicting the TOBT accurately is important, because all airport partners use it as a reference time for the departure of the flights after the aircraft turn-round. Understanding such influencing factors is therefore not only required for finding measures to counteract inaccurate TOBT predictions, but also for establishing a more efficient A-CDM turn-round process. The research method chosen comprises a number of steps. Firstly, within the framework of a Cognitive Work Analysis, the sub-processes as well as the information requirements during turn-round were analysed. Secondly, a survey approach aimed at finding and describing situations during turn-round that are critical for TOBT adherence was pursued. The problems identified here were then investigated in field observations at different airlines’ operation control rooms. Based on the findings from these previous steps, small-scale human-in-the-loop experiments were designed aimed at testing hypotheses about data/information availability that influence TOBT predictability. A turn-round monitoring tool was developed for the experiments. As a result of this project, the critical chain of turn-round events and the decisions necessary during all stages of the turn-round were identified. It was concluded that information required but not shared among participants can result in TOBT inaccuracy swings. In addition, TOBT predictability was shown to depend on the location of the TOBT turn-round controller who assigns the TOBT: More reliable TOBT predictions were observed when the turn-round controller was physically present at the aircraft. During the experiments, TOBT prediction could be improved by eight minutes, if available information was cooperatively shared ten minutes prior turn-round start between air crews and turn-round controller; TOBT prediction could be improved by 15 minutes, if additional information was provided by ramp agents five minutes after turnround start