다중공항에서 지상 지연 프로그램 발생시 지연전파를 고려한 항공사의 운항 일정 변경

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 문일경.본 연구의 목적은 항공 교통을 제어하는 중요한 수단 중 하나인 지상 지연 프로그램(GDP)이 발생할 경우 공항의 변경된 수용력에 대응하도록 항공사의 관점에서 항공편을 재조정하는데 도움을 주는 것이다. 단일 공항이 아닌 다중 공항으로 확장하여 동일한 공항뿐 아니라 다른 공항으로부터의 지연 전파를 고려했으며, 항공기 및 승무원의 계획된 일정에서 발생하는 현실적인 비용을 포함했다. GDP가 발행되면 항공사들은 변경된 시간대에 맞춰 항공편을 재조정할 수 있는 짧은 시간이 주어진다. 각 공항에는 수용력이 있으며, 특히 들어오는 항공기를 수용할 수 있는 용량인 공항 수용률(AAR)이 있다. 이 연구에서 비행 스케줄을 재조정하기 위해 혼합 정수 선형 프로그래밍 모델을 세웠다. 또한, 미래의 불확실성을 다루기 위해, MILP의 두 가지 버전을 사용하였다. AAR이 어느 시점에 다시 바뀌는 시나리오를 만든 후, 각 시나리오 별로 총 관련 비용을 최소화하는 솔루션을 도출하는 최적 모델과 모든 시나리오 솔루션의 총 관련 비용의 기댓값을 최소화하는 솔루션을 도출하는 추계 모델을 제시하고 서로 비교하였다The purpose of this thesis is to reschedule flights from the airline company’s perspective to correspond to the airport’s changed capacity in the event of a ground delay program (GDP), one of the important means of controlling air traffic. We considered delay propagation not only within the same airport but within other airports by extending the setup to include several airports rather than a single airport. We also included realistic costs from planned schedules of the aircraft and crew. When a GDP is issued, airlines are given a short time to reschedule flights in time for the changed slot. Each airport has its own capacity, especially the airport acceptance rate (AAR), which is a capacity that can accommodate incoming aircraft. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. To handle the uncertainty of future scheduling, two versions of the MILP model may be applied. With scenarios in which the AAR changes again, an optimal model that obtains a minimizing total relevant cost in each scenario solution and a stochastic model solution that obtains a minimizing expectation of the total relevant cost of all scenarios are presented and compared.Chapter 1 Introduction 1 Chapter 2 Literature review 3 Chapter 3 Mathematical model 5 3.0 Model description 5 3.1 Multi-airport Scenario-based Optimal Rescheduling Problem 10 3.2 Multi-airport Scenario-based Stochastic Rescheduling Problem 13 Chapter 4 Computational experiments 14 4.0 Settings 14 4.1 Experiment 1 16 4.2 Experiment 2 18 4.3 Experiment 3 19 4.4 Experiment 4 20 Chapter 5 Conclusions 25 Appendix 27 Appendix A. 27 Appendix B. 28 Bibliography 31 국문초록 35석

MASALAH GROUND-HOLDING DENGAN DUA TERMINAL DALAM PENGENDALIAN LALU LINTAS UDARA

Setiap maskapai penerbangan memiliki strategi untuk meminimumkan biaya penundaan antara lain kebijakan ground-holding. Kebijakan ini mampu membuat maskapai untuk menahan pesawatnya di terminal keberangkatan meskipun sudah dijadwalkan untuk lepas landas sehingga setelah sampai di kota tujuan dapat langsung mendarat tanpa harus menunggu di udara. Dalam karya ilmiah ini dibahas tentang penentuan waktu keberangkatan dan kedatangan dari setiap penerbangan yang dapat meminimumkan biaya penundaan. Masalah ground-holding dengan dua terminal dalam pengendalian lalu lintas udara dapat diformulasikan menjadi masalah Pure 0-1 integer linear programming. Dalam penelitian ini dibahas dua kasus dari kebijakan ground-holding. Kasus pertama: seluruh penerbangan dapat menahan pesawatnya di terminal keberangkatan dan dapat tertahan di udara. Kasus kedua: seluruh penerbangan hanya menahan pesawatnya di terminal keberangkatan sehingga pada saat sampai di kota tujuan tidak tertahan di udara. Diberikan simulasi dengan mengasumsikan terdapat 26 penerbangan dan jadwal waktu keberangkatan serta waktu kedatangan dari setiap penerbangan. Jika penerbangan terjadi dari terminal keberangkatan kota awal menuju terminal kedatangan kota tujuan, dengan integer programming tersebut akan diperoleh waktu keberangkatan dan waktu kedatangan yang meminimumkan biaya penundaan

Hybrid metaheuristic optimization algorithm for strategic planning of {4D} aircraft trajectories at the continent scale

International audienceGlobal air-traffic demand is continuously increasing. To handle such a tremendous traffic volume while maintaining at least the same level of safety, a more efficient strategic trajectory planning is necessary. In this work, we present a strategic trajectory planning methodology which aims to minimize interaction between aircraft at the European-continent scale. In addition, we propose a preliminary study that takes into account uncertainties of aircraft positions in the horizontal plane. The proposed methodology separates aircraft by modifying their trajectories and departure times. This route/departure-time assignment problem is modeled as a mixed-integer optimization problem. Due to the very high combinatorics involved in the continent-scale context (involving more than 30,000 flights), we develop and implement a hybrid-metaheuristic optimization algorithm. In addition, we present a computationally-efficient interaction detection method for large trajectory sets. The proposed methodology is successfully implemented and tested on a full-day simulated air traffic over the European airspace, yielding to an interaction-free trajectory plan

Feedback Control of the National Airspace System

This paper proposes a general modeling framework adapted to the feedback control of traffic flows in Eulerian models of the National Airspace System. It is shown that the problems of scheduling and routing aircraft flows in the National Airspace System can be posed as the control of a network of queues with load-dependent service rates. Focus can then shift to developing techniques to ensure that the aircraft queues in each airspace sector, which are an indicator of the air traffic controller workloads, are kept small. This paper uses the proposed framework to develop control laws that help prepare the National Airspace System for fast recovery from a weather event, given a probabilistic forecast of capacities. In particular, the model includes the management of airport arrivals and departures subject to runway capacity constraints, which are highly sensitive to weather disruptions.National Science Foundation (U.S.) (Contract ECCS-0745237)United States. National Aeronautics and Space Administration (Contract NNA06CN24A

Stochastic Modelling of Aircraft Queues: A Review

In this paper we consider the modelling and optimal control of queues of aircraft waiting to use the runway(s) at airports, and present a review of the related literature. We discuss the formulation of aircraft queues as nonstationary queueing systems and examine the common assumptions made in the literature regarding the random distributions for inter-arrival and service times. These depend on various operational factors, including the expected level of precision in meeting pre-scheduled operation times and the inherent uncertainty in airport capacity due to weather and wind variations. We also discuss strategic and tactical methods for managing congestion at airports, including the use of slot controls, ground holding programs, runway configuration changes and aircraft sequencing policies

Hybrid Metaheuristic for Air Traffic Management with Uncertainty

International audience To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this chapter presents a strategic trajectory planning approach aiming at minimizing the number of conflicts between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each aircraft at a nation-wide scale.In real-life situations, aircraft may arrive at a given position with some uncertainties on its curvilinear abscissa due to external events. To ensure robustness of the strategic trajectory plan, the aircraft arrival time to any given position will be represented here by a probabilistic distribution over its nominal assigned arrival time.The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French airspace for which a conflict-free and robust 4D trajectory plan is produced. Document type: Part of book or chapter of boo

Multi-Period Stochastic Resource Planning: Models, Algorithms and Applications

This research addresses the problem of sequential decision making in the presence of uncertainty in the professional service industry. Specifically, it considers the problem of dynamically assigning resources to tasks in a stochastic environment with both the uncertainty of resource availability due to attrition, and the uncertainty of job availability due to unknown project bid outcome. This problem is motivated by the resource planning application at the Hewlett Packard (HP) Enterprises. The challenge is to provide resource planning support over a time horizon under the influence of internal resource attrition and demand uncertainty. To ensure demand is satisfied, the external contingent resources can be engaged to make up for internal resource attrition. The objective is to maximize profitability by identifying the optimal mix of internal and contingent resources and their assignments to project tasks under explicit uncertainty. While the sequential decision problems under uncertainty can often be modeled as a Markov decision process (MDP), the classical dynamic programming (DP) method using the Bellman’s equation suffers the well-known curses-of-dimensionality and only works for small size instances. To tackle the challenge of curses-of-dimensionality this research focuses on developing computationally tractable closed-loop Approximate Dynamic Programming (ADP) algorithms to obtain near-optimal solutions in reasonable computational time. Various approximation schemes are developed to approximate the cost-to-go function. A comprehensive computational experiment is conducted to investigate the performance and behavior of the ADP algorithm. The performance of ADP is also compared with that of a rolling horizon approach as a benchmark solution. Computational results show that the optimization model and algorithm developed in this thesis are able to offer solutions with higher profitability and utilization of internal resource for companies in the professional service industry

Computational optimization of networks of dynamical systems under uncertainties: application to the air transportation system

To efficiently balance traffic demand and capacity, optimization of air traffic management relies on accurate predictions of future capacities, which are inherently uncertain due to weather forecast. This dissertation presents a novel computational efficient approach to address the uncertainties in air traffic system by using chance constrained optimization model. First, a chance constrained model for a single airport ground holding problem is proposed with the concept of service level, which provides a event-oriented performance criterion for uncertainty. With the validated advantage on robust optimal planning under uncertainty, the chance constrained model is developed for joint planning for multiple related airports. The probabilistic capacity constraints of airspace resources provide a quantized way to balance the solution’s robustness and potential cost, which is well validated against the classic stochastic scenario tree-based method. Following the similar idea, the chance constrained model is extended to formulate a traffic flow management problem under probabilistic sector capacities, which is derived from a previous deterministic linear model. The nonlinearity from the chance constraint makes this problem difficult to solve, especially for a large scale case. To address the computational efficiency problem, a novel convex approximation based approach is proposed based on the numerical properties of the Bernstein polynomial. By effectively controlling the approximation error for both the function value and gradient, a first-order algorithm can be adopted to obtain a satisfactory solution which is expected to be optimal. The convex approximation approach is evaluated to be reliable by comparing with a brute-force method.Finally, the specially designed architecture of the convex approximation provides massive independent internal approximation processes, which makes parallel computing to be suitable. A distributed computing framework is designed based on Spark, a big data cluster computing system, to further improve the computational efficiency. By taking the advantage of Spark, the distributed framework enables concurrent executions for the convex approximation processes. Evolved from a basic cloud computing package, Hadoop MapReduce, Spark provides advanced features on in-memory computing and dynamical task allocation. Performed on a small cluster of six workstations, these features are well demonstrated by comparing with MapReduce in solving the chance constrained model

Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of automation were proposed for integrated flight planning and re-routing functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This article provides a comprehensive review of Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, with a special focus on the recent advances introduced in the CNS+A research context. In the first section, a brief introduction is given, together with an overview of the main international research initiatives where this topic has been studied, and the problem statement is provided. The second section introduces the mathematical formulation and the third section reviews the numerical solution techniques, including discretisation and optimisation methods for the specific problem formulated. The fourth section summarises the strategies to articulate the preferences and to select optimal trajectories when multiple conflicting objectives are introduced. The fifth section introduces a number of models defining the optimality criteria and constraints typically adopted in MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, airspace and airport operations