Air Traffic Scheduling Research Articles

Mathematical model for optimization in air traffic scheduling management during the COVID-19 pandemic

In this paper, the object of the study is determining air traffic scheduling management by optimizing mathematical models. The problem in this study is that the COVID-19 pandemic has significantly disrupted air traffic, resulting in changes to regulations, travel restrictions and a decrease in passenger demand. One of the problems that must be resolved is how to organize and adapt flight schedules to current conditions by focusing on mathematical models that are used to optimize or increase the efficiency of managing flight schedules or air traffic. Mathematical models can help find ways to optimize the use of available resources, such as airport capacity, flight routes and flight frequency. The results obtained in this research are a mathematical model that specifically takes into account the variables involved in setting air traffic schedules during the COVID-19 pandemic so that capacity limits at airports and airspace are always normal. An optimization model was developed from previous research, namely the model that takes into account ground and air delays as well as the use of alternative paths and avoids deviations from the initial more accurate flight plan, which overall indicates that the maximum time and maximum distance values for each item have been optimized to achieve better values. This research has the novelty of producing a mathematical model using variables, objective functions, capacity limits, flight structure limits and variable domains, which then produces an algorithm with data input processes, determining optimization models, determining variables, determining objective functions, determining problems. The results of this model can be recommended to airlines in scheduling flights during the pandemic

A Stochastic Integer Programming Approach to Air Traffic Scheduling and Operations

Air traffic management measures comprise tactical operating procedures to minimize delay costs and strategic scheduling interventions to control overcapacity scheduling. Although interdependent, these problems have been treated in isolation. This paper proposes an integrated model of scheduling and operations in airport networks that jointly optimizes scheduling interventions and ground-holding operations across airports networks under operating uncertainty. It is formulated as a two-stage stochastic program with integer recourse. To solve it, we develop an original decomposition algorithm with provable solution quality guarantees. The algorithm relies on new optimality cuts—dual integer cuts—that leverage the reduced costs of the dual linear programming relaxation of the second-stage problem. The algorithm also incorporates neighborhood constraints, which shift from exploration to exploitation at later stages. We also use a scenario generation approach to construct representative scenarios from historical records of operations—using integer programming. Computational experiments show that our algorithm yields near-optimal solutions for the entire U.S. National Airspace System network. Ultimately, the proposed approach enhances airport demand management models through scale integration (by capturing network-wide interdependencies) and scope integration (by capturing interdependencies between scheduling and operations).

A Hierarchical Heuristic Approach for Solving Air Traffic Scheduling and Routing Problem With a Novel Air Traffic Model

Efficient flight routing and scheduling play an important role in air traffic flow management, which aims to maximize the utilization of airport and enroute capacities to ensure safety and efficiency of air transportation. In this paper, we first propose a novel discrete-time flow dynamic model for an air traffic network, consisting of airports, waypoints, and air links, upon which we formulate an air flow routing and scheduling problem as an integer linear programming problem. Considering the NP-hard nature of the problem, we present a novel hierarchical flow routing and scheduling approach, where the hierarchical architecture is derived naturally from the network containment relationship, and computation is carried out in a bottom-up manner, which relies on an incremental strategy. On the resulting flow routes and schedules, a heuristic algorithm is carried out to determine flight plans for individual aircrafts. The effectiveness of the proposed hierarchical approach is illustrated by air traffic data in four flight information regions in the association of Southeast Asian nations.

Airport Taxi Situation Awareness with a Macroscopic Distribution Network Analysis

This paper proposes a framework for airport taxi situation awareness to enhance the assessment of aircraft ground movements in complex airport surfaces. Through a macroscopic distribution network (MDN) of arrival and departure taxi processes in a spatial-temporal domain, we establish two sets of taxi situation indices (TSIs) from the perspectives of single aircraft and the whole network. These TSIs are characterized into five categories: aircraft taxi time indices (ATTIs), surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs), and slot resource demand indices (SRDIs). The coverage of the TSIs system is discussed in detail based on the departure and arrival reference aircraft. A real-world case study of Shanghai Pudong airport demonstrates significant correlations among some of the proposed TSIs such as the ATTIs, SCFIs and AQLIs. We identify the most crucial influencing factors of the taxi process and propose two new metrics to assess the taxi situation at the aircraft and network levels, by establishing taxi situation assessment models instead of using two systems of multiple TSIs. The findings can provide significant references to decision makers regarding airport ground movements for the purposes of air traffic scheduling and congestion control in complex airports.

Efficient Computation of Separation-Compliant Speed Advisories for Air Traffic Arriving in Terminal Airspace

A class of problems in air traffic management (ATM) asks for a scheduling algorithm that supplies the air traffic services authority not only with a schedule of arrivals and departures but also with speed advisories. Since advisories must be finite, a scheduling algorithm must ultimately produce a finite data set, hence must either start with a purely discrete model or involve a discretization of a continuous one. The former choice, often preferred for intuitive clarity, naturally leads to mixed-integer programs (MIPs), hindering proofs of correctness and computational cost bounds (crucial for real-time operations). In this paper, a hybrid control system is used to model air traffic scheduling, capturing both the discrete and continuous aspects. This framework is applied to a class of problems, called the fully routed nominal problem. We prove a number of geometric results on feasible schedules and use these results to formulate an algorithm that attempts to compute a collective speed advisory, effectively piecewise linear with finitely many vertices, and has computational cost polynomial in the number of aircraft. This work is a first step toward optimization and models refined with more realistic detail.

Sensitivity of contrail cirrus radiative forcing to air traffic scheduling

Air traffic effects high cloudiness and therefore the Earth's radiation budget by producing contrail cirrus. Contrail cirrus comprise of line‐shaped contrails and irregularly shaped ice clouds that originate from them. The warming effect of contrail cirrus is disproportionally large at night, since at daytime the cooling due to the short wave cloud albedo effect acts toward compensating the long wave warming effect. Therefore it has been suggested to restrict air traffic to daytime in order to reduce its climate impact. The potential for reducing the contrail cirrus radiative forcing by shifting air traffic to daytime depends on the diurnal cycle of contrail cirrus coverage which is in turn determined by the diurnal cycle of air traffic and the contrail cirrus lifetimes. Simulations with a global atmospheric general circulation model indicate that the annual mean contrail cirrus coverage may be almost constant over the day even in areas where air traffic is close to zero at night. A conceptual model describing the temporal evolution of contrail cirrus coverage reveals that this is due to the large variability in contrail cirrus lifetimes in combination with the spreading of contrail cirrus. This large variability of lifetimes is consistent with observational evidence but more observations are needed to constrain the contrail lifetime distribution. An idealized mitigation experiment, shifting nighttime flights to daytime, indicates that contrail cirrus radiative forcing is not significantly changed.

Contributors

Contributors

A stochastic 0–1 program based approach for the air traffic flow management problem

We present a model and a robust algorithmic framework for the Air Traffic Flow Management Problem (TFMP) under uncertainty in airport arrival and departure and airspace capacity due to weather conditions. For this purpose we use the state-of-the-art 0–1 deterministic model due to Bertsimas and Stock. We present two 0–1 versions of the stochastic model, depending upon the type of recourse policy to use. A multistage scenario analysis approach based on a simple and full recourse scheme is used. The air traffic scheduling can be implemented for a given set of initial time periods in the full recourse environment and the solution for the other periods does not need to be anticipated and, then, it depends on the scenario to occur. We present a Fix-and-Relax approach to solve the very large-scale 0–1 deterministic equivalent model. Computational results are presented by comparing the deterministic approach (where the stochasticity of the uncertain parameters is reduced to their average) with the recourse based schemes; the optimality gap is within 0.25% of the LP optimal solution for most of the cases with dozens of thousands of constraints and variables.