Aircraft Scheduling Research Articles

A metaheuristic algorithm for efficient aircraft sequencing and scheduling in terminal maneuvering areas

A metaheuristic algorithm for efficient aircraft sequencing and scheduling in terminal maneuvering areas

Aircraft type recognition based on YOLOv8

Abstract The field of aircraft type recognition has a broad range of applications. This technology serves as a valuable tool for airlines and airports in the scheduling and maintenance of aircraft. Furthermore, it provides crucial support to aviation regulatory authorities in their efforts to ensure safety in aviation. However, the task of recognizing aircraft types in real-world scenarios presents a significant challenge due to the subtle differences between various aircraft. Moreover, practical applications need real-time detection of aircraft type. To address these issues, this paper introduces the YOLOv8 object detector and investigates the performance of five different versions of YOLOv8 in the context of aircraft type recognition, to identify the most suitable model for this task. Experimental results on the Military Aircraft Detection Dataset demonstrate that the YOLOv8l model outperforms the others, achieving the highest performance with a COCO AP score of 84.2.

Subnetwork prediction approach for aircraft schedule recovery

The combinatorial optimization techniques for the aircraft schedule recovery problem are too expensive to compute. However, we require a reasonable solution within 1–2 min for real-time application. The computational time of traditional optimization algorithms will drastically reduce if we can accurately predict the subnetwork affected by the disruption. In this study, we advocate the use of machine learning (ML) as a promising tool to predict the subnetwork. The optimal recovery schedules for a large number of disruptions are used for offline training, and subsequently, the trained ML model is employed to reduce the search space for new disruption cases substantially. To achieve this, we employ a unique feature space specifically designed to capture the essential characteristics of the problem. A notable contribution is constructing a novel feature space based on revised aircraft schedules designed to capture the essential characteristics of the problem. The recursive feature elimination technique is employed for optimal feature selection. Five machine learning classifiers: Logistic Regression, Random Forest, Extreme gradient boosting, Light gradient boosting, and Categorical Boosting are compared. The performance is evaluated on real data obtained from an airline company. Our study demonstrates that, with the subnetwork of aircraft predicted by the classifier, the computational time of the column generation approach is remarkably reduced without deteriorating the solution quality in all the disruption instances tested, with more than 98% of the instances being solved within 60 s. The results highlight the remarkable advantage of integrating ML in the pre-optimization phase.

Static test of T800 carbon fiber reinforced polymer (CFRP) wing-box

This paper studies static test methods and test loads of Carbon Fiber Reinforced Polymer (CFRP) wing-box, analyzes the test results, and compares the test results of T800 CFRP wing-box from HENGSHEN Co., LTD and TORAY. It comes out that the stiffness of the T800 CFRP wing-box from HENGSHEN is equivalent with that of TORAY, while the strength meets the structural design requirements, which can be applied on the scheduled aircraft structure for further verification.

New Results on the Robust Coloring Problem

Many variations of the classical graph coloring model have been intensively studied due to their multiple applications; scheduling problems and aircraft assignments, for instance, motivate the robust coloring problem. This model gets to capture natural constraints of those optimization problems by combining the information provided by two colorings: a vertex coloring of a graph and the induced edge coloring on a subgraph of its complement; the goal is to minimize, among all proper colorings of the graph for a fixed number of colors, the number of edges in the subgraph with the endpoints of the same color. The study of the robust coloring model has been focused on the search for heuristics due to its NP-hard character when using at least three colors, but little progress has been made in other directions. We present a new approach on the problem obtaining the first collection of non-heuristic results for general graphs; among them, we prove that robust coloring is the model that better approaches the equitable partition of the vertex set, even when the graph does not admit a so-called equitable coloring. We also show the NP-completeness of its decision problem for the unsolved case of two colors, obtain bounds on the associated robust coloring parameter, and solve a conjecture on paths that illustrates the complexity of studying this coloring model.

Integrated commercial and operations planning model for schedule design, aircraft rotation and crew scheduling in airlines

AbstractThe commercial and operations planning in airlines has traditionally been a hierarchical process starting with flight schedule design, followed by fleet assignment, aircraft rotation planning and finally the crew scheduling. The hierarchical planning approach has a drawback that the optimal solution for a planning phase higher in hierarchy may either be infeasible for the subsequent phase or may lead to a sub‐optimal overall solution. In this paper, we solve a profit‐maximizing integrated planning model for clean‐sheet “rotated” schedule design with flight re‐time option and crew scheduling for a low‐cost carrier (LCC) in an emerging market. While the aircraft rotation problem has been traditionally modeled in the literature as a daily routing of individual aircraft for maintenance requirement, in this work we address the requirement of planned aircraft rotations as part of schedule design for LCCs. The planned aircraft routing is important in our case to create as many via‐flights as possible due to the underserved nature of the emerging market. We solve this large‐scale integer‐programming problem using two approaches – Benders Decomposition and Lagrangian Relaxation. For Lagrangian Relaxation, we exploit the special structure of our problem and intuitive understanding behind the Lagrangian duals to develop a multiplier adjustment approach to find an improved lower bound of integrated model solution. The crew‐pairing sub‐problem is solved using column generation through multi‐label shortest path algorithm followed by branch‐and‐price for integer solution. We test our solution methodology on a flight universe of 378 unique flights for different problem sizes by varying the number of aircraft available for operations. Our computational results show that within a reasonable run time of few hours both the approaches, Benders Decomposition and Lagrangian Relaxation, are successful in finding lower bounds of the integrated model solution, which are higher than the solution of traditional hierarchical approach by 0.5%–2.5%. We find Lagrangian Relaxation methodology to usually attain an improved solution faster than the Benders Decomposition approach, particularly for large‐scale problems.

Sensor Network Solutions for Aircraft Route Scheduling and Parking Allocation with Localization and Synchronization

The Wireless Sensor Network (WSN) is the modernized version of the sensor networking, earlier the concerned networking system used to be wired. The wireless and modified features have been able to increase the efficiencies of the networking in routing scheduling of aircraft and allocating the parking. This new approach to the WSN system is not much different from the typical sensor networks framework that contains sensors, a communication system, and a controller. Instead of a communication system, a wireless protocol is applied within the sensor network. The smart system of parking has effectively implied due to its hugely innovative as well as in-ground sensors. This can monitor individual spaces of parking and able to relay the status of occupancy to the smart-sport gateways. Then, this can send the live status data to the platform of the smart cloud. This entire process enables the real-time parking data to be accessed and observed on multiple devices.

Agent-Based Management and Coordination of Aircraft at Intersections

This work presents a game-theoretic approach to address the problem of coordination and scheduling of aircraft at intersections, with the goal of avoiding conflicts and potential collisions. The proposed algorithm enables simple agents to work together in a way that leads to cooperative behaviors, resulting in equilibria that improve the overall efficiency of the system. The researchers tested and compared the game-theoretic approach with a centralized approach, specifically FirstCome-First-Serve (FCFS), using data from Mohammed 5 Casablanca airport. The initial results suggest that the game-theoretic model is promising, despite its higher complexity. The approach has the potential to improve the overall coordination and scheduling of aircraft, leading to a more efficient and safe system. The proposed game-theoretic approach is designed to improve the coordination and scheduling of aircraft at intersections, ultimately leading to a safer and more efficient system. The approach is shown to be promising in initial testing, offering a potentially superior alternative to centralized approaches like FCFS. This research highlights the potential benefits of game-theoretic models in addressing complex coordination problems in multi-agent systems.

A machine learning based column-and-row generation approach for integrated air cargo recovery problem

Freighter airlines need to recover both aircraft and cargo schedules when disruptions happen. This process is usually divided into three sequential decisions to recover flights, aircraft, and cargoes. This study focuses on the integrated recovery problem that makes aircraft and cargo recovery decisions simultaneously. We formulate a string-based model to solve the integrated air cargo recovery problem. The main difficulty of the string-based model is that the number of constraints grows with the newly generated flight delay decisions in the variable generation subproblem. Therefore, the traditional column generation method can not be applied directly. To tackle this challenge, we propose a machine learning-based column-and-row generation approach. The machine learning method is used to uncover the critical delay decisions of short through connections in each column-and-row generation iteration by eliminating the poor flight delay decisions. We also propose a set of valid inequality constraints that can greatly improve the objective of LP relaxation solution and reduce the integral gap. The effectiveness and efficiency of our model are tested by simulated scenarios based on real operational data from the largest Chinese freighter airline. The computational results show that a significant cost reduction can be achieved with the proposed integrated model in a reasonable time.

Optimal Collaborative Scheduling of Multi-Aircraft Types for Forest Fires General Aviation Rescue

The scheduling of rescue aircraft needs to be studied in depth because of its criticality for the general aviation rescue of forest fires. This paper constructs a collaborative schedule optimization model for general aviation rescue under the condition of multiple aircraft, from multiple rally points to multiple fire points, targeting the shortest rescue time and the lowest rescue cost in the context of forest fires based on the simulation verification of a forest fire that broke out simultaneously in multiple locations in Liangshan Prefecture, Sichuan Province, China. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm was used to find the optimal set of solutions satisfying the objective function: four feasible solutions. Then, the optimal solution was solved based on the weighted TOPSIS method, which was the optimal solution for this rescue task. The simulation results show that unnecessary flight times can be reduced by optimizing the schedule plan. Under the premise of ensuring rescue timeliness, the utilization rate of rescue aircraft was improved, and rescue costs were further reduced. The presented work provides a theoretical reference for the efficient development of general aviation rescue.

Maximizing robustness of aircraft routing with heterogeneous maintenance tasks

In aircraft maintenance routing, the disruption caused by the stochasticity of the maintenance duration is a critical challenge. Previous studies considered the sources of disruptions in an aggregated manner, which resulted in aircraft schedules with insufficient robustness. In this study, we incorporated the uncertainties of heterogeneous maintenance tasks into a robust aircraft routing decision framework and considered other sources of disruptions. Our aim is to study the impact of maintenance task uncertainties on aircraft schedules. Accordingly, we propose a robust aircraft maintenance routing model, which is solved by an accelerated column generation algorithm. Computational experiments are conducted based on actual flight data to examine the efficiency of the proposed algorithm. The results show that the proposed acceleration strategies significantly speeded up the computational process without affecting the solution quality. Moreover, robust aircraft routes were obtained that were less vulnerable to maintenance disruptions.

A heuristic approach for scheduling advanced air mobility aircraft at vertiports

Recent progress in electric vertical take-off and landing (eVTOL) vehicles suggests that soon these vehicles could safely and efficiently transport people and cargo in urban areas. Therefore, advanced air mobility vehicles could become an alternative means of transport to overcome traffic congestion in cities in the upcoming years. There has been enormous interest from companies and governments in recent years in developing such technologies and enabling markets for new air transportation services. Despite the interest in the topic, little research has been done to address the aircraft scheduling problem in advanced air mobility take-off and landing areas (vertiports). The vertiports serve as the airports of eVTOL vehicles and could experience congestion problems similar to those of airports. This work proposes two optimization models for scheduling departing and landing aircraft at the vertiports’ common ground taxi routes (taxiways), gates, and touchdown and lift-off (TLOF) pads. The mathematical models include advanced air mobility features such as separation rules and blocking constraints. As scheduling objectives, the first model maximizes the vertiport throughput, and the second model minimizes the deviation from the expected take-off/landing time. In addition, as a solution methodology, we developed two heuristic algorithms that use scheduling rules to assign and sequence the aircraft to the vertiport components. Computational results show that the optimization models find optimal schedules for small-sized instances of up to 10 aircraft, while the heuristic algorithms provide good results in terms of solution quality and computational time for large instances.

Optimising Airport Ground Resource Allocation for Multiple Aircraft Using Machine Learning-Based Arrival Time Prediction

Managing aircraft turnaround is a complex process due to various factors, including passenger handling. Airport ground handling, resource planning, optimal manpower, and equipment utilisation are some cost-cutting strategies, particularly for airlines and ground handling service teams. Scheduled aircraft arrival and departure times are critical aspects of the entire ground management and passenger handling process. This research aimed to optimise airport ground resource allocation for multiple aircraft using machine learning-based prediction methodologies to enhance the prediction of aircraft arrival time, an uncontrollable variable. Our proposed models include a multiple linear regression (MLR) model and a multilayer perceptron (MLP)-based model, both of which are used for predicting round-trip arrival times. Additionally, we developed a MLP-based model for multiclass classification of arrival delays based on departure time and delay from the same airport. Under normal weather conditions and operational scenarios, the models were able to predict round-trip arrival times with a root mean squared error of 8 min for each origin–destination pair and classify arrival delays with an average accuracy of 93.5%. Our findings suggest that machine learning-based approaches can be used to predict round-trip arrival times based on the departure time from the same airport, and thereby accurately estimate the number of actual flight movements per hour well in advance. This predictability enables optimised ground resource planning for multiple aircraft based on constrained airport resource deployment and utilisation.

Analyzing factors affecting charter flight service demand: A case of Taiwan

Global charter flight demand remined relatively stable over the last decades, and international charter flight is an integrated product of the aviation and tourism industries. To better understand charter flight tourism in Asia, this study analyses key reasons affected Taiwan’s charter flight demand from Japan, South Korea, Malaysia, and Singapore during the period 2009‒2018, with a consideration of aviation- and tourism-related variables. Empirical results show that the reduction in scheduled flight aircraft size stimulated new charter flight demand to Taiwan, offsetting the negative effects caused by scheduled seat increment to a certain degree. This study contributes to literature by exploring the impact of scheduled flight service availability on charter flight demand, and further enriches our understanding of the impact of scheduled flight services on international tourist arrivals in terms of aircraft size. Importantly, it has implications for policymakers and stakeholders involved in charter business, assisting them in facilitating aviation and tourism recovery during the post-COVID-19 era.

Air-traffic controller and autopilot joint control of aircraft as mixed integer quadratic problem

One of the traditional problems in air-traffic management is optimal safe scheduling aircraft at a point of air-routes join. However, this problem implies another one, namely, the problem of guiding the aircraft to the prescribed point of its route at the necessary instant. The latter problem is studied much less attentively. Change of the arrival instant can be provided by alternating the aircraft motion trajectory on the basis of near-airport air-routes scheme or by alternating its velocity. Such a control can be worked out as automatically by the aircraft autopilot as manually by an air-traffic controller. But it can be difficult to an air-traffic controller to construct such a control especially during rush hours in loaded airports. In this paper, the authors suggest an optimizational formalization of the problem of automatic directions generation for such a guiding. The criterion to be minimized in this formalization is the number of controller-pilot interactions, because each such an interaction is a long, difficult process liable to linguistic, technical, psychological, and other errors. Since the criterion and other processes are of discrete kind, the formalization is done in the framework of mixed integer quadratic programming. The obtained problems are solved numerically by means of MIQP-solver provided by the Gurobi library. Each subproblem is solved quite fast within 0.1-0.5 seconds. So, solution of the entire problem (which includes enumeration of trajectories and choice of the optimal velocity regime for each of them) takes one-two seconds. Thus, the problem for the entire group of aircraft can be solved during a time acceptable for a real time application.

Identification of Airport Similar Days Based on K-prototype Cluster and Grey Correlation Analysis

Considering similar air traffic control techniques for the present based on close historical dates is a good approach due to the unpredictability of weather and air traffic, as well as to increase controller efficiency. A K-prototype clustering technique and grey correlation analysis are proposed to discover similar days to address the problem of similar identification. Firstly, the weather and air traffic datasets are used to create a set of features broken down into numerical and categorical attributes. Secondly, the historical data are clustered using the K-prototype clustering, which is then paired with grey correlation analysis to identify days similar to the reference day and examine the traffic management initiatives employed on that day. Finally, the research uses actual weather information and aircraft schedules from Nanjing Lukou International Airport as examples. The outcomes demonstrate that the similar days picked by the model are representative and can serve as a foundation for airport controllers' decision-making.

A New Technique to Uniquely Identify the Edges of a Graph

Graphs are useful for analysing the structure models in computer science, operations research, and sociology. The word metric dimension is the basis of the distance function, which has a symmetric property. Moreover, finding the resolving set of a graph is NP-complete, and the possibilities of finding the resolving set are reduced due to the symmetric behaviour of the graph. In this paper, we introduce the idea of the edge-multiset dimension of graphs. A representation of an edge is defined as the multiset of distances between it and the vertices of a set, B⊆V(Γ). If the representation of two different edges is unequal, then B is an edge-multiset resolving a set of Γ. The least possible cardinality of the edge-multiset resolving a set is referred to as the edge-multiset dimension of Γ. This article presents preliminary results, special conditions, and bounds on the edge-multiset dimension of certain graphs. This research provides new insights into structure models in computer science, operations research, and sociology. They could have implications for developing computer algorithms, aircraft scheduling, and species movement between regions.

Effective upper and lower bounds for a two-stage reentrant flexible flow shop scheduling problem

Flow shop scheduling is important in modern industrial manufacturing to improve production efficiency. This paper studies a realistic two-stage reentrant flexible flow shop scheduling problem (TSRFFS) with broad applications in aircraft scheduling, manufacturing, and the medical industry, etc. Given a flow shop with a single machine in Stage 1, a set of parallel machines in Stage 2, and a set of jobs to be processed, the TSRFFS aims to determine the completion time of jobs in Stage 1 and then that in Stage 2, and finally returns to Stage 1, as well as determine the job-to-machine assignment in Stage 2 such that all jobs are served and the total processing time of jobs (makespan) is minimized. The optimal solution properties are investigated, based on which a mixed integer programming mathematical model and a greedy random constructive heuristic for near optimal solutions are proposed. By solving series of a revised parallel machine scheduling problem (Pm||Cmax), a lower bound method is developed. Extensive numerical experiments on 1560 random instances with up to 1000 jobs and 50 realistic airport simulation instances were conducted to demonstrate the effectiveness of the proposed algorithms. The average gap between the proposed upper bound and the best lower bounds is approximately 1.78%, and the average gap between the proposed lower bound and the best upper bounds is 0.91%, which far outperforms state-of-the-art approaches in terms of solution quality and computational time.

Aircraft scheduling optimization model for on-ramp of corridors-in-the-sky

<abstract> <p>To efficiently handle the projected growth in air traffic, operational concepts such as "corridors-in-the-sky" have been developed in future Air Traffic Management (ATM) plans. Corridors-in-the-sky are defined as reserved airspace where aircraft move in a common direction along parallel lanes on high flight levels. The on and off ramps play an important role in connecting this airspace structure to the jet route. This paper addresses the scheduling problem for flight sequences entering the corridors-in-the-sky via the on-ramp. To satisfy the requirements of various stakeholders, this paper proposes time-based and cost-based aircraft scheduling optimization models for the on-ramp of the corridors-in-the-sky. An improved Particle Swarm Optimization (PSO) algorithm is also developed to solve a case study of the air route B215 in the Beijing control area. Computational results indicate that the optimized models have advantages in reducing the delay time and cost for both single aircraft and flight sequences, compared with the First-come, First-served (FCFS) approach.</p> </abstract>

Optimal aircraft arrival scheduling with continuous descent operations in busy terminal maneuvering areas

Continuous Descent Operation (CDO), an aircraft arrival procedure in which an aircraft descends from an optimal position with minimum engine thrust, can significantly reduce aircraft noise, fuel burn and emissions. The implementation of CDOs, however, requires more separation buffers among flights than traditional stepwise descents, resulting in loss of airspace and runway capacity. It is therefore challenging to maximize the number of aircraft performing CDOs for an airport, especially in busy terminal maneuvering areas. In this paper, we tackle the difficulty by developing an aircraft arrival scheduling model to sequence arrival aircraft before their merge point and generate conflict-free trajectories by means of a lateral path stretching method. The contributions of our study to the research community are twofold: firstly, we investigate a new aircraft arrival scheduling problem that incorporates both the CDO trajectory optimization and aircraft scheduling, and develop the first model simultaneously considering these two procedures; secondly, we conduct extensive computational experiments based on historical data in Guangzhou Baiyun International Airport, which would provide an important reference for future research in the area. Our numerical results demonstrate the benefit of the proposed CDO-based aircraft arrival scheduling approach in terms of reducing total flight time and saving fuel consumption when applied in this congested airport.