Search Results (47)

The terminal maneuvering area (TMA) serves as a critical transition zone between upper enroute airways and airports, representing one of the most complex regions for managing high volumes of arrival and departure traffic. This paper presents the multi-route TMA scheduling problem as an optimization challenge aimed at optimizing TMA interventions, such as rerouting, speed control, time-based metering, dynamic minimum time separation, and holding procedures; the objective function minimizes schedule deviations and the accumulated holding time. Furthermore, the problem is formulated as a mixed-integer linear program (MILP) to facilitate finding solutions. A rolling horizon control (RHC) dynamic optimization framework is also introduced to decompose the large-scale problem into manageable subproblems for iterative resolution. To demonstrate the applicability and effectiveness of the proposed scheduling models, a hub airport—Chengdu Tianfu International Airport (ICAO code: ZUTF) in the Cheng-Yu Metroplex—is selected for validation. Numerical analyses confirm the superiority of the proposed models, which are expected to reduce aircraft delays, shorten airborne and holding times, and improve airspace resource utilization. This study provides intelligent decision support and engineering design ideas for the macroscopic-level collaborative optimization framework of the Integrated Arrival–Departure and Surface (IADS) system. Full article

By assuming that both travel and departure time are normally distributed variables, a multi-objective stochastic optimization model for the multi-airport flight cooperative scheduling problem (MAFCSP) with CvaR of travel and departure time is firstly proposed. Herein, conflicts of flights from different airports at the same waypoint can be avoided by simultaneously assigning an optimal route to each flight between the airport and waypoint and determining its practical departure time. Furthermore, several real-world constraints, including the safe interval between any two aircraft at the same waypoint and the maximum allowable delay for each flight, have been incorporated into the proposed model. The primary objective is minimization of both total carbon emissions and delay times for all flights across all airports. A feasible set of non-dominated solutions were obtained using a two-stage heuristic approach-based NSGA-II. Finally, we present a case study of four airports and three waypoints in the Beijing–Tianjin–Hebei region of China to test our study. Full article

Previous works by our group evidenced stability problems associated with flight control law design for flexible aircraft regarding gain scheduling. This paper proposes an output feedback fuzzy-based gain scheduling approach to adequate closed-loop response in a broader range of the flight envelope. This method applies a variation of the controller gains based on the membership function design for all the varying parameters, such as dynamic pressure. It aims for performance improvement while enforcing global stability gain scheduling. The technique was demonstrated for the flexible ITA X-HALE aircraft nonlinear model and compared to the classical interpolation-based gain scheduling technique. The results revealed that fuzzy-based gain scheduling can effectively handle high-order systems while ensuring global system stability, leading to an overall improvement in performance. Full article

Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction. Full article

Unmanned Aircraft Systems (UASs) have undergone rapid development over recent years, but have also became vulnerable to security attacks and the volatile external environment. Ensuring that the performance of UASs is safe and secure no matter how the environment changes is challenging. Runtime Verification (RV) is a lightweight formal verification technique that could be used to monitor UAS performance to guarantee safety and security, while reactive synthesis is a methodology for automatically synthesizing a correct-by-construction controller. This paper addresses the problem of the generation and design of a secure UAS controller by introducing a unified monitor and controller synthesis method based on RV and reactive synthesis. First, we introduce a novel methodological framework, in which RV monitors is applied to guarantee various UAS properties, with the reactive controller mainly focusing on the handling of tasks. Then, we propose a specification pattern to represent different UAS properties and generate RV monitors. In addition, a detailed priority-based scheduling method to schedule monitor and controller events is proposed. Furthermore, we design two methods based on specification generation and re-synthesis to solve the problem of task generation using metrics for reactive synthesis. Then, to facilitate users using our method to design secure UAS controllers more efficiently, we develop a domain-specific language (UAS-DL) for modeling UASs. Finally, we use F Prime to implement our method and conduct experiments on a joint simulation platform. The experimental results show that our method can generate secure UAS controllers, guarantee greater UAS safety and security, and require less synthesis time. Full article

The resource scheduling optimization problem in predictive maintenance is a complex operational research challenge involving reasoning about stochastic failure models and the dynamic allocation of repair resources. In recent years, resource scheduling methods based on deep learning have been increasingly applied in this field, demonstrating promising performances. Among these, resource scheduling algorithms based on heterogeneous graphs have shown exceptional results in multi-objective optimization tasks. However, conventional graph neural networks primarily operate on binary relational graphs, which struggle to effectively utilize data in multi-relational settings, thereby limiting the scheduler’s performance. To address this limitation, this paper proposes a heterogeneous hypergraph-based resource scheduling algorithm for aircraft maintenance tasks to tackle the challenges of higher-order and many-to-many relationship processing inherent in traditional graph neural networks. Specifically, the proposed algorithm defines aircraft nodes and maintenance personnel nodes while introducing decision nodes and state nodes to construct hyperedges. It employs hypergraph convolution with a multi-head attention mechanism to learn the long-term value of decisions, followed by policy selection based on a Markov decision process. This method offers a lightweight, non-parametric dynamic scheduling solution capable of robust learning in highly stochastic environments. Comparative experiments conducted on three datasets of varying scales demonstrate that the proposed method outperforms both heuristic algorithms and existing deep learning methods in terms of its optimization performance on M1 and M2 metrics. Furthermore, it surpasses resource scheduling algorithms based on heterogeneous graph neural networks across multiple metrics. Full article

A well designed transition control schedule can enable the engine to quickly and smoothly transition from one operating state to another, thereby enhancing the maneuverability of the aircraft. Although traditional pointwise optimization methods are fast in solving the transition control schedules, their optimized control schedules suffer from fluctuation problems. While global optimization methods can suppress fluctuation problems, their slow solving speed makes them unsuitable for engineering applications. In this paper, a combined optimization method for the transition control schedules of aero-engines is proposed. This method divided the optimization of the control schedules into two layers. In the outer-layer optimization, the global optimization technique was utilized to suppress the fluctuation of geometrically adjustable parameters. In the inner-layer optimization, the pointwise optimization technique was adopted to quickly obtain the control schedule of fuel flow rate. Moreover, a construction method of non-uniform control points in the global optimization layer was proposed, which significantly reduced the number of control points that needed to be optimized; thus, improving the efficiency of global optimization. The optimization problem of the acceleration and deceleration control schedules of a mixed-flow turbofan engine was used to verify the effectiveness of the combined optimization method. The results show that, compared with the pointwise optimization method, the transition time optimized by the combined optimization method shows no obvious difference. The control schedules optimized by the combined optimization method are not only smooth but can also prevent some components from approaching their working boundaries. Full article

Integrated arrival–departure and surface scheduling (IADS) is a critical research task in next-generation air traffic management that aims to harmonize the complex and interrelated processes of airspace and airport operations in the Metroplex. This paper investigates the microscopic-level collaborative optimization framework for IADS operations, i.e., the problem of coordinating aircraft scheduling on runways and taxiways. It also describes the mixed-integer linear programming (MILP) bi-layer decision support for solving this problem. In runway scheduling, a combined arrival–departure scheduling method is introduced based on our previous research, which can identify the optimal sequence of arrival and departure streams to minimize runway delays. For taxiway scheduling, the Multi-Route Airport Surface Scheduling Method (MASM) is proposed, aiming to determine the routes and taxi metering for each aircraft while minimizing the gap compared with the runway scheduling solution. Furthermore, this paper develops a feedback mechanism to further close the runway and taxiway schedule deviation. To demonstrate the universality and validity of the proposed bi-layer decision support method, two hub airports, Chengdu Shuangliu International Airport (ICAO code: ZUUU) and Chengdu Tianfu International Airport (ICAO code: ZUTF), within the Cheng-Yu Metroplex, were selected for validation. The obtained results show that the proposed method could achieve closed-loop decision making for runway scheduling and taxiway scheduling and reduce runway delay and taxi time. The key anticipated mechanisms of benefits from this research include improving the efficiency and predictability of operations on the airport surface and maintaining situational awareness and coordination between the stand and the tower. Full article

This paper presents an efficient schedule method for maintenance, repair, and overhaul (MRO) tasks for aircraft engines using a constraint programming algorithm. Using data obtained from Korean Air’s MRO maintenance logs, we analyze and predict the optimal scheduling of regular inspections and fault repairs for various engine types. By proposing a proper modeling of the problem and preparing data for the constraint programming algorithm, we demonstrate superior performance in scheduling efficiency and resource utilization. The experimental results show an average utilization of 99.35%, and the method can even achieve 100% utilization in some cases. Full article

Urban air mobility (UAM) is a revolutionary approach to transportation in densely populated cities. UAM involves using small, highly automated aircraft to transport passengers and goods at lower altitudes within urban and suburban areas, aiming to transform how people and parcels move within these environments. On average, UAM can reduce travel times by 30% to 40% for point-to-point journeys, with even greater reductions of 40% to 50% in major cities in the United States and China, compared to land transport. UAM includes advanced airborne transportation options like electric vertical takeoff and landing (eVTOL) aircraft and unmanned aerial vehicles (UAVs or drones). These technologies offer the potential to ease traffic congestion, decrease greenhouse gas emissions, and substantially cut travel times in urban areas. Studying the applications of eVTOLs and UAVs in parcel delivery and passenger transportation poses intricate challenges when examined through the lens of operations research (OR). By OR approaches, we mean mathematical programming, models, and solution methods addressing eVTOL- and UAV-aided parcel/people transportation problems. Despite the academic and practical importance, there is no review paper on eVTOL- and UAV-based optimization problems in the UAM sector. The present paper, applying a systematic literature review, develops a classification scheme for these problems, dividing them into routing and scheduling of eVTOLs and UAVs, infrastructure planning, safety and security, and the trade-off between efficiency and sustainability. The OR methodologies and the characteristics of the solution methods proposed for each problem are discussed. Finally, the study gaps and future research directions are presented alongside the concluding remarks. Full article

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account. Full article

In industries such as aircraft or train manufacturing, large-scale manufacturing companies often manage several complex projects. Each of these projects includes multiple tasks that share a set of limited resources. Typically, these tasks are also subject to time dependencies among them. One frequent goal in these scenarios is to minimize the makespan, or total time required to complete all the tasks within the entire project. Decisions revolve around scheduling these tasks, determining the sequence in which they are processed, and allocating shared resources to optimize efficiency while respecting the time dependencies among tasks. This problem is known in the scientific literature as the Resource-Constrained Project Scheduling Problem (RCPSP). Being an NP-hard problem with time dependencies and resource constraints, several optimization algorithms have already been proposed to tackle the RCPSP. In this paper, a novel discrete-event heuristic is introduced and later extended into an agile biased-randomized algorithm complemented with an adaptive capability to tune the parameters of the algorithm. The results underscore the effectiveness of the algorithm in finding competitive solutions for this problem within short computing times. Full article

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. Full article

This paper demonstrates the effectiveness of the Extended First-Come, First-Served (EFCFS) scheduler for integrated arrival and departure scheduling by comparing the scheduling results with the recorded operational data at Incheon International Airport (ICN), Republic of Korea. The EFCFS scheduler can handle multiple capacity- or flow-rate-related constraints along the path of each flight, which is represented by a node–link graph structure, and can solve large-scale problems with low computational cost. However, few studies have attempted a systematic verification of the EFCFS scheduler by comparing the scheduling results with historical operational data. In this paper, flights are scheduled between gates and runways on the airport surface with detailed constraints such as runway wake turbulence separation minima and conflict-free taxiing. The scheduler is tested using historical flight data from 15 August 2022 at ICN. The input schedule is generated based on the flight plan data extracted from the Flight Operation Information System (FOIS) and airport surface detection equipment data, and the results are compared with the times extracted from the FOIS data. The scheduling results for 500 aircraft show that the average takeoff delay is reduced by about 19 min, while the average landing delay is increased by less than one minute when the gate occupancy constraint is not considered. The results also confirm that the EFCFS effectively utilizes the available time slots to reduce delays by switching the original departure or arrival orders for a small number of flights. Full article

An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle manufacturing, and multiple aircraft maintenance. However, optimizing the scheduling puts forward higher requirements on accuracy, real time, and generalization, while subject to the curse of dimension and usually incomplete information. Various coupling relations among operations, stations, and resources aggravate the problem. To deal with the above challenges, we propose a multi-agent reinforcement learning algorithm where the scheduling environment is modeled as a decentralized partially observable Markov decision process. Each job is regarded as an agent that decides the next triplet, i.e., operation, station, and employed resource. This paper is novel in addressing the flexible job shop scheduling problem with dual flexibility and varied transportation time in consideration and proposing a double Q-value mixing (DQMIX) optimization algorithm under a multi-agent reinforcement learning framework. The experiments of our case study show that the DQMIX algorithm outperforms existing multi-agent reinforcement learning algorithms in terms of solution accuracy, stability, and generalization. In addition, it achieves better solution quality for larger-scale cases than traditional intelligent optimization algorithms. Full article