Airport Ground Handling Research Articles

An agent-based simulator for airport ground handling services

Airport ground handling plays an essential role in shortening the turnaround time for better utilization of aircraft that can reduce the cost per flight. One of the key challenges for improving the efficiency of airport ground operations is to comprehensively simulate the ground handling processes to optimize and evaluate various operation strategies. In this work, we develop an agent-based simulator that specifically focuses on the whole process of airport ground handling from landing to takeoff. It allows different types of agents, i.e. aircraft, ground vehicles, operators, and dispatchers, to execute their own behaviors according to the ground operation characteristics. The interactions among different agents are also modeled according to the specific service agreements. We use the Shanghai Pudong International Airport (PVG) for the case study to demonstrate the interfaces and outputs of the simulator including the simulated flight information, the progress of ground handling services, airfield status, etc. With the developed simulator, we could not only reproduce the real-world operations of different types of services but also have enough potential to optimize and evaluate various airport ground operation strategies under a series of complex scenarios.

Neural Airport Ground Handling

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at: https://github.com/RoyalSkye/AGH.

Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated based on real scenarios, where we integrate imitation learning and graph convolutional network (GCN) to learn a destroy operator to automatically select variables, and employ an off-the-shelf solver as the repair operator to reoptimize the selected variables. Experimental results based on a real airport show that the proposed method allows for handling up to 200 flights with 10 types of operations simultaneously, and outperforms state-of-the-art methods. Moreover, the learned method performs consistently accompanying different solvers, and generalizes well on larger instances, verifying the versatility and scalability of our method.

Formation and Routing of Worker Teams for Airport Ground Handling Operations: A Branch-and-Price-and-Check Approach

We address workforce optimization for ground handling operations at the airport, focusing on baggage loading and unloading. Teams of skilled workers have to be formed and routed across the apron to unload the baggage from the aircraft after a landing and to load it before takeoff. Such tasks must be performed within time windows and require a team of workers with different skill levels. The goal is to find a feasible plan that minimizes the sum of the tasks completion times. We formalize a variation of the workforce scheduling and routing problem, integrating team formation, hierarchical skills with downgrading, multiple trips, and different execution modes. We propose a solution approach based on branch-and-price-and-check and test it on real-world instances from a major European hub airport. We propose a model based on the Dantzig–Wolfe decomposition. In the pricing problem, we generate tours of teams as shortest paths with constrained resources in a network. In the master problem, we select an optimal set of tours that do not exceed the workforce availability. Our experiments show that the proposed algorithm can produce optimal solutions for small- and medium-sized instances and good or optimal solutions for large instances. The results also show that our approach outperforms the current airport dispatching policy. Funding: G. Dall’Olio was funded by the Deutsche Forschungsgemeinschaft [Grant Advanced Optimization in a Networked Economy Graduiertenkolleg 2201, Project 277991500]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0110 .

Hazard identification at airport ground handling locations

Elements that participate in operational risk analysis procedures are hazard sources, hazards, and undesirable events. Following the International Civil Aviation Organization, the continuous hazard identification process allows the safety level in civil aviation to be reduced and maintained at an acceptable level or below that level. For hazard detection to be a systemic process, tools are needed to achieve this goal. The main stages of formulating hazards are task assessment, description of hazards, and description of causes. The description of hazards should be prepared based on information about the hazard sources. Brainstorming, engineering knowledge, or checklists can be used to obtain information about the hazard sources in a selected area. Checklists for the areas of analysis are a frequently used tool to identify hazard sources. The safety of air operations begins at the airport and apron. That is why assessing the risk at ground handling workstations is so essential. After analyzing the regulations on the safety of ground handling workers in terms of safety and occupational health and safety, many issues remain unregulated. Ground handling workers are an essential link affecting the safety of air operations. It is important that when starting the performance of their duties, employees are adequately trained, equipped with the necessary tools, and have a good level of well-being so that an accident does not occur. A checklist was developed to determine whether the employee has the appropriate equipment, is adequately trained, and whether his mental and physical health condition allows him to perform his duties. Forms were proposed for the airport equipment operator, airport apron worker, and operations coordination specialist. Declaring a response to the hazard allows the decision on whether or not to allow an employee to work.

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.

Practices of Airport Operators and Groundhandling:

Practices of Airport Operators and Groundhandling:

Hubungan tingkat intensitas bunyi dengan penurunan daya dengar pekerja

Abstract
 Noise is unwanted sound that can cause health problems and environmental comfort at a certain level and time. Noise Induced Hearing Loss (NHL) is a type of sensorineural hearing loss caused by exposure to sufficient/loud noise over a long period of time. This study was conducted to determine the effect of noise on the hearing of workers working in the Ground Handling area of ​​Samarinda airport, to determine the noise level received by workers in the Airport Ground Handling Area, and to know the importance of using PPE on the hearing of workers while doing work. The noise measurement method is measuring at predetermined points using a Sound Level Meter. Then, it was continued by measuring the hearing level of workers in a soundproof room alternately using audiometry, then a correlation test was carried out to determine the relationship between the two variables, namely noise and the hearing level of workers. Based on the results of the study, it is known from the results of the average hearing that has been correlated with total noise exposure in the work area, it can be seen that the hearing level of workers is decreasing over time, this is due to the amount of noise exposure received by workers during work activities.

Airport resource allocation using machine learning techniques

The airport ground handling has a global trend to meet the Service Level Agreement (SLA) requirementsthat represents resource allocation with more restrictions according to flights. That can be achieved by predictingfuture resources demands. this research presents a comparison between the most used machine learning techniquesimplemented in many different fields for demand prediction and resource allocation. The prediction model nomi-nated and used in this research is the Support Vector Machine (SVM) to predict the required resources for eachflight, despite the restrictions imposed by airlines when contracting their services in the Service Level Agreement.The approach has been trained and tested using real data from Cairo International Airport. the proposed (SVM)technique implemented and explained with a varying accuracy of resource allocation prediction, showing thateven for variations accuracy in resource prediction in different scenarios; the Support Vector Machine techniquecan produce a good performance as resource allocation in the airport.

Optimization of Airport Capacity Efficiency by Selecting Optimal Aircraft and Airline Business Model

This paper analyses the impact of airline business models on airport infrastructure and operational capacity and answers the question how to optimize capacity in order to achieve maximum efficiency and profitability as well as how to maintain an adequate level of service quality. As part of the research, a new model was created as an integral part of the Airport Management Strategy Software (AMSS) application. The purpose of the application is to enable the airport management to review and optimize operations in terms of maximum technical and technological capacity utilization. In addition, the application can be used to fill the available slots according to the specifics of the airline’s business model without compromising the security, flexibility, and profitability of airport operations. The validation of the application was conducted at Zagreb Airport, which generated traffic of 3.4 million passengers in 2019. The result of the research is a model which, based on the calculation of the existing capacity of the airport infrastructure and ground handling equipment, enables the simulation of new airline business models and aircraft type implementation. Furthermore, the model also analyses their impact on the utilization of the airport infrastructure and equipment. The research demonstrated the interdependence between airport capacity optimization and optimal slot allocation, and the specifics of airline business models and aircraft types in their fleets. By adopting this model, airport managers can prevent mistakes that arise during negotiations with airlines, which can result in the under capacity of the infrastructure, equipment, and human resources as well as cause lower levels of security, numerous delays, reduced quality of service and, ultimately, negative financial effects.

Multi-Criteria Decision-Making Techniques for Solving the Airport Ground Handling Service Equipment Vendor Selection Problem

Appropriate airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. Previous research has seldom integrated multi-criteria decision-making techniques with goal programming to solve the AGHSEVS problem. This paper describes a new system evaluation model for AGHSEVS by considering both qualitative and quantitative methods. We compare the fuzzy TOPSIS method based on fuzzy weighted average left and right score methods with multi-choice and multi-aspiration goal programming approach of an AGHS company in Taiwan. These study results can help airport ground handling service company managers make optimal decisions for AGHSEVS problems. We hope the practicability of the comparable model with slight modifications of real situation data can be used in other AGHS companies.

Impact of multi-criteria optimized trajectories on European airline efficiency, safety and airspace demand

Today the European airspace is facing multiple capacity constraints which are regulating demand during busy traffic periods of the day. These capacity limits cause inefficiencies in flight and airport ground handling. Current market forecasts predict an annual growth in passenger air traffic demand between 4.5 percent and 4.8 percent. This growth will be realized by an increasing number of aircraft movements reflected in an expected annual growth of jet airplanes by 3.3 percent with a negative impact on airspace capacity. To better manage the rare airspace capacity, the Single European Sky ATM Research (SESAR) and the Next Generation Air Transportation System (NextGen) program suggest free route airspaces, a Performance Based Navigation (PBN) and harmonized airspace structures as efficient concepts of improvement in air traffic efficiency. Beside today's minimum fuel and time objectives, a growing public awareness of the anthropogenic environmental impact necessitates further functions for flight planning and execution. Additionally, today's high safety standards must not be negatively influenced by the introduction of free route airspaces. In this paper, we present a trajectory calculation model capable of exploiting a 4D free route optimization potential while considering the divergent targets of safety, efficiency and environmental compatibility. In particular, the environmental effects of condensation trails depending on the time of the day are carefully considered. To further estimate the impact of free routes on airspace demand and on safety issues, the model is implemented in the simulation environment TOMATO, and the European flight intentions have been optimized for an entire day based on departure airports, arrival airports and original departure times July 2016. The resulting trajectories are evaluated against the number, location and duration of separation infringements. Despite constantly changing air speeds and cruising altitudes induced by the optimization target functions, the number and duration of separation infringements could be reduced by 30% due to optimized lateral and vertical trajectories. The results of this case study show a high potential for an increased airspace capacity under free routing conditions. Furthermore, fuel burn (20%) and airline direct operational costs (40%) could be significantly reduced.

Multicriteria-Optimized Trajectories Impacting Today’s Air Traffic Density, Efficiency, and Environmental Compatibility

Today’s air traffic system is faced with airspace capacity constraints that can cause inefficiencies in flight and in airport ground handling. Free routing performance-based navigation and harmonized airspace structures are seen as efficient mitigation measures to save distance and fuel, as far as free routes are implemented as optimized and predictable trajectories. In this case, a monitoring of the air traffic system is expected due to an improved predictability of those trajectories. In this paper, a trajectory optimization model for three-dimensional free routes considering multiple targets is presented, including operational costs, time costs, environmental costs, and expected external costs of condensation trails. The model is applied for a case study that looks at optimization of trajectories for an entire day based on the departure airport, arrival airport, and departure time in July of 2016. The resulting trajectories are evaluated against the number of conflicts. The case study shows that today’s air traffic demand already stresses the free route capacity when considering efficiency, ecological compatibility, and safety standards.

Column generation for vehicle routing problems with multiple synchronization constraints

Synchronization of workers and vehicles plays a major role in many industries such as logistics, healthcare or airport ground handling. In this paper, we focus on operational ground handling planning and model it as an archetype of vehicle routing problems with multiple synchronization constraints, coined as “abstract vehicle routing problem with worker and vehicle synchronization” (AVRPWVS). The AVRPWVS deals with routing workers to ground handling jobs such as unloading baggage or refuelling an aircraft, while meeting each job’s time window. Moreover, each job can be performed by a variable number of workers. As airports span vast distances and due to security regulations, workers use vehicles to travel between locations. Furthermore, each vehicle, moved by a driver, can carry several workers. We propose two mathematical multi-commodity flow formulations based on time-space networks to efficiently model five synchronization types including movement and load synchronization. Moreover, we develop a branch-and-price heuristic that employs both conventional variable branching and a novel variable fixing strategy. We demonstrate that the procedure achieves results close to the optimal solution in short time when compared to the two integer models.

Minimizing contrail formation by rerouting around dynamic ice-supersaturated regions

Today, the European airspace is already faced with airspace capacity constraints, especially during business driven peak periods of the day in central European air traffic control sectors. This capacity bottleneck will cause a challenging number of severe difficulties in future flight planning and airport ground handling. Additionally, the growing public awareness and the increasing scientific knowledge of the aviation environmental impact urges air traffic stakeholders to reduce the aviation induced global warming to an acceptable level. These efforts should include both the prevention of unnecessary fuel burn due to detours and the avoidance of passing ice-supersaturated regions during cruise to prevent contrail formation. Therewith, conflicting goals have to be considered in trajectory optimization. The induced contrails influence the radiation budget of the Earth atmosphere, which depend on the dynamic size and location of the ice-supersaturated regions. However, contrail avoidance can lead to unsolvable high requirements on airspace capacity in dry and warm air spaces, where contrails are not induced. Furthermore, contrail avoidance procedures can lead to large detours, which in turn cause more fuel burn and an increased impact on the environment due to the emission of additional radiative active substances. In this paper, the Air Traffic Control Fast Time Simulator and Air Traffic Optimizer AirTOp is used to simulate one day of Europeans air traffic and to reduce the radiative forcing of contrails by minimizing the number of flight hours through dynamic ice-supersaturated regions. Rerouting the affected flights does this, so that separation requirements are still fulfilled and each aircraft still reaches its destination. In this paper the following measures are assessed without and with contrail involved rerouting: the decrease of airspace capacity, the additional distance flown, the additional fuel burn and the contrail induced environmental impact. We found, that rerouting on this special day would have caused higher additional fuel and time costs, than saved reduced contrail costs.

휴먼터치가 지상조업 종사자의 이직의도에 미치는 영향

휴먼터치가 지상조업 종사자의 이직의도에 미치는 영향

Agent-Based Distributed Control for Improving Complex Resource Scheduling: Application to Airport Ground Handling Operations

Recent research on highly distributed control methods has produced a series of new philosophies based on negotiation, which bring together process engineering with computer science. Among these control philosophies, the ones based on multiagent systems (MASs) have proved to be especially suitable to address distributed decision making in highly volatile environments. Furthermore, this agent-based approach allows using new manufacturing concepts in scheduling problems because of its scalability, ability to negotiate, and distributed capabilities. However, MAS models have the drawback of excessive dependence on up-to-date field information. In this paper, a theoretical and experimental MAS is presented to test new strategies for managing handling operations supported by radio-frequency identification information feedback. Based on this new visibility framework, these strategies applied to scheduling control have been structured on a new distributed organization model to enforce the idea of the division between physical elements and information and communication technologies. This division into two platforms simplifies the design and development of the MAS, providing a higher level of abstraction and preserving the independence between platforms. The proposed MAS, called MAS-DUO, has been validated using a test bench simulator and at the Ciudad Real Central Airport in Spain.

Efficiency and Robustness in a Support Platform for Intelligent Airport Ground Handling

One of the effects of the development of air traffic during recent years has been the increase of congestion on major airports and their aprons, where ground handling operations take place. Efficiency and safety issues become important on the aprons. The “Integrated Airport Apron Safety Fleet Management—AAS” project aims at setting a decision support system for efficient and safe management of apron traffic, taking advantage of information gathered by “intelligent” vehicles equipped with on-board positioning and monitoring systems. In this project, we optimize the assignment of vehicles to apron operations. We present a mathematical formulation of the problem and sketch a fast sequential heuristic, which provides efficient assignments based on real-time data on position and status of vehicles and operations. We then discuss how the procedure can reach good trade-offs between efficiency and robustness against unpredicted, but frequent, delays occurring in real time at airports. The proposed approach has been applied to a major European airport (Berlin's Tegel), leading to efficiency improvements and reduction of expected delays due to handling operations.

Wireless Sensor Networks and RFID for Improving Information Visibility in Airport Ground Handling Management

Abstract The new economic situation in all industries, and especially in the air transportation industry, claims for new business models supported by accurate management processes, which need constant feedback of the real status of the environment. A combined use of identification technologies and wireless sensor networks (WSN) can provide the required visibility to that information on status. This work proposes a WSN based on a Zigbee network of RFID readers with active tags as end nodes. The WSN can get the identification (ID) information stored in the end nodes, which are carried by the physical resources. Besides the ID, the tag can include other useful data coming from sensors such as acceleration, temperature, humidity, fuel level, etc. The final objective is to achieve an updated/real picture of the resources in the airport environment that will help in the decision-making.

Safety culture in Swedish air traffic control

European air traffic control is undergoing changes in organization and technology in order to increase efficiency in air traffic. Change processes can have a negative impact on existing safety cultures, the foundations of safety work, and on safety. This paper reports on an assessment of safety culture at one administrative and two operative units in a Swedish air traffic control setting, using questionnaire packages and interviews, and focuses on 391 questionnaire respondents. The objectives of the study were to obtain baseline data of safety culture aspects and relationships between safety culture and organizational climate before major organizational and technical changes were implemented, and to yield reference data on safety culture aspects in this transport branch. The safety culture aspects were generally perceived and judged to be good by the respondents. Relationships were identified between the organizational climate and the safety culture at the two operative units, where a higher level of Support for ideas and a lower level of Conflicts were positively related to many of the investigated safety culture aspects. The general safety culture in the air traffic control setting was found to be on a somewhat higher average score level than that found in the authors’ previous studies of passenger shipping and airport ground handling.