Airport Operations Management Research Articles

Airport take‐off and landing optimization through genetic algorithms

AbstractThis research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm‐based method for minimizing pollution from fuel combustion during aircraft take‐off and landing at airports. This algorithm uniquely integrates the optimization of both landing gates and take‐off/landing runways, considering the correlation between engine operation time and pollutant levels. The approach employs advanced constraint handling techniques to manage the intricate time and resource limitations inherent in airport operations. Additionally, the study conducts a thorough sensitivity analysis of the model, with a particular emphasis on the mutation factor and the type of penalty function, to fine‐tune the optimization process. This dual‐focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, establishing a new standard for comprehensive and efficient airport operation management.

Airport gate assignment problem with harbor constraints based on Branch-and-Price algorithm

The airport gate assignment problem is the main problem in airport operation management. Most existing studies on the airport gate assignment problem focus more on the improvement of gate utilization efficiency and ignore apron operation safety. Aiming at addressing this problem, an airport gate assignment problem with harbor safety constraints is proposed in this paper. A two-phase mathematical optimization model is constructed to optimize the efficiency of gate utilization considering the safety constraints in harbors. To the best of our knowledge, this is the first study to explicitly focus on harbor apron security in the gate assignment problem. Then, the exact branch-and-price method is improved by incorporating a label-based pricing algorithm and two acceleration strategies. These strategies include an upper bound prediction strategy for handling the large number of subproblems and a lower parameter symmetry elimination strategy to overcome column generation degradation. The results show that the proposed gate assignment model and improved branch-and-price method optimize the efficiency of gate utilization under the condition of avoiding security conflicts in harbor aprons. The improved branch-and-price method has advantages in terms of both accuracy and efficiency compared with those of other solvers.

How to Improve the Efficiency of Check-In Counter: A Counter-Sharing Method

Improving the efficiency of check-in counters is a significant concern for airport operation management departments and airline companies. However, the existing studies mainly optimize the counter allocation based on the number of counters and airport demands, which needs more exploration on improving the utilization rate of check-in counters under the condition of unchanged resource allocation. In this paper, we propose a counter-sharing method to improve check-in efficiency by sharing the idle check-in counters in the adjacent check-in area. In the proposed counter-sharing method, we first define the passenger’s total walking distance and waiting time for queues as the metrics, then reassign the check-in areas and adjust the internal departure sequence of flights to maximize the counter-sharing rate. The numerical results indicate that the proposed counter-sharing method can improve the efficiency of check-in counters and reduce the passenger’s total walking distance and waiting time in the queue during the check-in process, which can enhance the airport’s operation efficiency and competitiveness.

High-fidelity CFD modeling of pollutant dispersion from aircraft auxiliary power units (APUs) at a realistic airport and the effects on airport air quality

Many studies have investigated air-traffic impact on airport air quality through main-engine emissions, however very few have characterized the APU-related impact. As field-monitoring remains limited, our study used CFD simulations to provide a high-fidelity representation of 19 APU dispersion plumes in a realistic airport configuration. Results of three dominant wind configurations show that the global dispersion plume (from all APUs) was transported by incident winds. Depending on wind direction, pollutant trapping within a large downwash region was observed downstream of the terminal-building. Local differences were observed depending on source locations; emissions within the downwash region were pushed against the terminal-facade which promoted emission spillover on terminal-roof, while plumes within the unperturbed boundary layer were transported by incident winds. Quantitatively, APU pollutant concentrations dropped overall by 3–4 orders of magnitude around the terminal-building, while diluted concentrations by 5–6 orders of magnitude were monitored at the airport exit. Given source concentrations with an appropriate experimental apparatus, this model can serve as an airport air quality decision-making tool to finely characterize individual source impacts. Such information can help assist airport operation management (taxi to gate/runway, gate assignment, safe corridors for ground-handling personnel) so as to reduce/prevent the impact of pollutant hot spots depending on wind configurations.

An Improved Tunicate Swarm Algorithm for Solving the MultiObjective Optimisation Problem of Airport Gate Assignments

Airport gate assignment is a critical issue in airport operations management. However, limited airport parking spaces and rising fuel costs have caused serious issues with gate assignment. In this paper, an effective multiobjective optimisation model for gate assignment is proposed, with the optimisation objectives of minimising real-time flight conflicts, maximising the boarding bridge rate, and minimising aircraft taxiing fuel consumption. An improved tunicate swarm algorithm based on cosine mutation and adaptive grouping (CG-TSA) is proposed to solve the airport gate assignment problem. First, the Halton sequence is used to initialise the agent positions to improve the initial traversal and allocation efficiency of the algorithm. Second, the population as a whole is adaptively divided into dominant and inferior groups based on fitness values. To improve the searchability of the TSA for the dominant group, an arithmetic optimisation strategy based on ideas related to the arithmetic optimisation algorithm (AOA) is proposed. For the inferior group, the global optimal solution is used to guide the update to improve the convergence speed of the algorithm. Finally, the cosine mutation strategy is introduced to perturb the optimal solution and prevent the target from falling into the local extrema as a way to efficiently and reasonably allocate airport gates. The CG-TSA is validated using benchmark test functions, Wilcoxon rank-sum detection, and CEC2017 complex test functions and the results show that the improved algorithm has good optimality-seeking ability and shows high robustness in the multiobjective optimisation problem of airport gate assignment.

The airport gate assignment problem: A Branch-and-Price Approach for improving utilization of jetways

With the development of the civil aviation industry, airports' parking space resources, especially those with jetways, become increasingly scarce. The airport gate assignment problem (AGAP) is a critical issue in airport operation management. When the number of flights is large, solving this situation is challenging. Therefore, designing an efficient gate assignment methodology is very important. This paper considers several significant constraints and aims to maximize the number of passengers using jetways, which is not fully considered in the existing research. A model with gate plans (possible scheme combinations) as decision variables is established, which effectively reduces the number of constraints. However, considering that the scale of this problem is still quite large, the Branch- and-Price algorithm (BAPA) is used to solve the model. The solving speed is improved by adding a heuristic search with a certain probability in each search node. The model's performance is verified by using the actual flight data of Kunming Changshui International Airport, China. The experimental results show that the method of this paper can achieve a 12.71 % increase in the optimization goal compared to the current computer-assisted manual allocation. In terms of finding the optimal and satisfactory solution, the feasibility and effectiveness of this method are verified by comparing with the commercial solver CPLEX, Tabu Search (TS) algorithm, improved Adaptive Parallel Genetic (APGA) algorithm, and Greedy algorithm (GA).

Post disaster adaptation management in airport: A coordination of runway and hangar resources for relief cargo transports

Air traffic congestions for processing relief cargos under post-disaster relief scenarios are common, due to high transport demands within a short time. To enhance the resilience of relief operations at airport, an optimization problem of relief air cargo transportations involving aircraft sequencing and loading/unloading within a designated hangar is studied in this paper. The objective is minimizing the tardiness in fulfilling inbound and outbound relief cargos. A mixed-integer linear programming model is formulated, which incorporates aircraft sequencing and hangar parking planning. To resolve the practical problem efficiently, we propose a two-stage optimization approach, which reduces complexity in solving the original model by coordinating the decisions of aircraft landing and take-off schedule and cargo hangar parking arrangement through iterations. The efficiency of the proposed method is examined through the computational results. High-quality solutions can be obtained by the two-stage optimization method within a reasonable time for practical implementation, which enhances the responsiveness and resource utilization of airport operations management under disaster relief situations.

An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem

Gate assignment problem (GAP) is the core issue of airport operation management. However, the limited resources of airport gates and the increase of flight scale result in serious problems for gate allocation. In this paper, to provide decision-making support for large-scale GAPs, a model based on gate assignment rules (e.g., flight type constraints, safe time interval constraints, and adjacency conflict constraints) is built to formulate the problem. An improved adaptive parallel genetic algorithm (APGA) is then designed to solve the model. The algorithm is effective because it introduces the idea of elite strategy and parallel design and can adaptively adjust the crossover probability. Moreover, different instances are presented to demonstrate the proposed algorithm. The calculation results of this algorithm are compared with those of standard genetic algorithm and CPLEX, which show that the proposed algorithm has better performance and takes a shorter computational time. In addition, we verify the stability and practicability of the algorithm by repeated experiments on large-scale flight data.

Continuous Time Formulation and Lagrangian Relaxation Algorithm for the Gate Assignment Problem

Gates are important operating facilities and resources in civil airports. It is a core task in the airport operation management to select reasonable gates for inbound and outbound flights. We present a continuous time formulation with second-order cone programming (SOCP) for the gate assignment problem which allocates flights to available gates to optimize both the transfer time of passengers and the robustness of the airport operations schedules. The problem is formulated as a mixed integer nonlinear program, and then, the quadratic objective that minimizes the walking distance of transferring passengers is linearized, and the objective that minimizes the variance of idle time at the gates is transformed to a second-order cone constraint with a linear objective function. Then, a Lagrangian relaxation algorithm is developed by exploiting the problem structure. Computational tests are carried out to illustrate the efficiency of the model and the algorithms. It is shown that the continuous time formulation is more efficient than the existing model, and the Lagrangian relaxation algorithm can obtain better solutions faster than a commercial solver.

Assessing the Contribution of Data Mining Methods to Avoid Aircraft Run-Off from the Runway to Increase the Safety and Reduce the Negative Environmental Impacts.

The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution “Safety Support Tools for Avoiding Runway Excursions”. This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection.

Airport operations management

Airport operations management

A robust approach to airport gate assignment with a solution-dependent uncertainty budget

Airport gate assignment (AGA) is a critical issue for airport operations management. It aims to assign flights to gates according to their arrival and departure times. To tackle flight delays in airports, we propose a robust airport gate assignment (RAGA) to minimize the (1−α)-quantile of the total real-time overlap between consecutive flights at the same gate, namely, the total gate blockage time, so that the realized total gate blockage time is worse than its quantile with a probability, at most α. Given any constant, we develop an asymptotically tight upper bound for the violation probability that total gate blockage time is worse than the constant. Based on the upper bound, a solution-dependent uncertainty budget is introduced to develop a robust counterpart (RCP) for the RAGA. We further develop a solution technique for the RCP by transforming the problem into a finite number of tractable binary programmings. An empirical study of the Shuangliu International Airport (CTU) indicates that our proposed robust approach for AGA outperforms existing methods.

Simulation of New Policies for the Baggage Check in the Security Gates of the Airports: The Logiscan Case Study

In recent years, the trend to mix in the same airports legacy and low-cost companies is heavily affecting the airport operations, stressing the security checks in particular. In fact, low-cost companies force the airports to add additional and strict rules for the hand baggage allowance, with additional checks in the security gates, introducing delays for all the passengers, including the legacy ones. Moreover, recent changes to the rules for hand luggage allowance introduced by legacy airlines are forcing the airport operations management to think of new strategies to automate the entire security check process.One of the most advanced systems presently available to speed up the security operations is Logiscan, developed by Datalogic e Logital in collaboration with the “Aeroporto Guglielmo Marconi di Bologna S.p.A.”, the company managing the Bologna Airport. The system automates the operations of size and weight measures and allowance checks and, in conjunction with a redesign of the queuing system, aims to improve the overall system efficiency in terms of accuracy and efficiency.In order to check the new overall system, the entire process has been modeled and analyzed by means of AirSIM, a simulation tool for airport operations based on the well-known OMNeT++ Discrete Event Simulator. AirSIM is an object-oriented simulator able to describe the behavior of different passenger types, as well as to incorporate the logic of different flow management policies. In this paper we will show both how AirSIM can be used to analyze and choose the different parameters of the new security system of an airport, and how the overall system performances can be enhanced.

Airport taxi planning: Lagrangian decomposition

SUMMARYThe airport taxi planning (TP) module is a decision tool intended to guide airport surface management operations. TP is defined by a flow network optimization model that represents flight ground movements and improves aircraft taxiing routes and schedules during periods of aircraft congestion. TP is not intended to operate as a stand‐alone tool for airport operations management: on the contrary, it must be used in conjunction with existing departing and arriving traffic tools and overseen by the taxi planner of the airport, also known as the aircraft ground controller. TP must be flexible in order to accommodate changing inputs while maintaining consistent routes and schedules already delivered from past executions. Within this dynamic environment, the execution time of TP may not exceed a few minutes. Classic methods for solving binary multi‐commodity flow networks with side constraints are not efficient enough; therefore, a Lagrangian decomposition methodology has been adapted to solve it. We demonstrate TP Lagrangian decomposition using actual data from the Madrid‐Barajas Airport. Copyright © 2011 John Wiley & Sons, Ltd.

Simple Sensors Used for Measuring Service Times and Counting Pedestrians

Sensors composed of light barriers and switching mats are used to continuously measure service times at security and border checkpoints in an airport setting. The service times in combination with the number of service checkpoints operating in parallel determine the passenger flow. Checkpoints are a major bottleneck at airports and hence determine the capacity of the whole airport system. The results show that at the considered locations both light barriers and switching mats after initial calibration are able to provide continuous information of acceptable accuracy on the distribution of service times. In addition, the technology can be used to inexpensively measure the flow of passengers with reasonable accuracy compared with other pedestrian counting technologies. As such it provides valuable input to the airport operations management.

A decision support system for total airport operations management and planning

SUMMARYThe airport planning and decision making process exhibits various trade‐offs and complications due to the large number of stakeholders having different, and sometimes conflicting, objectives regarding the assessment of airport performance. As a result, the airport performance assessment necessitates the use of advanced modelling capabilities and decision support systems or tools in order to capture the multifaceted aspects, interests and measures of airport performance like capacity, delays, safety, security, noise and cost‐effectiveness. Presently, airport decision makers lack decision support tools able to provide an integrated view of total airport (both airside and landside) operations and analyse at a reasonable effort and decision‐oriented manner the various trade‐offs involved among different airport performance measures. The objective of this paper is twofold: (i) to describe the decision‐oriented modelling framework and development process of a decision support system for total airport operations management and planning, and (ii) to demonstrate the decision support capabilities and basic modelling functionalities of the proposed system. Copyright © 2010 John Wiley & Sons, Ltd.