Airport Gate Assignment Research Articles

Dynamic airport gate assignment with improved Shuffled Frog-Leaping Algorithm and triangle membership function

he rapid development of the air transportation industry has increased air traffic, posing challenges to the task of airport gate assignment (AGA) for flights. Most past studies have solved the AGA problem (AGAP) using deterministic models, which are incapable of dealing with uncertainty and dynamic conditions at airports. Thus, this research employs fuzzy theory and proposes a triangular membership function to handle flight uncertainty in the AGAP. In addition, an improved metaheuristic, termed the improved Shuffled Frog-Leaping Algorithm (ISFLA), is proposed to circumvent the computationally intractable problems commonly faced by exact approaches when handling large instances. In this research, the AGAP is first formulated as a stochastic Mixed-Integer Linear Programming (MILP) model, with stochastic flight lateness and earliness considered. The objective of this model is to minimize the total cost, which consists of three sub-costs: passenger walking distances, non-preferred gate (NPG) assignments for planes, and fuzzy idle times of gates. These three sub-costs correspond to the major concerns of passengers, airlines, and airports, respectively. The cooperation between the ISFLA and the triangular membership function demonstrates their capability to effectively handle big AGAP instances. Furthermore, the experimental results show that the ISFLA outperforms the standard SFLA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA).

Gate Assignment Algorithm for Airport Peak Time Based on Reinforcement Learning

In existing airport gate allocation studies, little consideration has been given to situations where gate resources are limited during peak periods. Under such circumstances, some flights may not be able to make regular stops. In this paper, the airport gate assignment problem under peak time is investigated. We propose a gate pre-assignment model to maximize the gate matching degree and the near gate passenger allocation rate. Besides, to minimize the pre-assignment gate change rate, we propose a dynamic reassignment model based on the pre-assignment model. By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a gate assignment algorithm based on proximal policy optimization (GABPPO) is proposed. The simulation results show that the algorithm can effectively solve the gate shortage problem during the airport peak period. Compared with the adaptive parallel genetic, deep Q-network, and policy gradient algorithms, the target value of solutions obtained by the proposed algorithm in the near gate passenger allocation rate is increased by 5.7%, 3.6%, and 7.9%, respectively, and the target value in the gate matching degree is increased by 10.6%, 4.9%, and 11.5% respectively.

Joint runway–gate assignment based on the Branch-and-Price algorithm

Airport runway assignment and gate assignment are both crucial problems for large multirunway airports. A cooperative schedule for the runway and gate can have a significant effect on airport ground operation efficiency and safety. This paper focuses on the joint assignment problem of runways and gates considering parking time differences and airline preferences. A quadratic model is established to reduce taxiing fuel consumption loss and increase the robustness of gate assignment. An improved branch-and-price algorithm is proposed by utilizing an improved pulse algorithm and various acceleration strategies to improve the solution performance. The actual flight data at an international hub airport are utilized to validate the proposed model. The results indicate that the joint assignment model can effectively reduce ground taxiing loss, optimize gate assignment robustness, and increase the utilization rate of contact gates. The proposed algorithm also achieves better computational accuracy and efficiency.

Buffer Times Between Scheduled Events in Resource Assignment Problem: A Conflict-Robust Perspective

Problem definition: In many resource scheduling problems for services with scheduled starting and completion times (e.g., airport gate assignment), a common approach is to maintain appropriate buffer between successive services assigned to a common resource. With a large buffer, the chances of a “crossing” (i.e., a flight arriving later than the succeeding one at the gate) will be significantly reduced. This approach is often preferred over more sophisticated stochastic mixed-integer programming methods that track the arrival of all the flights to infer the number of “conflicts” (i.e., a flight arriving at a time when the assigned gate becomes unavailable). We provide a theoretical explanation, from the perspective of robust optimization for the good performance of the buffering approach in minimizing not only the number of crossings but also the number of conflicts in the operations. Methodology/results: We show that the buffering method inherently minimizes the worst-case number of “conflicts” under both robust and distributionally robust optimization models using down-monotone uncertainty sets. Interestingly, under down-monotone properties, the worst-case number of crossings is identical to the worst-case number of conflicts. Using this equivalence, we demonstrate how feature information from flight and historical delay information can be used to enhance the effectiveness of the buffering method. Managerial implications: The paper provides the first theoretical justification on the use of buffering method to control for the number of conflicts in resource assignment problem. Funding: This work was supported by the 2019 Academic Research Fund Tier 3 of the Ministry of Education-Singapore [Grant MOE-2019-T3-1-010] and the Research Grants Council of Hong Kong SAR, China [Grant PolyU 152240/17E]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0572 .

A branch-and-price algorithm for the airport gate assignment problem considering the trade-off between robustness and efficiency

In this paper, a novel objective for the airport gate assignment problem which considers the trade-off between robustness and operational efficiency in a unified dimension is developed. A task-based two-phase model is established, where the branch-and-price framework is embedded in the first stage. A set partitioning model is developed, where a series of aircrafts assigned to the same gate of a certain type are defined as a gate plan. The pricing subproblem is also decomposed into three parallel subproblems according to the gate size to account for the heterogeneity of the gates. Each gate plan is allocated to a physical gate in order to minimize the utilization of remote gates in the second stage. The pulse algorithm with two acceleration strategies, that generates a lower bound matrix in reverse order and applies path combination, is used to solve the pricing subproblems. Finally, extensive computational experiments are conducted to demonstrate the high performance of the proposed model, especially for large-scale instances.

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.

Efficient Aircraft Arrival Sequencing Given Airport Gate Assignment

The aircraft sequencing problem has been well discussed in many variants over the years; and during this time, the problem has changed from a standalone optimization problem to an embedded problem. At hub airports, aircraft sequencing influences passengers’ available connecting times. Short connections could benefit from a modification of the arrival or departure sequence. In our work, we have developed a bilevel optimization problem that combines the aircraft sequencing problem and the airport gate assignment problem at hub airports to make connections more efficient. The decision on the gate assignments is made at the upper level, whereas the efficient aircraft arrival sequencing is addressed at the lower level. Methods for solving this kind of large-scale bilevel optimization problem by considering a full day of operation and dynamically changing traffic situations have yet to be researched. In this paper, we focus on efficient aircraft sequencing by taking into consideration gate assignments, and we propose a multiobjective mixed integer linear program that is solved by an interactive method and considers the (contradicting) objectives of competing airlines and the air traffic control. The capabilities of our approach are demonstrated by modeling and solving a real-case scenario for Munich Airport.

A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem.

Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm.

A branch-and-price approach for airport gate assignment problem with chance constraints

At an airport, the flights should be attached to the gates to embark or disembark the passengers. The airport gate assignment problem concerns the efficient utilization of gates by assigning flights to the gates according to the flights’ planned schedules. However, due to ever-increasing air traffic demands and unpredictable weather conditions, it is hard to expect that the flights strictly follow the planned schedules. Therefore, establishing a robust gate assignment plan against uncertain flight schedule deviations is crucial for airport operators. However, ensuring the robustness of the plan by imposing too long idle times of gates may deteriorate the efficiency of gate usage. To address the trade-off between two goals, we introduce an overlap chance-constrained airport gate assignment problem that limits the probability of an overlap occurrence of flight schedules for each gate to given thresholds while maximizing the sum of preference values of flights for gates. We propose a network-based integer programming model for this problem, estimating the probability distribution using the historical flight arrival/departure deviation data. Then, we strengthen the model using the concept of gate assignment patterns, and a branch-and-price algorithm is devised to solve the model. A labeling algorithm-based efficient column generation algorithm is developed, further accelerated by clustering gates with similar characteristics. Through computational experiments, we demonstrate the efficiency of the proposed algorithm, and the trade-off between the overlap occurrence probability and the preference is analyzed using real-life instances.

A new formulation and an effective matheuristic for the airport gate assignment problem

This study considers an airport gate assignment problem where a set of aircraft arriving to an airport are assigned to the fixed gates of the airport terminal or to the apron. The aim is to lexicographically minimize the number of aircraft assigned to the apron, and then the total walking distance by passengers. A new mixed integer linear programming formulation and a matheuristic is proposed for the problem. The proposed formulation is based on the idea of flow of passengers and has smaller size compared to the existing formulations in the literature. The proposed matheuristic, which relies on solving a restricted version of the proposed formulation of the problem, is not only easy to implement but is also very effective. A computational study performed on benchmark instances reveals that the proposed formulation and the matheuristic outperform the existing exact and heuristic algorithms in the literature.

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.

Airport Gate Assignment as a Nash Equilibrium Problem

The airport gate assignment problem addresses the optimal assignment of a set of aircraft to a set of stands. The underlying combinatorial optimization problem is usually modeled as a binary quadratic assignment problem, whereby the stand assignment of a certain aircraft depends on the stand assignment of all other aircraft. The solving time of the optimization problem may increase exponentially with the number of aircraft and stands considered. For this reason, real-case scenarios can be solved only by heuristics in due time. In this paper, we propose a novel approach on modeling and solving the airport gate assignment problem by making use of the game theory. The aim is to identify a Nash equilibrium as a solution of the airport gate assignment problem in the following sense: no aircraft can improve its stand assignment by a sole deviation from its assigned stand. The algorithm is capable of delivering an assignment for a real-case scenario in minutes instead of hours. The performance of the algorithm is demonstrated by modeling and solving a real-case scenario for terminal 2 of the Munich Airport.

An Evolutionary Algorithm in Static Airport Gate Assignment Problem

Background and Objectives: Gate Assignment Problem is an existing issue at modern airports. Gate assignment is a complex issue in which different airports have their own demographic and geographic features although the gate and flight pattern are identical, and flights may not be assigned precisely to the gates. Methods: The gate assignment model would be a suitable and an appropriate tool for airport authorities to assign aircraft to gates in an effective and efficient way. The aim of the model is to assign each aircraft to an available gate to maximise both efficient operations for airports and airlines, and convenience for passengers. The model would benefit airports by improving efficiency of operations and convenience for travellers. The model illustrates how the resources are fully utilised, achieving an optimal result. This model applies the evolutionary approach to handle the gate assignment problem. The smart and generative algorithm speeds up the solving process for providing the solution within a reasonable time. Results: This model can reduce the business class travellers’ total walking distance by optimising the utilisation of gate resources. This has been was applied at the Taiwan Taipei Taoyuan International Airport and the results have shown an improvement in minimising the total walking distances, and the results for business class travellers are promising. Conclusion: A metropolitan airport usually handles more than thirty boarding gates and hundreds of flights every day. Gate assignment can help an airport to assign the gates to the flights more effectively, with the advancement of genetic algorithms. The gate assignment problem model performed a successful assignment solution within an acceptable timeframe. The proposed evolutionary algorithm gate assignment model could reduce the business class passengers’ total walking distances.

Optimization of multi-objective airport gate assignment problem: considering fairness between airlines

The Airport Gate Assignment Problem (AGAP) deals with assigning a set of aircraft to the gates at airports with respect to several operational and commercial constraints. In this study, we model the AGAP as a multi-objective optimization problem that minimizes the aircraft taxiing costs and passenger walking distance. An additional consideration is taken regarding the fairness of assignment among different airlines. To solve this problem, we propose the NSGA-II-LNS algorithm which builds upon the NSGA-II framework and incorporates the advantages of Large Neighborhood Search (LNS) for refining the solution quality. Two search operators are designed within LNS tailored to different senses of fairness coefficients. A numerical study at Nanjing Lukou International Airport indicates that the proposed algorithm significantly outperforms the previously published algorithms in terms of both solution convergence and diversity. Moreover, acceptable computing time implies the practical potential of our model.

A robust strategy to address the airport gate assignment problem considering operators’ preferences

The airport gate assignment problem (AGAP) has been well studied in the field of airport transportation planning and operations management. For airport operators, it is important to assign flight activities to limited gates economically, and the robustness of the assignment is also a key issue. In this paper, we address the problem of assigning a number of flight activities, including arrival, parking, and departure, to different gates during the operating period. A robust strategy is developed by considering the airport operators’ tow-averse attributes. To solve the problem, firstly, a multi-objective integer programming model is proposed. There are two objectives in the model, one is to maximize the operators’ preferences characterized by scores, and the other is to minimize robustness cost caused by the changes of flight schedule. Secondly, a two-phase Monte Carlo based NSGA-II (TPMC-NSGA II) algorithm is designed, which effectively combines the Monte Carlo characters and the NSGA-II algorithm. Furthermore, a set of computational analyses are conducted. The results show that the proposed model and algorithm are capable of solving the airport gate assignment problem with an economical, robust, and preferred output.

A New Formulation and an Effective Matheuristic for the Airport Gate Assignment Problem

A New Formulation and an Effective Matheuristic for the Airport Gate Assignment Problem

Robust Airport Gate Assignment to Minimize Aircraft Conflicts

Robust Airport Gate Assignment to Minimize Aircraft Conflicts

A New Formulation and an Effective Matheuristic for the Airport Gate Assignment Problem

A New Formulation and an Effective Matheuristic for the Airport Gate Assignment Problem

Learning-driven feasible and infeasible tabu search for airport gate assignment

The gate assignment problem (GAP) is an important task in airport management. This study investigates an original probability learning based heuristic algorithm for solving the problem. The proposed algorithm relies on a mixed search strategy exploring both feasible and infeasible solutions with the tabu search method and employs a reinforcement learning mechanism to guide the search toward new promising regions. The algorithm is compared with several reference algorithms on three sets of real-world benchmark instances in the literature. Computational results show the high competitiveness of the algorithm in terms of solution quality and computation time. Especially, it reports improved best solutions (new upper bounds) for all the 180 tested real-world benchmark instances in the literature. The key components of the algorithm are analyzed. The code of the algorithm will be publicly available.

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).