Optimizing airport gate assignment with operational safety constraints

Liu, Shuo; Chen, Wenhua; Liu, Jiyin

doi:10.1109/iconac.2014.6935461

Cited by 5 publications

(12 citation statements)

References 14 publications

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“…Due to the limitations of the search scope, this method ignores the key factors that lead to poor configuration results. And when the number of flights reaches thousands, the complexity increases with order, and artificial intelligence methods are difficult to attain the requirements of real-time allocation at boarding gates [5] .…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Summary of research on optimum configuration of airport boarding gate

Liu¹,

Liu²,

Jiang³

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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With the increase in air transportation, the problem of of the airport gate shortage has become increasingly prominent. Unreasonable gate allocation will affect resource utilization, reduce customer satisfaction, and even lead to chaos in airport operations, restricting the rapid development of the airport. As the expansion of the gate requires a lot of capital, manpower and material resources, it is extremely important to optimize the configuration of the existing gates using reasonable and efficient methods. This article reviews the research status of the optimization of airport gates at home and abroad. The three main research methods are summarized and the general process of airport gate optimization configuration is sorted out, and the commonly used airport gate optimization configuration models are reviewed, including the main decision variables, optimization goals and constraints. The paper briefly analyzes the solution of the model based on the accurate algorithms, heuristic algorithm and meta-heuristic algorithm, and looks forward to the future research direction of the optimal configuration of airport gates.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Summary of research on optimum configuration of airport boarding gate

Liu¹,

Liu²,

Jiang³

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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“…Tabu search and ant colony algorithms were used to obtain a reasonable allocation scheme with better robustness. Liu et al [25] proposed an optimization model considering operational security, the objective of which was to minimize the deviation of the gate idle time, and a genetic algorithm was used to solve the model. Dell'Orco et al [26] proposed a new metaheuristic algorithm which was called fuzzy bee colony algorithm that combines bee colony algorithm and fuzzy inference system to minimize the total passenger travel time and the number of remote stands used.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Liang

Li²,

et al. 2020

Journal of Advanced Transportation

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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.

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“…Passenger satisfaction, usually defined as the minimization of the total walking distance, is probably the most popular objective considered in the literature (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). By nature, the total passenger walking distance is based on the passenger transfer volume between every pair of aircrafts and the distance between every pair of gates.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The idea of ungated flights, considered as a way of minimizing the number of flights assigned to remote terminal gates (apron stands), or equivalently, maximizing the number of flights assigned to fixed gates is recognized as the second most prevalent objective in the literature (14,16,17,23,27). This issue is discussed predominantly in the context of avoiding mismatches between flight sizes and gate types, as well as satisfying safety requirements in (27).…”

Section: Literature Reviewmentioning

confidence: 99%

A Non-Dominated Sorting Genetic Algorithm Approach for Optimization of Multi-Objective Airport Gate Assignment Problem

Mokhtarimousavi

Talebi

Asgari

2018

Transportation Research Record: Journal of the Transportation R

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Gate assignment problems (GAP) are one of the most substantial issues in airport operation. The ever-increasing demand producing high occupancy rates of gates, the potential financial loss from imbalances between supply and demand in congested airports, and the limited scope for expanding facilities present challenges that require an advanced methodology for optimal supply allocation. In principle, tackling GAP involves seeking to maintain an airport’s maximum capacity through the best possible allocation of resources (gates). There are a wide range of dependent and independent resources and limitations involved in the problem, adding to the complexity of GAP from both theoretical and practical perspectives. In this study, GAP is extended and mathematically formulated as a three-objective problem, taking into account all resources and restrictions, which can be directly linked to airport authorities’ multiple criteria decision-making processes. The preliminary goal of multi-objective formulation is to consider a wider scope, in which a higher number of objectives are simultaneously optimized, and thus to increase the practical efficiency of the final solution. The problem is solved by applying the second version of Non-dominated Sorting Genetic Algorithm (NSGA-II) as a parallel evolutionary optimization algorithm. Results illustrate that the proposed mathematical model could address most of the major criteria in the decision-making process in airport management in terms of passenger walking distances, robustness, and traditional costs. Moreover, the proposed solution approach shows promise in finding acceptable and plausible solutions compared with other multi-objective algorithms (BAT, PSO, ACO, and ABC).

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Optimizing airport gate assignment with operational safety constraints

Cited by 5 publications

References 14 publications

Summary of research on optimum configuration of airport boarding gate

Summary of research on optimum configuration of airport boarding gate

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

A Non-Dominated Sorting Genetic Algorithm Approach for Optimization of Multi-Objective Airport Gate Assignment Problem

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