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

Liang, Bingjie ; Li, Yongliang ; Bi, Jun ; Ding, Cong ; Zhao, Xiaomei Pellegrini, Paola ; Paola Pellegrini

Journal of advanced transportation, 2020-12, Vol.2020, p.1-17 [Periódico revisado por pares]

London: Hindawi

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Título:
An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem
Autor: Liang, Bingjie ; Li, Yongliang ; Bi, Jun ; Ding, Cong ; Zhao, Xiaomei
Pellegrini, Paola ; Paola Pellegrini
Assuntos: Adaptive algorithms ; Aircraft ; Airline scheduling ; Airports ; Algorithms ; Analysis ; Aviation ; Computer applications ; Computer simulation ; Computing time ; Crossovers ; Decision making ; Efficiency ; Flight ; Genetic algorithms ; Heuristic ; Integer programming ; Linear programming ; Literature reviews ; Management science ; Mathematical programming ; Methods ; Operations research ; Optimization ; Passengers ; Resource management
É parte de: Journal of advanced transportation, 2020-12, Vol.2020, p.1-17
Descrição: 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.
Editor: London: Hindawi
Idioma: Inglês

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

Liang, Bingjie ; Li, Yongliang ; Bi, Jun ; Ding, Cong ; Zhao, Xiaomei Pellegrini, Paola ; Paola Pellegrini

London: Hindawi

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