Solving airport gate assignment problem using an improved genetic algorithm with dynamic topology

Abstract

The rapid growth of air transportation demand led to numerous studies on the airport gate assignment problem (AGAP). As the problem scale gets larger, mathematical programming is no longer available, and heuristic methods like genetic algorithm (GA) have been applied. Taking into account of adding soft constraint to the model and ameliorate objective value of the AGAP, this paper proposes an improved GA considering structural properties to avoid GA’s prematurity. A dynamic topology integrated in crossover operator contributes to a better tradeoff between convergence speed and quality of solutions. Finally, experimental results illustrate the effectiveness of the proposed improved GA to solve AGAP in comparison with traditional GA and a reliable commercial software CPLEX CP Optimizer.