A ripple-spreading Genetic Algorithm for the airport Gate Assignment Problem

Since the Gate Assignment Problem (GAP) at airport terminals is a combinatorial optimization problem, permutation representations based on aircraft dwelling orders are typically used in the implementation of Genetic Algorithms (GAs), The design of such GAs is often confronted with feasibility and memory-efficiency problems. This paper proposes a hybrid GA, which transforms the original order based GAP solutions into value based ones, so that the basic a binary representation and all classic evolutionary operations can be applied free of the above problems. In the hybrid GA scheme, aircraft queues to gates are projected as points into a parameterized space. A deterministic model inspired by the phenomenon of natural ripple-spreading on liquid surfaces is developed which uses relative spatial parameters as input to connect all aircraft points to construct aircraft queues to gates, and then a traditional binary GA compatible to all classic evolutionary operators is used to evolve these spatial parameters in order to find an optimal or near-optimal solution. The effectiveness of the new hybrid GA based on the ripple-spreading model for the GAP problem are illustrated by experiments.

[1]  Yu Gu,et al.  GENETIC ALGORITHM APPROACH TO AIRCRAFT GATE REASSIGNMENT PROBLEM , 1999 .

[2]  Xiao-Bing Hu,et al.  An efficient Genetic Algorithm with uniform crossover for the multi-objective Airport Gate Assignment Problem , 2007 .

[3]  Ezequiel A. Di Paolo,et al.  Ripple-spreading model and Genetic Algorithm for random complex networks: Preliminary study , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[4]  Ali Haghani,et al.  Optimizing gate assignments at airport terminals , 1998 .

[5]  Dušan Teodorović,et al.  Aircraft Stand Assignment to Minimize Walking , 1984 .

[6]  Wen-Hua Chen,et al.  Genetic algorithm based on receding horizon control for arrival sequencing and scheduling , 2005, Eng. Appl. Artif. Intell..

[7]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[8]  Ezequiel A. Di Paolo,et al.  An efficient Genetic Algorithm with uniform crossover for the multi-objective Airport Gate Assignment Problem , 2007, 2007 IEEE Congress on Evolutionary Computation.

[9]  Jun Zhang,et al.  Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms , 2007, IEEE Transactions on Evolutionary Computation.

[10]  Ahmet Bolat,et al.  Procedures for providing robust gate assignments for arriving aircrafts , 2000, Eur. J. Oper. Res..

[11]  A Bolat,et al.  Models and a genetic algorithm for static aircraft-gate assignment problem , 2001, J. Oper. Res. Soc..

[12]  Krishnaswami Srihari,et al.  An expert system methodology for aircraft-gate assignment , 1991 .

[13]  S. F. Wu,et al.  A self-adaptive Genetic Algorithm based on fuzzy mechanism , 2007, 2007 IEEE Congress on Evolutionary Computation.

[14]  Dennis F. X. Mathaisel,et al.  Optimizing Gate Assignments at Airport Terminals , 1985, Transp. Sci..

[15]  Geoffrey D. Gosling,et al.  Design of an expert system for aircraft gate assignment , 1990 .

[16]  Richard A. Bihr,et al.  A conceptual solution to the aircraft gate assignment problem using 0,1 linear programming , 1990 .

[17]  T. Glenn Bailey,et al.  The airport gate assignment problem: mathematical model and a tabu search algorithm , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[18]  Yi Zhu,et al.  The over-constrained airport gate assignment problem , 2005, Comput. Oper. Res..

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .