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Solution to boarding bridge operator scheduling problembased on improved adaptive genetic algorithm(PDF)


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Solution to boarding bridge operator scheduling problembased on improved adaptive genetic algorithm
Ding Fang1Yang Chuang1Guan Shandu2Chen Guibo2
1.College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China; 2.Baiyun Airport Ground Equipment Co.,Ltd. Ground Service Department,Guangzhou CityGuangdong Province,Guangzhou 510000,China
improved adaptive genetic algorithm boarding bridge operator scheduling fitness function scheduling principle
In order to reduce the probability of the disruption of airport operation order,inefficiency and unbalanced workload caused by boarding bridge operator scheduling problem,an improved adaptive genetic algorithm combined with the scheduling principle is used to model and solve the scheduling problem. The corresponding fitness function is designed according to the problems. In order to overcome the problem that the traditional genetic algorithm can not be directly applied to the problem and can not improve the performance,the execution process of the algorithm is improved according to the characteristics of the scheduling problem. Finally,the improved algorithm is used to optimize the calculation of the problem. Satisfactory results are obtained through the improved algorithm calculation,and compared with the basic genetic algorithm,traditional improved adaptive genetic algorithm(AGA)and simulated annealing genetic algorithm,it is found that the performance is greatly improved. The improved algorithm can not only avoid the premature problem and the hidden danger of manual scheduling,but also accelerate convergence speed. It overcomes the problem that the traditional genetic algorithm can not be directly applied to the scheduling problem of boarding bridge drivers,and provides the airport ground service department tools and methods for scheduling problem,and has important practical significance and engineering application value.


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Last Update: 2019-02-28