The Application of Ant Colony Optimization Algorithm in the Flight Landing Scheduling Problem

2013 ◽  
Vol 411-414 ◽  
pp. 2698-2703
Author(s):  
Xiao Rong Feng ◽  
Xing Jie Feng ◽  
Dong Liu
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Global Search ◽  
Ant Colony ◽  
Particle Swarm Algorithm ◽  
Scheduling Problem ◽  
Ant Colony Optimization Algorithm ◽  
Flight Delays ◽  
First Come First Serve ◽  
Np Hard Problem

Flights landing scheduling problem is an NP-hard problem, the article presents an Ant colony optimization algorithm based on dynamic calculation of the heuristic information to solve a single runway flights landing scheduling problem. The algorithm has better global search ability and relatively fast convergence rate. The experimental results show that compared with traditional first come first serve, genetic algorithm and particle swarm algorithm, this method can quickly give the better flight approach and landing order to help controllers make efficient aircraft scheduling policy and reduce flight delays. Keywords:Heuristic Information entropy Ant colony optimization Global search

An Ant Colony System Algorithm for the Hybrid Flow-Shop Scheduling Problem

2012 ◽  
pp. 85-98
Author(s):  
Safa Khalouli ◽  
Fatima Ghedjati ◽  
Abdelaziz Hamzaoui
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Flow Shop ◽  
Ant Colony ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Ant Colony Optimization Algorithm ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

An integrated ant colony optimization algorithm (IACS-HFS) is proposed for a multistage hybrid flow-shop scheduling problem. The objective of scheduling is the minimization of the makespan. To solve this NP-hard problem, the IACS-HFS considers the assignment and sequencing sub-problems simultaneously in the construction procedures. The performance of the algorithm is evaluated by numerical experiments on benchmark problems taken from the literature. The results show that the proposed ant colony optimization algorithm gives promising and good results and outperforms some current approaches in the quality of schedules.

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