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The Aircraft Maintenance Routing Problem
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Particle swarm optimization for integer programming
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Particle swarm optimization: an introduction and its recent developments
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Using Ant Colony Optimization Algorithm to Solve Airline Crew Scheduling Problems
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Pseudocoevolutionary genetic algorithms for power electronic circuits optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant colony system: a cooperative learning approach to the traveling salesman problem
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Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
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Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
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Airline flight frequency determination in response to competitive interactions using fuzzy logic
Mathematical and Computer Modelling: An International Journal
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This paper proposes a PSO-FFPP algorithm based on the particle swarm optimization (PSO) framework to solve the flight frequency programming problem (FFPP). The FFPP is to determine the flight frequency for each type of aircraft on each flight route. This problem is fundamental to an airline's operational planning because it affects the airline's profit and market share greatly. The FFPP can be formulated as an integer programming problem with constraints that is very suitable to be solved by the PSO algorithm. The proposed PSO-FFPP algorithm codes the decision variables of the FFPP with real number to represent the potential solutions and defines the optimization objective as a maximization problem for the airlines profit. A constraints handling method that combines the ideas of feasible solution preserving and infeasible solution rejection is developed. This method avoids the expense of infeasibility repair or penalty, making the algorithm simple to use and easy to extend. An integer handing process is also devised to round the real number to the nearest valid integer before feasibility check and function evaluation. This process maintains the search tendency of the PSO algorithm and can help to search in a promising region for the global optimum. The feasibility of the proposed algorithm is demonstrated and compared with the Monte Carlo method and the enumeration method on a simulation case with promising results. Experiments are also conducted to investigate the factors that affect the solution quality and computational time.