Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Decision support for airline system operations control and irregular operations
Computers and Operations Research
How to solve it: modern heuristics
How to solve it: modern heuristics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Bi-Criterion Approach for the Airlines Crew Rostering Problem
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
International Journal of Computer Applications in Technology
Intelligent process planning methods for the manufacturing of moulds
International Journal of Computer Applications in Technology
Scheduling optimisation for supply chain in networked manufacturing
International Journal of Computer Applications in Technology
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
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Within the airline, any disturbance to normal operations has a dramatic impact, and usually imposes high additional costs. Because of irregular events during daily operations. When disruptions occur, flight schedules are affected due to the resulting infeasible flight schedule and improper assignments. Therefore, airlines need to recover disrupted schedule as soon as possible and minimise the extra cost as well as the impact on the airline image and customer satisfaction. Approaches such as linear programming, network modelling, greedy heuristics and decisions support systems are well-known approaches in solving irregular airline operation problem. This paper presents an alternative approach based on Multi Objective Genetic Algorithm. The aim of this research is to introduce the concept of Genetic Algorithm as a tool to solve irregular airline operation, amalgamation problem and monitor the reasons of schedule disruptions. The proposed model could obtain optimal solutions within seconds based on real data from medium airline case.