Abstract
Crew pairing is a sequence of flights beginning and ending at the same crewbase. Crew pairing planning is one of the primary processes in airline crew scheduling; it is also the primary cost-determining phase in airline crew scheduling. Optimizing crew pairings in an airline timetable helps minimize operational crew costs and maximize crew utilization. There are numerous restrictions that must be considered and just as many regulations that must be satisfied in crew pairing generation. The most important regulations—and the ones that make crew pairing planning a highly constrained optimization problem—are the the limits of the flight and the duty periods. Keeping these restrictions and regulations in mind, the main goal of the optimization is the generation of low cost sets of valid crew pairings which cover all flights in the airline’s timetable. For this research study, We examined studies about crew pairing optimization and used these previously existing methods of crew pairing to develop a new solution of the crew pairing problem using genetic algorithms. As part of the study we created a new genetic operator—called perturbation operator.Unlike traditional genetic algorithm implementations, this new perturbation operator provides much more stable results, an obvious increase in the convergence rate, and takes into account the existence of multiple crewbases.
Highlights
Airline crew scheduling is defined as process of assigning crew members with a variety of flight qualifications to flight duties to ensure all flights in an airline’s timetable are properly covered by the crew
We examined studies about crew pairing optimization and used these previously existing methods of crew pairing to develop a new solution of the crew pairing problem using genetic algorithms
The crew pairing optimization problem constitutes the main focus of this study
Summary
Airline crew scheduling is defined as process of assigning crew members with a variety of flight qualifications to flight duties to ensure all flights in an airline’s timetable are properly covered by the crew. Individuals have a higher probablity of survival than others, and less fit individuals are eliminated In this way, during the evolutionary process, the genes (genetic information) of individuals of good quality are transfered to new generations. Gene combinations of individuals of good quality may yield more fit individuals than themselves individuals of better quality can be produced during the evolution process. Genetic algorithms simulate this evolutionary optimization process by applying genetic operators in the first generation which is produced randomly in following generations. Individuals of good quality transfer their genetic information to new generations with a reproduction process which is implemented by crossover operator with other individuals of good quality. The whole process is repeated until a satisfactory solution was found
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