Crew Pairing Optimization with Genetic Algorithms

Abstract

Abstract. We present an algorithm for the crew pairing problem, an optimization problem that is part of the airline crew scheduling procedure. A pairing is a round trip starting and ending at the home base, which is susceptible to constraints that arise due to laws and regulations. The purpose of the crew pairing problem is to generate a set of pairings with minimal cost, covering all flight legs that the company has to carry out during a predefined time period. The proposed solution is a two-phase procedure. For the first phase, the pairing generation, a depth first search approach is employed. The second phase deals with the selection of a subset of the generated pairings with near optimal cost. This problem, which is modelled by a set covering formulation, is solved with a genetic algorithm. The presented method was tested on actual flight data of Olympic Airways.

Fuzzy-Logic Controlled Genetic Algorithm for the Rail-Freight Crew-Scheduling Problem

“…GAs have been applied either for the production of additional columns as a part of column generation [8] or for the solution of an SCP from the set of columns generated prior to the application of a GA [9][10][11][12], but there…”

An Improved Genetic Algorithm for Crew Pairing Optimization

“…First, an enormous number of legal crew pairings are generated for each crewbase. To generate crew pairings, airlines use either column generation based methods (as seen in reference [6]) or iterative tree search algorithms (as seen in reference [7]). Column generation based methods generate crew pairings during optimization in progress.…”

“…A method which was developed in reference [9] for abnormally large problems based on random pair generation with dept-first search algorithm can be applied. In this study, dept-first search method is used to generate crew pairings (like those in reference [7]). In the second phase, the optimization process is completed.…”

“…As noted in reference [4], Beasley and Chu used column based chromosomes, variable mutation rate, and a heuristic mechanism to repair chromosomes to solve the problem. In reference [7], Kornilakis and Stamatopoulos developed a crew pairing algorithm based on reference [4] and reported good results. And in reference [10], a row-based representation is used to solve a set-covering problem but the representation technique and operators used in this study make it difficult to cope with unfeasible chromosomes.…”

“…Generally, the crew pairing problem is solved in two phases because of the large size of search space (as can be seen in references [6] and [7]), and the constraints which convert the problem to a highly constrained nonlinear optimization problem and objective function are satisfied. 1) Crew pairing generation: In this phase, many legal crew pairings are generated using the airline's timetable; for each crew pairing necessary values are carried out that will be used in calculating the objective function.…”