A heuristic approach for optimal integrated airline schedule design and fleet assignment with demand recapture

Abstract Flight schedule design and fleet assignment are the two main elements of the airline scheduling process, which have the highest effect on cost and revenue. Although mixed-integer linear programming models were developed for integrated schedule design and fleet assignment, it has been shown that this approach was not efficient for large-scale models. Therefore, this paper aimed at developing a parallel master–slave Genetic Algorithm (PMS-GA) for solving the integrated flight schedule design and fleet assignment problem with demand recapture, particularly for large-scale problems. The integrated schedule design and fleet assignment problem was solved by the master GA, while the slave GA nested inside the master GA solved passenger flow adjustment problem. Considering the complexities of a large-scale integrated problem, we (1) proposed an innovative approach for creating feasible suboptimal initial population, (2) developed customized genetic operators to improve the performance of the PMS-GA compared to the conventional GAs, and (3) implemented migration and repopulation to prevent premature convergence. PMS-GA was tested on seven models with small-, medium-, and large-scales, and the results were compared with the gold-standard mixed-integer linear programming in terms of cost and runtime. The comparative study showed that the PMS-GA achieved suboptimal solutions with costs only 1.8% to 3.0% different than the optimal solution for medium- and large-scale models. However, these solutions were obtained in significantly shorter runtimes (over 500% to 1000%) compared to the mixed-integer linear programming. Also, the results showed that in contrast to the mixed-integer linear programming approach, runtimes of the proposed PMS-GA are highly predictable as a function of the problem size. Our results showed the importance of PMS-GA for integrated schedule design and fleet assignment, particularly for solving large-scale re-scheduling problems in a short time.

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