A dual population multi-operator genetic algorithm for flight deck operations scheduling problem

Abstract

It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations. In this paper, the precedence constraints and resource constraints in flight deck operations are analyzed, then the model of the multi-aircraft integrated scheduling problem with transfer times (MAISPTT) is established. A dual population multi-operator genetic algorithm (DPMOGA) is proposed for solving the problem. In the algorithm, the dual population structure and random-key encoding modified by starting/ending time of operations are adopted, and multiple genetic operators are self-adaptively used to obtain better encodings. In order to conduct the mapping from encodings to feasible schedules, serial and parallel scheduling generation scheme-based decoding operators, each of which adopts different justified mechanisms in two separated populations, are introduced. The superiority of the DPMOGA is verified by simulation experiments.