Elite Gene Transfer Learning Heuristic Algorithm in Scheduling Aircraft Assembly

Aircraft assembly scheduling (AAS) with multiconstraints, multitype workers, and the orders of tasks has become a key research focus in advanced industrial manufacturing. An elite gene retention metaheuristic algorithm (EGHA) is proposed in this article as a transfer knowledge generator, and the elite gene transfer learning heuristic algorithm (TL-EGHA) is also utilized as an optimized framework to tackle these issues for the AAS problem. Fully considered characteristics of the AAS problem, double time windows are proposed to deal with the constraints of worker type and space restrictions, and this guarantees that the proposed algorithm can obtain the solution quickly. The transfer learning strategy imports transfer knowledge associated with features and previous experiences, which prompts the initialization results closer to the task goals and supports dynamic adjustments of the parameters in TL-EGHA to enhance the global searching capability significantly. TL-EGHA has been a verified advancement in scheduling four real-world aircraft assembly lines by a comparative study with some existing scheduling algorithms, including well-known genetic transfer learning.