A reactive scheduling method for disturbances in aircraft moving assembly line

Aircraft assembly requires a large number of materials from hundreds of suppliers, and the uncertainty in material delivery has a negative impact on the assembly schedule. Existing researches stop short of introducing how to reschedule assembly activities in this context, so this paper addresses a reactive scheduling problem of aircraft moving assembly line with uncertain material delivery, and a bi-objective model is established. To absorb the advantage of machine learning-based method, we present a SVDD-based reactive scheduling method (SVDD-RS). Firstly, the models under different settings of disturbances in material delivery are solved, and the obtained policies are used to train the SVDD classification model in the offline training phase. In the online reactive scheduling phase, the trained SVDD classification model is used to make a preliminary decision for unstarted activities, and exact start-times are further determined by the local forward-looking algorithm. Computational experiments are carried out over practical cases generated from an aircraft assembly line to evaluate the performance of SVDD-RS. The results show that the SVDD classification model can quickly select policies with reasonable accuracy, and SVDD-RS can guarantee a quick response to the disturbance and produce a high-quality solution, compared to other existing reactive scheduling methods.