To effectively describe the influence of factors such as the assembly personnel level and the workpiece quality on the job quality of aircraft assembly and establish a reasonable proactive schedule for the assembly process

a support vector regression (SVR) prediction model and a two-level iterative search algorithm are developed. Firstly

with collecting relevant historical quality data

a SVR prediction model is trained by taking the data of assembly personnel level

workpiece quality and so on as input and job quality as output. On the basis of the trained SVR prediction model

a job list-based tabu search framework is adopted to search the neighborhood of job list

and the optimization of personnel allocation is achieved through the serial scheduling generation scheme with embedded personnel assignment search module. The results of numerical experiments show that the predicted value of job quality obtained by the SVR prediction model can be controlled within 5% in comparison with the measured value

and the highest prediction accuracy is 97.38%. The mean deviation between the two-level iterative search algorithm and CPLEX is between 9.99% and 27.54%

which is the smallest among proactive scheduling generation methods. In the uncertain environment

the right shift algorithm can obtain the optimal or sub-optimal mean makespan and mean deviation in the baseline schedules obtained by the two-level iterative search algorithm. The SVR prediction model can effectively predict the job quality of aircraft assembly

and the two-level iterative search algorithm can meet the requirement of constructing proactive scheduling for aircraft assembly.