A simulation and optimization approach for planning the production of engine parts using ACF-GA

To solve the problems of serious backlog of work-in-process (WIP) and high production cost in the manufacturing workshop of core components of an aerospace engine, this paper proposes a two-stage simulation optimization method based on Allocated Clearing Function-Genetic Algorithm (ACF-GA), aiming to optimize the production cost in the processing and manufacturing process. Firstly, a discrete event simulation model is established according to the existing data of the factory, and the model is warmed up based on the historical input data. In this process, the input-output loads of each work center are collected in a unit period, and the Clearing Function (CF) curve is fitted by piecewise linear fitting. Then, the ACF model is established to solve the initial production plan to minimize the production cost. In the second stage, it is iteratively optimized by combining the GA algorithm with simulation. The final result reduces the cost by about 40.45% compared with the initial production plan and has significant advantages over other solutions in reducing the on-line time of parts and improving the production stability of the production line, which verifies the effectiveness of the optimization method.