Diversity Guided Production Inventory Control in Automobile Manufacturers
Lue Tao, Weihua Chen, Gongshu Wang, Li-Jie Su, Yang Yang, Yun Dong · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · 2022
In this research, the production inventory control problem in automobile manufacturers is investigated to keep the inventory at the ideal level and minimize the production cost. Firstly, we establish a linear-quadratic tracking (LQT) model for three serial production workshops. The reinforcement learning (RL) algorithm is employed to give a control policy of the problem with unknown parameters. Furthermore, an improved multi-objective differential evolution (MODE) algorithm is proposed to adjust the weight matrix and hyperparameters of RL so that the diversity of policies on conflicting operational indicators can be enhanced. Simulation results show that the proposed algorithm achieves better performance on both production inventory control and parameter optimization.