Diversity Guided Production Inventory Control in Automobile Manufacturers
- Resource Type
- Conference
- Authors
- Tao, Lue; Chen, Weihua; Wang, Gongshu; Su, Lijie; Yang, Yang; Dong, Yun
- Source
- 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Automation Science and Engineering (CASE), 2022 IEEE 18th International Conference on. :148-153 Aug, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Costs
Statistical analysis
Simulation
Conferences
Production
Reinforcement learning
Inventory control
- Language
- ISSN
- 2161-8089
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.