TY  - JOUR
AU  - Wang, Renfang
AU  - Gong, Yufei
AU  - Su, Peng
AU  - Hu, Linmin
AU  - Jiang, Xin
PY  - 2025
DA  - 2025/11/11
TI  - Optimization and Control of Discrete-Time Production-Inventory Systems Using Reinforcement Learning
JO  - ICCK Transactions on Systems Safety and Reliability
T2  - ICCK Transactions on Systems Safety and Reliability
JF  - ICCK Transactions on Systems Safety and Reliability
VL  - 1
IS  - 2
SP  - 98
EP  - 113
DO  - 10.62762/TSSR.2025.621059
UR  - https://www.icck.org/article/abs/TSSR.2025.621059
KW  - reinforcement learning
KW  - economic manufacturing quantity
KW  - production inventory optimization
KW  - Q-Learning
KW  - dynamic decision-making
AB  - This study introduces a novel approach for enhancing production decision-making by applying Reinforcement Learning to optimize the Economic Manufacturing Quantity (EMQ) model within discrete-time production-inventory systems. By incorporating machine status, inventory levels, and production choices, a Markov Decision Process (MDP) is constructed and combined with the Q-learning algorithm to derive an adaptive control method. This method enables the dynamic adaptation of production decisions, by effectively balancing the normal operation and shutdown for rest states. Numerical simulations show that the suggested Reinforcement Learning model surpasses conventional EMQ models and steady-state probability models in both convergence speed and cost-effectiveness. This study offers a data-driven approach for optimizing production processes in smart manufacturing settings. It also supports the evolution of production-inventory systems from static planning to dynamic intelligent decision-making.
SN  - 3069-1087
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Wang2025Optimizati,
  author = {Renfang Wang and Yufei Gong and Peng Su and Linmin Hu and Xin Jiang},
  title = {Optimization and Control of Discrete-Time Production-Inventory Systems Using Reinforcement Learning},
  journal = {ICCK Transactions on Systems Safety and Reliability},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {98-113},
  doi = {10.62762/TSSR.2025.621059},
  url = {https://www.icck.org/article/abs/TSSR.2025.621059},
  abstract = {This study introduces a novel approach for enhancing production decision-making by applying Reinforcement Learning to optimize the Economic Manufacturing Quantity (EMQ) model within discrete-time production-inventory systems. By incorporating machine status, inventory levels, and production choices, a Markov Decision Process (MDP) is constructed and combined with the Q-learning algorithm to derive an adaptive control method. This method enables the dynamic adaptation of production decisions, by effectively balancing the normal operation and shutdown for rest states. Numerical simulations show that the suggested Reinforcement Learning model surpasses conventional EMQ models and steady-state probability models in both convergence speed and cost-effectiveness. This study offers a data-driven approach for optimizing production processes in smart manufacturing settings. It also supports the evolution of production-inventory systems from static planning to dynamic intelligent decision-making.},
  keywords = {reinforcement learning, economic manufacturing quantity, production inventory optimization, Q-Learning, dynamic decision-making},
  issn = {3069-1087},
  publisher = {Institute of Central Computation and Knowledge}
}