|
|
|
| Dynamic Evolutionary Analysis of Deep Reinforcement Learning Inventory Decision Results |
| LI Zhuoqun1, WANG Shuyi1, CAI Zicheng2
|
1. School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China;
2. Jiangxi Institute of Fashion Technology, Nanchang 330201, China |
|
|
|
|
Abstract In order to explore the impact of intelligent inventory decisions trained by deep reinforcement learning algorithms on the dynamic evolution of supply chain systems, this paper considers the perspective of real-world decision makers and utilizes system dynamics modeling to reproduce the logical structure of a four-order supply chain model constructed using deep reinforcement learning. The decision results are visualized to assess their impact on the system. The experiments illustrate that the algorithm can make better ordering decisions based on the setting of its objective function, but it fails to achieve the lowest cost for the members who apply the algorithm synchronously. The evolutionary process reveals that the Sterman strategy has the role of maintaining the stability of the system during dynamic evolution; establishing a reasonable number of iterations helps to obtain a lower total supply chain cost.
|
|
Received: 31 October 2023
Published: 09 October 2025
|
|
|
|
|
|
|
|
|
