Deep Reinforcement Learning Approach for Capacitated Supply Chain optimization under Demand Uncertainty

With the global trade competition becoming further intensified, Supply Chain Management (SCM) technology has become critical to maintain competitive advantages for enterprises. However, the economic integration and increased market uncertainty have brought great challenges to SCM. In this paper, two...

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Bibliographic Details
Main Authors: Peng, Zedong, Zhang, Yi, Feng, Yiping, Zhang, Tuchao, Wu, Zhengguang, Su, Hongye
Format: Conference Proceeding
Language:English
Subjects:
deep reinforcement learning
demand uncertainty
Dynamic scheduling
Machine learning
Mathematical model
Optimization
supply chain optimization
Supply chains
Uncertainty
vanilla policy gradient
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Summary:With the global trade competition becoming further intensified, Supply Chain Management (SCM) technology has become critical to maintain competitive advantages for enterprises. However, the economic integration and increased market uncertainty have brought great challenges to SCM. In this paper, two Deep Reinforcement Learning (DRL) based methods are proposed to solve multi-period capacitated supply chain optimization problem under demand uncertainty. The capacity constraints are satisfied from both modelling perspective and DRL algorithm perspective. Both continuous action space and discrete action space are considered. The performance of the methods is analyzed through the simulation of three different cases. Compared to the baseline of (r, Q) policy, the proposed methods show promising results for the supply chain optimization problem.
ISSN:2688-0938
DOI:10.1109/CAC48633.2019.8997498