Abstract
In this study, an online reinforcement learning-based approach and a reinforcement learning with prior knowledge approach are proposed to enhance decision intelligence in inventory management systems for handling nonstationary stochastic market demands in e-commerce environment with crowdsourcing resources. The proposed inventory control policies are designed to solve a multi-period inventory problem with the objectives of optimising inventory-related costs and service levels in the absence of prior information on demand patterns. An experimental analysis reveals that the proposed reinforcement learning-based inventory control policies achieve cost savings and higher service levels across various settings of cost ratios and lead times.
Original language | English |
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Article number | 2284427 |
Journal | Enterprise Information Systems |
Volume | 18 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Feb 2024 |
Keywords
- inventory policy
- nonstationary demand
- online optimal control
- Reinforcement learning
- supply chain
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems and Management