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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.15872 |
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| _version_ | 1866913213866573824 |
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| author | Cheng, Liqiang Luo, Jun Fan, Weiwei Zhang, Yidong Li, Yuan |
| author_facet | Cheng, Liqiang Luo, Jun Fan, Weiwei Zhang, Yidong Li, Yuan |
| contents | This paper addresses a multi-echelon inventory management problem with a complex network topology where deriving optimal ordering decisions is difficult. Deep reinforcement learning (DRL) has recently shown potential in solving such problems, while designing the neural networks in DRL remains a challenge. In order to address this, a DRL model is developed whose Q-network is based on radial basis functions. The approach can be more easily constructed compared to classic DRL models based on neural networks, thus alleviating the computational burden of hyperparameter tuning. Through a series of simulation experiments, the superior performance of this approach is demonstrated compared to the simple base-stock policy, producing a better policy in the multi-echelon system and competitive performance in the serial system where the base-stock policy is optimal. In addition, the approach outperforms current DRL approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon Inventory Management Cheng, Liqiang Luo, Jun Fan, Weiwei Zhang, Yidong Li, Yuan Machine Learning Artificial Intelligence Optimization and Control This paper addresses a multi-echelon inventory management problem with a complex network topology where deriving optimal ordering decisions is difficult. Deep reinforcement learning (DRL) has recently shown potential in solving such problems, while designing the neural networks in DRL remains a challenge. In order to address this, a DRL model is developed whose Q-network is based on radial basis functions. The approach can be more easily constructed compared to classic DRL models based on neural networks, thus alleviating the computational burden of hyperparameter tuning. Through a series of simulation experiments, the superior performance of this approach is demonstrated compared to the simple base-stock policy, producing a better policy in the multi-echelon system and competitive performance in the serial system where the base-stock policy is optimal. In addition, the approach outperforms current DRL approaches. |
| title | A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon Inventory Management |
| topic | Machine Learning Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2401.15872 |