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| Formato: | Recurso digital |
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Zenodo
2025
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| Acceso en línea: | https://doi.org/10.5281/zenodo.17812792 |
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| _version_ | 1866901677389381632 |
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| author | Pramanik, Rudra pramanik, sonjita |
| author_facet | Pramanik, Rudra pramanik, sonjita |
| contents | <p>Supply chain management faces increasing com-<br>plexity due to demand uncertainty, lead time variability, and<br>operational costs. This paper presents a deep reinforcement learn-<br>ing (RL) framework using Q-learning for optimizing inventory<br>management decisions in supply chain systems. We develop an<br>intelligent agent that learns optimal ordering policies by bal-<br>ancing holding costs, stockout penalties, and ordering expenses.<br>Using real-world retail transaction data, our experimental results<br>demonstrate that the RL-based approach achieves 23.4% cost<br>reduction compared to traditional constant ordering policies<br>and 15.8% improvement over threshold-based heuristics. The<br>proposed system achieves a 94.2% service level while maintaining<br>average inventory levels 18% lower than baseline methods.<br>Our findings suggest that RL-based approaches offer significant<br>potential for autonomous supply chain optimization, particularly<br>in dynamic demand environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17812792 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Deep Reinforcement Learning for Intelligent Supply Chain Inventory Management: A Q-Learning Approach Pramanik, Rudra pramanik, sonjita <p>Supply chain management faces increasing com-<br>plexity due to demand uncertainty, lead time variability, and<br>operational costs. This paper presents a deep reinforcement learn-<br>ing (RL) framework using Q-learning for optimizing inventory<br>management decisions in supply chain systems. We develop an<br>intelligent agent that learns optimal ordering policies by bal-<br>ancing holding costs, stockout penalties, and ordering expenses.<br>Using real-world retail transaction data, our experimental results<br>demonstrate that the RL-based approach achieves 23.4% cost<br>reduction compared to traditional constant ordering policies<br>and 15.8% improvement over threshold-based heuristics. The<br>proposed system achieves a 94.2% service level while maintaining<br>average inventory levels 18% lower than baseline methods.<br>Our findings suggest that RL-based approaches offer significant<br>potential for autonomous supply chain optimization, particularly<br>in dynamic demand environments.</p> |
| title | Deep Reinforcement Learning for Intelligent Supply Chain Inventory Management: A Q-Learning Approach |
| url | https://doi.org/10.5281/zenodo.17812792 |