IP Library › Granted Patent US 12,718,190
Granted Patent B2
US 12,718,190 · App. 18/105,593 · Granted Aug 25, 2026

Bound enhanced reinforcement learning system for distribution supply chain management

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Quick Facts
Patent No.
US 12,718,190
App. No.
18/105,593
Granted
Aug 25, 2026
Kind
B2
Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: WANG, HAIYAN; KIUCHI, ATSUKI; TANG, HSIU-KHUERN; GUPTA, CHETAN; EL-SHAR, IBRAHIM; SUN, WENHUAN
To: HITACHI, LTD.
Reel/Frame 062589/0268 →
Continuity (1)
Related Publication 20240265339A1 · Aug 8, 2024
References Cited (37)
US 9239989B2 · Bouqata et al. · 2016 [cited by applicant]
US 20180046974A1 · Helander · 2018 [cited by examiner]
US 20190073611A1 · Sahota · 2019 [cited by examiner]
US 20220180026A1 · Barat · 2022 [cited by examiner]
US 20230245156A1 · Cella · 2023 [cited by examiner]
AU 2003301919B2 · 2008 [cited by applicant]
WO 2001009803A1 · 2001 [cited by applicant]
WO 2004044734A3 · 2024 [cited by applicant]
J. J. Rao, K. K. Ravulapati, and T. K. Das, “A Simulation-Based Approach to Study Stochastic Inventory-Planning Games,” International Journal of Systems Science, vol. 34, No. 12-13, pp. 717-730, 2003, doi: 10.1080/00207… [cited by applicant]
S. K. Chaharsooghi, J. Heydari, and S. H. Zegordi, “A reinforcement learning model for supply chain ordering management: An application to the beer game,” Decision Support Systems, vol. 45, No. 4, pp. 949-959, Nov. 2008… [cited by applicant]
K. K. Ravulapati, J. Rao, and T. K. Das, “A reinforcement learning approach to stochastic business games,” IIE Transactions (Institute of Industrial Engineers), vol. 36, No. 4, pp. 373-385, Apr. 2004, doi: 10.1080/07408… [cited by applicant]
Z. Sui, A. Gosavi, and L. Lin, “A Reinforcement Learning Approach for Inventory Replenishment in Vendor-Managed Inventory Systems With Consignment Inventory,” 2010, 10 pages. [cited by applicant]
T. van Tongeren, U. Kaymak, D. Naso, and E. van Asperen, “Q-Learning in a Competitive Supply Chain,” 2007, 6 ages. [cited by applicant]
S. Yang and J. Zhang, “Adaptive inventory control and bullwhip effect analysis for supply chains with non-stationary demand,” in Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015, Jul. 2015… [cited by applicant]
T. Stockheim, M. Schwind, and W. Koenig, “A Reinforcement Learning Approach for Supply Chain Management,” 2003. [Online]. Available: http://www.wi-frankfurt.de, 15 pages. [cited by applicant]
A. Mortazavi, A. Arshadi Khamseh, and P. Azimi, “Designing of an intelligent self-adaptive model for supply chain ordering management system,” Engineering Applications of Artificial Intelligence, vol. 37, pp. 207-220, J… [cited by applicant]
D. P. A., Carlos, R. M. T., Jairo, and F. A., Aldo, “Simulation-optimization using a reinforcement learning approach,” in Proceedings—Winter Simulation Conference, 2008, pp. 1376-1383. doi: 10.1109/WSC.2008.4736213. [cited by applicant]
I. Giannoccaro and P. Pontrandolfo, “Inventory management in supply chains: a reinforcement learning approach,” Int. J. Production Economics , 2002, 153-161. [cited by applicant]
Pontrandolfo et al., “Global Supply Chain Management: A Reinforcement Learning Approach”, 2002, 28 pages. [cited by applicant]
I. H. Kwon, C. O. Kim, J. Jun, and J. H. Lee, “Case-based myopic reinforcement learning for satisfying target service level in supply chain,” Expert Systems with Applications, vol. 35, No. 1-2, pp. 389-397, Jul. 2008, d… [cited by applicant]
Gijsbrechts et al., “Can Deep Reinforcement Learning Improve Inventory Management? Performance on Dual Sourcing, Lost Sales and Multi-Echelon Problems”, 2019, 27 pages. [cited by applicant]
C. O. Kim, J. Jun, J. K. Baek, R. L. Smith, and Y. D. Kim, “Adaptive inventory control models for supply chain management,” International Journal of Advanced Manufacturing Technology, vol. 26, No. 9-10, pp. 1184-1192, O… [cited by applicant]
C. O. Kim, I. H. Kwon, and J. G. Baek, “Asynchronous action-reward learning for nonstationary serial supply chain inventory control,” Applied Intelligence, vol. 28, No. 1, pp. 1-16, 2008, doi: 10.1007/s10489-007-0038-2. [cited by applicant]
C. O. Kim, I. H. Kwon, and C. Kwak, “Multi-agent based distributed inventory control model,” Expert Systems with Applications, vol. 37, No. 7, pp. 5186-5191, Jul. 2010, doi: 10.1016/j.eswa.2009.12.073. [cited by applicant]
J. Xu, J. Zhang, and Y. Liu, “An adaptive inventory control for a supply chain,” in 2009 Chinese Control and Decision Conference, CCDC 2009, 2009, pp. 5714-5719. doi: 10.1109/CCDC.2009.5195218, 6 pages. [cited by applicant]
C. Kwak, J. S. Choi, C. O. Kim, and I. H. Kwon, “Situation reactive approach to Vendor Managed Inventory problem,” Expert Systems with Applications, vol. 36, No. 5, pp. 9039-9045, Jul. 2009, doi: 10.1016/j.eswa.2008.12.… [cited by applicant]
L. Kemmer and J. Read, “Reinforcement learning for supply chain optimization,” European Workshop on Reinforcement Learning 14 (2018), 9 pages. [cited by applicant]
J. Li, P. Guo, and Z. Zuo, “Inventory control model for mobile supply chain management,” in Proceedings—The 2008 International Conference on Embedded Software and Systems Symposia, ICESS Symposia, 2008, pp. 459-463. doi… [cited by applicant]
M. H. F. Zarandi, S. V. Moosavi, and M. Zarinbal, “A fuzzy reinforcement learning algorithm for inventory control in supply chains,” International Journal of Advanced Manufacturing Technology, vol. 65, No. 1-4, pp. 557-… [cited by applicant]
Van Roy, B., Bertsekas, D. P., Lee, Y., & Tsitsiklis, J. N. (Dec. 1997), “A Neuro-Dynamic Programming Approach to Retailer Inventory Management”, Proceedings of the 36th IEEE Conference on Decision and Control (vol. 4, … [cited by applicant]
A. Oroojlooyjadid, M. Nazari, L. v. Snyder, and M. Takáč, “A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization,” Manufacturing & Service Operations Management, Feb. 2021, doi: 10.1… [cited by applicant]
R. Wang, X. Gan, Q. Li, and X. Yan, “Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/6643131, 17 pages. [cited by applicant]
H. Meisheri et al., “Scalable multi-product inventory control with lead time constraints using reinforcement learning,” Neural Computing and Applications, 2021, doi: 10.1007/s00521-021-06129-w, 23 pages. [cited by applicant]
Z. Peng, Y. Zhang, Y. Feng, T. Zhang, Z. Wu, and H. Su, “Deep Reinforcement Learning Approach for Capacitated Supply Chain Optimization under Demand Uncertainty,” 2019, 6 pages. [cited by applicant]
H. D. Perez, C. D. Hubbs, C. Li, and I. E. Grossmann, “Algorithmic approaches to inventory management optimization,” Processes, vol. 9, No. 1, pp. 1-17, Jan. 2021, doi: 10.3390/pr9010102. [cited by applicant]
Z. Kegenbekov and I. Jackson, “Adaptive supply chain: Demand-supply synchronization using deep reinforcement learning,” Algorithms, vol. 14, No. 8, Aug. 2021, doi: 10.3390/a14080240, 14 pages. [cited by applicant]
C. D. Hubbs, H. D. Perez, O. Sarwar, I. E. Grossmann, H. M. Stewart, and J. M. Wassick, “OR-Gym: A Reinforcement Learning Library for Operations Research Problems”, Journal on Computing, 2022, [Online]. Available: https… [cited by applicant]