IDEAS home Printed from https://ideas.repec.org/r/inm/ormnsc/v69y2023i2p759-773.html

A Practical End-to-End Inventory Management Model with Deep Learning

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ozan Ozyegen & Garima Malik & Mucahit Cevik & Kevin Ioi & Karim El Mokhtari, 2026. "A unified framework for financial commentary prediction," Information Technology and Management, Springer, vol. 27(1), pages 95-111, March.
  2. Du Chen & Geoffrey A. Chua, 2026. "An Algorithmic Approach to Managing Supply Chain Data Security: The Differentially Private Newsvendor," Operations Research, INFORMS, vol. 74(2), pages 958-983, March.
  3. Bergsma, Ritsaart & de Ruijt, Corné & Bhulai, Sandjai, 2025. "A systematic review of machine learning approaches in inventory control optimization," Operations Research Perspectives, Elsevier, vol. 15(C).
  4. Schmidt, Felix G. & Pibernik, Richard, 2025. "Data-driven inventory control for large product portfolios: A practical application of prescriptive analytics," European Journal of Operational Research, Elsevier, vol. 322(1), pages 254-269.
  5. Qi Feng & J. George Shanthikumar & Jian Wu, 2025. "Contextual Data-Integrated Newsvendor Solution with Operational Data Analytics (ODA)," Management Science, INFORMS, vol. 71(11), pages 9384-9403, November.
  6. Feddersen, Leif & Cleophas, Catherine, 2026. "Hierarchical neural additive models for interpretable demand forecasts," International Journal of Forecasting, Elsevier, vol. 42(1), pages 216-234.
  7. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).
  8. Wang, Zihao & Wang, Wenlong & Liu, Tianjun & Chang, Jasmine & Shi, Jim, 2025. "IoT-driven dynamic replenishment of fresh produce in the presence of seasonal variations: A deep reinforcement learning approach using reward shaping," Omega, Elsevier, vol. 134(C).
  9. Chen, Shan & Zhu, Meizhen & Han, Shuihua & Gupta, Shivam, 2026. "A deep fusion framework for end-to-end multi-product inventory optimization in e-commerce scenarios," International Journal of Production Economics, Elsevier, vol. 291(C).
  10. Zuo-Jun Max Shen & Shuo Sun & Yongzhi Qi & Hao Hu & Ningxuan Kang & Jianshen Zhang & Xin Wang & Xiaoming Lin, 2025. "JD.com Improves Fulfillment Efficiency with Data-Driven Integrated Assortment Planning and Inventory Allocation," Interfaces, INFORMS, vol. 55(5), pages 386-398, September.
  11. Yu-Xin Tian & Chuan Zhang, 2025. "A multimodal deep reinforcement learning framework for multi-period inventory decision-making under demand uncertainty," Fuzzy Optimization and Decision Making, Springer, vol. 24(4), pages 723-750, December.
  12. Bootaki, Behrang & Zhang, Guoqing, 2024. "A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology," International Journal of Production Economics, Elsevier, vol. 275(C).
  13. Hongkang Tao & Guhong Wang & Jiansheng Liu & Zan Yang, 2024. "A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-23, April.
  14. Wang, Xinyu & Peng, Yiyang & Ma, Wei, 2026. "SPO-VCS: An end-to-end smart predict-then-optimize framework with alternating differentiation method for relocation problems in large-scale vehicle crowd sensing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 205(C).
  15. Yi Chen & Jing Dong & Zhaoran Wang & Chuheng Zhang, 2026. "A Primal-Dual Approach to Constrained Markov Decision Processes with Applications to Queue Scheduling and Inventory Management," Management Science, INFORMS, vol. 72(2), pages 955-988, February.
  16. Svoboda, Josef & Minner, Stefan, 2026. "A data-driven approach for strategic inventory placement in multi-echelon supply networks," European Journal of Operational Research, Elsevier, vol. 328(2), pages 446-459.
  17. Chenyu Huang & Zhengyang Tang & Shixi Hu & Ruoqing Jiang & Xin Zheng & Dongdong Ge & Benyou Wang & Zizhuo Wang, 2025. "ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling," Operations Research, INFORMS, vol. 73(6), pages 2986-3009, November.
  18. M. Harshvardhan & Cara Curtland & Jerry Hwang & Chuck VanDam & Adam Ghozeil & Pedro A. Neto & Frederic Marie & Chuanren Liu, 2025. "Print Demand Forecasting with Machine Learning at HP Inc," Interfaces, INFORMS, vol. 55(6), pages 469-483, November.
  19. Olivares-Nadal, Alba V., 2024. "Constructing decision rules for multiproduct newsvendors: An integrated estimation-and-optimization framework," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1021-1037.
  20. Jiaxi Liu & Shuyi Lin & Linwei Xin & Yidong Zhang, 2023. "AI vs. Human Buyers: A Study of Alibaba’s Inventory Replenishment System," Interfaces, INFORMS, vol. 53(5), pages 372-387, September.
  21. Cong Cheng & Jian Dai, 2025. "Predicting Cross-border Merger and Acquisition Completion through CEO Characteristics: A Machine Learning Approach," Management International Review, Springer, vol. 65(1), pages 43-84, February.
  22. Sadana, Utsav & Chenreddy, Abhilash & Delage, Erick & Forel, Alexandre & Frejinger, Emma & Vidal, Thibaut, 2025. "A survey of contextual optimization methods for decision-making under uncertainty," European Journal of Operational Research, Elsevier, vol. 320(2), pages 271-289.
  23. Pavithra Harsha & Ashish Jagmohan & Jayant Kalagnanam & Brian Quanz & Divya Singhvi, 2025. "Deep Policy Iteration with Integer Programming for Inventory Management," Manufacturing & Service Operations Management, INFORMS, vol. 27(2), pages 369-388, March.
  24. Yen, Benjamin P.-C. & Luo, Yu, 2023. "Navigational guidance – A deep learning approach," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1179-1191.
  25. Menglei Jia & Albert H. Schrotenboer & Feng Chen, 2025. "Scenario Predict-then-Optimize for Data-Driven Online Inventory Routing," Transportation Science, INFORMS, vol. 59(5), pages 1032-1056, September.
  26. Guo, Yuhang & Su, Zicheng & Yang, Hai & Liang, Enming & Zhong, Chen & Ma, Wanjing, 2026. "A smart predict-then-optimize framework for vehicle rebalancing problem," Transportation Research Part B: Methodological, Elsevier, vol. 206(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.