Applying deep learning to the newsvendor problem
Author
Abstract
Suggested Citation
DOI: 10.1080/24725854.2019.1632502
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Serrano, Breno & Minner, Stefan & Schiffer, Maximilian & Vidal, Thibaut, 2024. "Bilevel optimization for feature selection in the data-driven newsvendor problem," European Journal of Operational Research, Elsevier, vol. 315(2), pages 703-714.
- Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
- Hongzhe Zhang & Xiaohang Zhao & Xiao Fang & Bintong Chen, 2024. "Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model," Information Systems Research, INFORMS, vol. 35(2), pages 528-550, June.
- Lin An & Andrew A. Li & Benjamin Moseley & R. Ravi, 2023. "The Nonstationary Newsvendor with (and without) Predictions," Papers 2305.07993, arXiv.org, revised Jul 2024.
- van der Haar, Joost F. & Wellens, Arnoud P. & Boute, Robert N. & Basten, Rob J.I., 2024. "Supervised learning for integrated forecasting and inventory control," European Journal of Operational Research, Elsevier, vol. 319(2), pages 573-586.
- Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
- Yue Tan & Liyi Gu & Senyu Xu & Mingchao Li, 2024. "Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach," Mathematics, MDPI, vol. 12(4), pages 1-30, February.
- Thais de Castro Moraes & Xue‐Ming Yuan & Ek Peng Chew, 2024. "Hybrid convolutional long short‐term memory models for sales forecasting in retail," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1278-1293, August.
- Pirayesh Neghab, Davood & Khayyati, Siamak & Karaesmen, Fikri, 2022. "An integrated data-driven method using deep learning for a newsvendor problem with unobservable features," European Journal of Operational Research, Elsevier, vol. 302(2), pages 482-496.
- Reza Bradrania & Davood Pirayesh Neghab, 2022. "State-dependent Asset Allocation Using Neural Networks," Papers 2211.00871, arXiv.org.
- Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2023. "Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 158-177, January.
- Suhan Wu & Min Luo & Jingxia Zhang & Daoheng Zhang & Lianmin Zhang, 2022. "Pharmaceutical Supply Chain in China: Pricing and Production Decisions with Price-Sensitive and Uncertain Demand," Sustainability, MDPI, vol. 14(13), pages 1-28, June.
- van der Laan, Niels & Teunter, Ruud H. & Romeijnders, Ward & Kilic, Onur A., 2022. "The data-driven newsvendor problem: Achieving on-target service-levels using distributionally robust chance-constrained optimization," International Journal of Production Economics, Elsevier, vol. 249(C).
- Erkip, Nesim Kohen, 2023. "Can accessing much data reshape the theory? Inventory theory under the challenge of data-driven systems," European Journal of Operational Research, Elsevier, vol. 308(3), pages 949-959.
- Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
- Bradrania, Reza & Pirayesh Neghab, Davood, 2021. "State-dependent asset allocation using neural networks," MPRA Paper 115254, University Library of Munich, Germany.
- Meng Qi & Yuanyuan Shi & Yongzhi Qi & Chenxin Ma & Rong Yuan & Di Wu & Zuo-Jun (Max) Shen, 2023. "A Practical End-to-End Inventory Management Model with Deep Learning," Management Science, INFORMS, vol. 69(2), pages 759-773, February.
- Meng Qi & Ho‐Yin Mak & Zuo‐Jun Max Shen, 2020. "Data‐driven research in retail operations—A review," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 595-616, December.
- Joaquin Gonzalez & Liliana Avelar Sosa & Gabriel Bravo & Oliverio Cruz-Mejia & Jose-Manuel Mejia-Muñoz, 2024. "Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit," Logistics, MDPI, vol. 8(2), pages 1-14, June.
- Bingnan Yang & Xianhao Xu & Yeming Gong & Yacine Rekik, 2024. "Data-driven optimization models for inventory and financing decisions in online retailing platforms," Annals of Operations Research, Springer, vol. 339(1), pages 741-764, August.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:uiiexx:v:52:y:2020:i:4:p:444-463. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.