{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/applying-deep-learning-to-the-newsvendor","title":"Applying Deep Learning to the Newsvendor Problem","arxiv_id":"1607.02177","date":"2016-07-07","proceeding":null,"authors":["Afshin Oroojlooyjadid","Lawrence Snyder","Martin Takáč"],"abstract":"The newsvendor problem is one of the most basic and widely applied inventory\nmodels. There are numerous extensions of this problem. If the probability\ndistribution of the demand is known, the problem can be solved analytically.\n  However, approximating the probability distribution is not easy and is prone\nto error; therefore, the resulting solution to the newsvendor problem may be\nnot optimal. To address this issue, we propose an algorithm based on deep\nlearning that optimizes the order quantities for all products based on features\nof the demand data. Our algorithm integrates the forecasting and\ninventory-optimization steps, rather than solving them separately, as is\ntypically done, and does not require knowledge of the probability distributions\nof the demand. Numerical experiments on real-world data suggest that our\nalgorithm outperforms other approaches, including data-driven and machine\nlearning approaches, especially for demands with high volatility. Finally, in\norder to show how this approach can be used for other inventory optimization\nproblems, we provide an extension for (r,Q) policies.","url_abs":"http://arxiv.org/abs/1607.02177v4","url_pdf":"http://arxiv.org/pdf/1607.02177v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"applying-deep-learning-to-the-newsvendor","repo_url":"https://github.com/oroojlooy/newsvendor","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"applying-deep-learning-to-the-newsvendor","repo_url":"https://github.com/karnwatcharasupat/DeepNewsvendor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1607.02177","atlas_url":"https://app.syntology.ai/?focus=1607.02177","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}