Deep Neural Newsvendor

References

• Adcock B, Dexter N (2021) The gap between theory and practice in function approximation with deep neural networks. SIAM J. Math Data Sci. 3(2):624–655.Crossref, Google Scholar
• Anthony M, Bartlett P (1999) Neural Network Learning: Theoretical Foundations (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Aouad A, Désir A (2022) Representing random utility choice models with neural networks. Preprint, submitted July 2022, https://arxiv.org/abs/2207.12877.Google Scholar
• Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
• Ban GY, Gao Z, Taigel F (2020) Model mis-specification in newsvendor decisions: A comparison of frequentist parametric, Bayesian parametric and nonparametric approaches. Preprint, submitted June 2020, https://doi.org/10.2139/ssrn.3495733.Google Scholar
• Bartlett PL, Harvey N, Liaw C, Mehrabian A (2019) Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks. J. Machine Learn. Res. 20(1):2285–2301.Google Scholar
• Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
• Bertsimas D, Koduri N (2022) Data-driven optimization: A reproducing kernel Hilbert space approach. Oper. Res. 70(1):454–471.Link, Google Scholar
• Chan TC, Lin B, Saxe S (2025) Machine learning-augmented optimization of large bilevel and two-stage stochastic programs: Application to cycling network design Manufacturing Service Oper. Management Forthcoming.Google Scholar
• Chen N, Lagzi S, Milner J (2022) Using neural networks to guide data-driven operational decisions. Preprint, submitted September 2022, https://doi.org/10.2139/ssrn.4217092.Google Scholar
• Farrell MH, Liang T, Misra S (2021) Deep neural networks for estimation and inference. Econometrica 89(1):181–213.Crossref, Google Scholar
• Feng Q, Shanthikumar JG (2018) How research in production and operations management may evolve in the era of big data. Production Oper. Management 27(9):1670–1684.Crossref, Google Scholar
• Feng Q, Shanthikumar JG (2022) Developing operations management data analytics. Production Oper. Management 31(12):4544–4557.Crossref, Google Scholar
• Gabel S, Timoshenko A (2022) Product choice with large assortments: A scalable deep-learning model. Management Sci. 68(3):1808–1827.Link, Google Scholar
• Gijsbrechts J, Boute RN, Van Mieghem JA, Zhang DJ (2022) Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems. Manufacturing Service Oper. Management 24(3):1349–1368.Link, Google Scholar
• Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Proc. 13th Internat. Conf. Artificial Intelligence Statis. (PMLR, New York), 249–256.Google Scholar
• He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proc. IEEE Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 1026–1034.Google Scholar
• Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, Senior A, et al. (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29(6):82–97.Crossref, Google Scholar
• Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Networks 4(2):251–257.Crossref, Google Scholar
• Hu Y, Kallus N, Mao X (2022) Fast rates for contextual linear optimization. Management Sci. 68(6):4236–4245.Link, Google Scholar
• Jiao Y, Shen G, Lin Y, Huang J (2023) Deep nonparametric regression on approximately low-dimensional manifolds. Ann. Statist. 51(2):691–716.Crossref, Google Scholar
• Kallus N, Mao X (2023) Stochastic optimization forests. Management Sci. 69(4):1975–1994.Link, Google Scholar
• Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Preprint, submitted December 2014, https://arxiv.org/abs/1412.6980.Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 25.Google Scholar
• Kuznetsov V, Mohri M (2015) Learning theory and algorithms for forecasting non-stationary time series. Adv. Neural Inform. Processing Systems 28:541–549.Google Scholar
• Levi R, Perakis G, Uichanco J (2015) The data-driven newsvendor problem: New bounds and insights. Oper. Res. 63(6):1294–1306.Link, Google Scholar
• Levi R, Roundy RO, Shmoys DB (2007) Provably near-optimal sampling-based policies for stochastic inventory control models. Math. Oper. Res. 32(4):821–839.Link, Google Scholar
• Lin M, Huh WT, Krishnan H, Uichanco J (2022) Data-driven newsvendor problem: Performance of the sample average approximation. Oper. Res. 70(4):1996–2012.Link, Google Scholar
• Liyanage LH, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.Crossref, Google Scholar
• Miao S, Chen X, Chao X, Liu J, Zhang Y (2022) Context-based dynamic pricing with online clustering. Production Oper. Management 31(9):3559–3575.Crossref, Google Scholar
• Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of Machine Learning (MIT Press, Cambridge, MA).Google Scholar
• Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. Proc. 27th Internat. Conf. Machine Learn. (ICML, WI), 807–814. Google Scholar
• Oroojlooyjadid A, Snyder LV, Takáč M (2020) Applying deep learning to the newsvendor problem. IISE Trans. 52(4):444–463.Crossref, Google Scholar
• Oroojlooyjadid A, Nazari M, Snyder LV, Takáč M (2022) A deep Q-network for the beer game: Deep reinforcement learning for inventory optimization. Manufacturing Service Oper. Management 24(1):285–304.Link, Google Scholar
• Padilla OHM, Tansey W, Chen Y (2022) Quantile regression with ReLU networks: Estimators and minimax rates. J. Machine Learn. Res. 23(247):1–42.Google Scholar
• Perakis G, Tsiourvas A (2022) Optimizing objective functions from ReLU neural networks in revenue management applications. Working paper, MIT, Cambridge, MA.Google Scholar
• Qi M, Cao Y, Shen ZJ (2022) Distributionally robust conditional quantile prediction with fixed design. Management Sci. 68(3):1639–1658.Link, Google Scholar
• Qi M, Shi Y, Qi Y, Ma C, Yuan R, Wu D, Shen ZJ (2023) A practical end-to-end inventory management model with deep learning. Management Sci. 69(2):759–773.Link, Google Scholar
• Scarlett J, Cevher V (2019) An introductory guide to Fano’s inequality with applications in statistical estimation. Preprint, submitted November 2019, https://arxiv.org/abs/1901.00555.Google Scholar
• Schmidt-Hieber AJ (2020) Nonparametric regression using deep neural networks with ReLU activation function. Ann. Statist. 48(4):1875–1897.Google Scholar
• Shapiro A, Dentcheva D, Ruszczynski A (2021) Lectures on Stochastic Programming: Modeling and Theory, vol. 9 (SIAM, Philadelphia).Crossref, Google Scholar
• Shen G, Jiao Y, Lin Y, Horowitz JL, Huang J (2021) Deep quantile regression: Mitigating the curse of dimensionality through composition. Preprint, submitted August 2021, https://arxiv.org/abs/2107.04907.Google Scholar
• Shi C, Chen W, Duenyas I (2016) Nonparametric data-driven algorithms for multiproduct inventory systems with censored demand. Oper. Res. 64(2):362–370.Link, Google Scholar
• Siegel AF, Wagner MR (2021) Profit estimation error in the newsvendor model under a parametric demand distribution. Management Sci. 67(8):4863–4879.Link, Google Scholar
• Siegel AF, Wagner MR (2023) Data-driven profit estimation error in the newsvendor model. Oper. Res. 71(6):2146–2157.Link, Google Scholar
• Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489.Crossref, Google Scholar
• Simchi-Levi D, Xu Y (2022) Bypassing the monster: A faster and simpler optimal algorithm for contextual bandits under realizability. Math. Oper. Res. 47(3):1904–1931.Link, Google Scholar
• Simchi-Levi D, Xu Y (2023) Phase transitions in bandits with switching constraints. Management Sci. 69(12):7182–7201.Link, Google Scholar
• Ye Z, Zhang Z, Zhang D, Zhang H, Zhang RP (2025) Deep learning based causal inference for large-scale combinatorial experiments: Theory and empirical evidence. Management Sci. Forthcoming.Google Scholar
• Zhang H, Chao X, Shi C (2018) Perishable inventory systems: Convexity results for base-stock policies and learning algorithms under censored demand. Oper. Res. 66(5):1276–1286.Link, Google Scholar
• Zhang L, Yang J, Gao R (2024) Optimal robust policy for feature-based newsvendor. Management Sci. 70(4):2315–2329.Link, Google Scholar