From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms

References

• Ban G-Y, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
• Batini C, Cappiello C, Francalanci C, Maurino A (2009) Methodologies for data quality assessment and improvement. ACM Comput. Surveys 41(3):1–52.Crossref, 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
• Bertsimas D, McCord C (2019) From predictions to prescriptions in multistage optimization problems. Preprint, submitted April 26, https://arxiv.org/abs/1904.11637.Google Scholar
• Besbes O, Mouchtaki O (2023) How big should your data really be? Data-driven newsvendor: Learning one sample at a time. Management Sci. 69(10):5848–5865.Link, Google Scholar
• Besbes O, Ma W, Mouchtaki O (2025) Beyond IID: Data-driven decision making in heterogeneous environments. Management Sci. 71(12):10538–10555.Link, Google Scholar
• Bhattacharya PK, Gangopadhyay AK (1990) Kernel and nearest-neighbor estimation of a conditional quantile. Ann. Statist. 18(3):1400–1415.Crossref, Google Scholar
• Bilodeau B, Negrea J, Roy DM (2020) Relaxing the i.i.d. assumption: Adaptively minimax optimal regret via root-entropic regularization. Preprint, submitted July 13, https://arxiv.org/abs/2007.06552.Google Scholar
• Blanchard M (2022) Universal online learning: An optimistically universal learning rule. Loh P-L, Raginsky M, eds. Proc. 35th Conf. Learn. Theory, Proceedings of Machine Learning Research (PMLR, New York), 1077–1125. Google Scholar
• Bousquet O, Elisseeff A (2002) Stability and generalization. J. Machine Learn. Res. 2:499–526.Google Scholar
• Chaudhuri P (1991) Nonparametric estimates of regression quantiles and their local Bahadur representation. Ann. Statist. 19(2):760–777.Crossref, Google Scholar
• Chen N, Hu M (2023) Frontiers in service science: Data-driven revenue management: The interplay of data, model, and decisions. Service Sci. 15(2):79–91.Link, Google Scholar
• Cheung WC, Simchi-Levi D (2019) Sampling-based approximation schemes for capacitated stochastic inventory control models. Math. Oper. Res. 44(2):668–692.Link, Google Scholar
• Cortes C, Jackel LD, Chiang W-P (1995) Limits on learning machine accuracy imposed by data quality. Tesauro G, Touretzky D, Leen T, eds. Adv. Neural Inform. Processing Systems, vol. 7 (MIT Press, Cambridge, MA), 239–246.Google Scholar
• Donti P, Amos B, Kolter JZ (2017) Task-based end-to-end model learning in stochastic optimization. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems 30 (NIPS 2017) (Curran Associates, Red Hook, NY), 5484–5494.Google Scholar
• Elmachtoub AN, Grigas P (2021) Smart “predict, then optimize.” Management Sci. 68(1):9–26.Link, Google Scholar
• Farahani A, Voghoei S, Rasheed K, Arabnia HR (2021) A brief review of domain adaptation. Stahlbock R, Weiss GM, Abou-Nasr M, Yang C-Y, Arabnia HR, Deligiannidis L, eds. Adv. Data Sci. Inform. Engrg. Proc. ICDATA 2020 IKE 2020 (Springer, Cham, Switzerland), 877–894.Google Scholar
• Fu M, Li X, Zhang L (2021) Data-driven feature-based newsvendor: A distributionally robust approach. Preprint, submitted July 13, http://dx.doi.org/10.2139/ssrn.3885663.Google Scholar
• Gallego G, Moon I (1993) The distribution free newsboy problem: Review and extensions. J. Oper. Res. Soc. 44(8):825–834.Crossref, Google Scholar
• Gudivada V, Apon A, Ding J (2017) Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations. Internat. J. Adv. Software 10(1):1–20.Google Scholar
• Gupta V, Kallus N (2022) Data pooling in stochastic optimization. Management Sci. 68(3):1595–1615.Link, Google Scholar
• Gupta V, Rusmevichientong P (2021) Small-data, large-scale linear optimization with uncertain objectives. Management Sci. 67(1):220–241.Link, Google Scholar
• Györfi L, Kohler M, Krzyzak A, Walk H (2002) A Distribution-Free Theory of Nonparametric Regression, vol. 1 (Springer, New York).Crossref, Google Scholar
• Hannah L, Powell W, Blei D (2010) Nonparametric density estimation for stochastic optimization with an observable state variable. Lafferty J, Williams C, Shawe-Taylor J, Zemel R, Culotta A, eds. Adv. Neural Inform. Processing Systems, vol. 23 (Curran Associates, Red Hook, NY), 820–828.Google Scholar
• Huber J, Müller S, Fleischmann M, Stuckenschmidt H (2019) A data-driven newsvendor problem: From data to decision. Eur. J. Oper. Res. 278(3):904–915.Crossref, Google Scholar
• Kallus N, Mao X (2023) Stochastic optimization forests. Management Sci. 69(4):1975–1994.Link, Google Scholar
• Kannan R, Bayraksan G, Luedtke JR (2025) Technical note—Data-driven sample average approximation with covariate information. Oper. Res. 73(6):3245–3259.Link, Google Scholar
• Kao Y-H, Roy B, Yan X (2009) Directed regression. Bengio Y, Schuurmans D, Lafferty J, Williams C, Culotta A, eds. Adv. Neural Inform. Processing Systems, vol. 22 (Curran Associates, Red Hook, NY), 889–897.Google Scholar
• Kim S, Pasupathy R, Henderson SG (2015) A guide to sample average approximation. Fu M, ed. Handbook of Simulation Optimization (Springer, New York), 207–243.Crossref, Google Scholar
• Kleywegt AJ, Shapiro A, Homem-de Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.Crossref, Google Scholar
• Koenker R (2005) Quantile Regression (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Koenker R (2017) Quantile regression: 40 years on. Annual Rev. Econom. 9(1):155–176.Crossref, 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, Pál M, Roundy RO, Shmoys DB (2007) Approximation algorithms for stochastic inventory control models. Math. Oper. Res. 32(2):284–302.Link, Google Scholar
• Loog M, Viering T (2022) A survey of learning curves with bad behavior: Or how more data need not lead to better performance. Preprint, submitted November 25, https://arxiv.org/abs/2211.14061.Google Scholar
• Mohr F, van Rijn JN (2024) Learning curves for decision making in supervised machine learning: A survey. Machine Learn. 113(11):8371–8425.Crossref, Google Scholar
• Mohri M, Muñoz Medina A (2012) New analysis and algorithm for learning with drifting distributions. Bshouty NH, Stoltz G, Vayatis N, Zeugmann T, eds. Algorithmic Learn. Theory ALT 2012 (Springer, Berlin), 124–138.Google Scholar
• Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of Machine Learning (MIT Press, Cambridge, MA).Google Scholar
• Nadaraya EA (1964) On estimating regression. Theory Probab. Appl. 9(1):141–142.Crossref, 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
• Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans. Knowledge Data Engrg. 22(10):1345–1359.Crossref, Google Scholar
• Perakis G, Roels G (2008) Regret in the newsvendor model with partial information. Oper. Res. 56(1):188–203.Link, Google Scholar
• Qi M, Cao Y, Shen Z-J (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 Z-J (2023) A practical end-to-end inventory management model with deep learning. Management Sci. 69(2):759–773.Link, Google Scholar
• Rakhlin A, Sridharan K, Tewari A (2011) Online learning: Stochastic and constrained adversaries. Preprint, submitted April 27, https://arxiv.org/abs/1104.5070.Google Scholar
• Redko I, Morvant E, Habrard A, Sebban M, Bennani Y (2020) A survey on domain adaptation theory: Learning bounds and theoretical guarantees. Preprint, submitted April 24, https://arxiv.org/abs/2004.11829.Google Scholar
• Scarf H (1958) A min-max solution of an inventory problem. Arrow KJ, Karlin S, Scarf H, eds. Studies in the Mathematical Theory of Inventory and Production (Stanford University Press, Stanford, CA), 201–209.Google Scholar
• See C-T, Sim M (2010) Robust approximation to multiperiod inventory management. Oper. Res. 58(3):583–594.Link, Google Scholar
• Settles B (2012) Active Learning (Morgan & Claypool, San Rafael, CA).Google Scholar
• Shalev-Shwartz S, Ben-David S (2014) Understanding Machine Learning: From Theory to Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Srivastava PR, Wang Y, Hanasusanto GA, Ho CP (2021) On data-driven prescriptive analytics with side information: A regularized Nadaraya-Watson approach. Preprint, submitted October 10, https://arxiv.org/abs/2110.04855.Google Scholar
• Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans. Neural Networks 10(5):988–999.Crossref, Google Scholar
• Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surveys 53(3):1–34.Crossref, Google Scholar
• Watson GS (1964) Smooth regression analysis. Sankhyā Indian J. Statist. Ser. A 26(4):359–372.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
• Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. Proc. IEEE 109(1):43–76.Crossref, Google Scholar