Contextual Offline Demand Learning and Pricing with Separable Models

Published Online:https://doi.org/10.1287/mnsc.2023.04026

References

  • Alley M, Biggs M, Hariss R, Herrmann C, Li ML, Perakis G (2023) Pricing for heterogeneous products: Analytics for ticket reselling. Manufacturing Service Oper. Management 25(2):409–426.LinkGoogle Scholar
  • Audibert JY (2007) Progressive mixture rules are deviation suboptimal. Platt J, Koller D, Singer Y, Roweis S, eds. Advances in Neural Information Processing Systems, vol. 20 (Curran Associates Inc., Red Hook, NY), 41–48.Google Scholar
  • Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Bertsekas DP (1997) Nonlinear programming. J. Oper. Res. Soc. 48(3):334–334.CrossrefGoogle Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.LinkGoogle Scholar
  • Besbes O, Zeevi A (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.LinkGoogle Scholar
  • Boyd SP, Vandenberghe L (2004) Convex Optimization (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Broder J, Rusmevichientong P (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.LinkGoogle Scholar
  • Brunk H, Barlow RE, Bartholomew DJ, Bremner JM (1972) Statistical inference under order restrictions (the theory and application of isotonic regression). Internat. Statist. Rev. 41(3):395–430.Google Scholar
  • Bu J, Simchi-Levi D, Wang C (2022a) Context-based dynamic pricing with partially linear demand model. Adv. Neural Inform. Process. Syst. 35:23780–23791.Google Scholar
  • Bu J, Simchi-Levi D, Wang C (2022b) Context-based dynamic pricing with separable demand models. Preprint, submitted June 18, https://doi.org/10.2139/ssrn.4140550.Google Scholar
  • Bu J, Simchi-Levi D, Xu Y (2022c) Online pricing with offline data: Phase transition and inverse square law. Management Sci. 68(12):8568–8588.LinkGoogle Scholar
  • Bu J, Simchi-Levi D, Wang L (2023) Offline pricing and demand learning with censored data. Management Sci. 69(2):885–903.LinkGoogle Scholar
  • Cakanyildirim M, Roundy RO (2002) SeDFAM: Semiconductor demand forecast accuracy model. IIE Trans. 34(5):449–465.CrossrefGoogle Scholar
  • Carbonneau R, Laframboise K, Vahidov R (2008) Application of machine learning techniques for supply chain demand forecasting. Eur. J. Oper. Res. 184(3):1140–1154.CrossrefGoogle Scholar
  • Chakravarti N (1989) Isotonic median regression: A linear programming approach. Math. Oper. Res. 14(2):303–308.LinkGoogle Scholar
  • Chase CW Jr (2013) Using big data to enhance demand-driven forecasting and planning. J. Bus. Forecasting 32(2):27–35.Google Scholar
  • Chen N, Gallego G (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.LinkGoogle 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.LinkGoogle Scholar
  • Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 785–794.Google Scholar
  • Chen X, Jasin S, Shi C (2022a) The Elements of Joint Learning and Optimization in Operations Management, vol. 18 (Springer Nature, Cham, Switzerland).CrossrefGoogle Scholar
  • Chen X, Owen Z, Pixton C, Simchi-Levi D (2022b) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.LinkGoogle Scholar
  • Cheung WC, Simchi-Levi D (2019) Sampling-based approximation schemes for capacitated stochastic inventory control models. Math. Oper. Res. 44(2):668–692.LinkGoogle Scholar
  • Cohen MC, Lobel I, Paes Leme R (2020) Feature-based dynamic pricing. Management Sci. 66(11):4921–4943.LinkGoogle Scholar
  • Deng Y, Zhang X, Wang T, Wang L, Zhang Y, Wang X, Zhao S, Qi Y, Yang G, Peng X (2023) Alibaba realizes millions in cost savings through integrated demand forecasting, inventory management, price optimization, and product recommendations. INFORMS J. Appl. Anal. 53(1):32–46.LinkGoogle Scholar
  • Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize”. Management Sci. 68(1):9–26.LinkGoogle Scholar
  • Elmachtoub A, Gupta V, Zhao Y (2023a) Balanced off-policy evaluation for personalized pricing. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 10901–10917.Google Scholar
  • Elmachtoub AN, Lam H, Zhang H, Zhao Y (2023b) Estimate-then-optimize versus integrated-estimation-optimization: A stochastic dominance perspective. Preprint, submitted April 13, https://arxiv.org/abs/2304.06833.Google Scholar
  • Fan J, Guo Y, Yu M (2022) Policy optimization using semiparametric models for dynamic pricing. J. Amer. Statist. Assoc. 119(545):552–564. Google Scholar
  • Feng Q, Shanthikumar JG (2018a) How research in production and operations management may evolve in the era of big data. Production Oper. Management 27(9):1670–1684.CrossrefGoogle Scholar
  • Feng Q, Shanthikumar JG (2018b) Supply and demand functions in inventory models. Oper. Res. 66(1):77–91.LinkGoogle Scholar
  • Feng Q, Shanthikumar JG (2022) Developing operations management data analytics. Production Oper. Management 31(12):4544–4557.CrossrefGoogle Scholar
  • Feng Q, Shanthikumar JG (2023) The framework of parametric and non-parametric operational data analytics (ODA). Preprint, submitted March 20, https://doi.org/10.2139/ssrn.4400555.Google Scholar
  • Feng Q, Luo S, Zhang D (2014) Dynamic inventory–Pricing control under backorder: Demand estimation and policy optimization. Manufacturing Service Oper. Management 16(1):149–160.LinkGoogle Scholar
  • Ferreira KJ, Lee BHA, Simchi-Levi D (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.LinkGoogle Scholar
  • Friedman JH (2001) Greedy function approximation: A gradient boosting machine. Ann. Statist. 29(5):1189–1232. Google Scholar
  • Gallego G, Topaloglu H (2014) Constrained assortment optimization for the nested logit model. Management Sci. 60(10):2583–2601.LinkGoogle Scholar
  • Gallego G, Wang R (2014) Multiproduct price optimization and competition under the nested logit model with product-differentiated price sensitivities. Oper. Res. 62(2):450–461.LinkGoogle Scholar
  • Gao P, Ma Y, Chen N, Gallego G, Li A, Rusmevichientong P, Topaloglu H (2021) Assortment optimization and pricing under the multinomial logit model with impatient customers: Sequential recommendation and selection. Oper. Res. 69(5):1509–1532.LinkGoogle Scholar
  • Gardner ES (1990) Evaluating forecast performance in an inventory control system. Management Sci. 36(4):490–499.LinkGoogle Scholar
  • Groeneboom P, Jongbloed G (2014) Nonparametric Estimation under Shape Constraints, Number 38 (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Hastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2 (Springer, New York).CrossrefGoogle Scholar
  • Hoch SJ, Kim BD, Montgomery AL, Rossi PE (1995) Determinants of store-level price elasticity. J. Marketing Res. 32(1):17–29.CrossrefGoogle Scholar
  • Hong M, Wang X, Razaviyayn M, Luo ZQ (2017) Iteration complexity analysis of block coordinate descent methods. Math. Programming 163:85–114.CrossrefGoogle 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.CrossrefGoogle Scholar
  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 3146–3154.Google Scholar
  • Keskin N, Zeevi A (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.LinkGoogle Scholar
  • Keskin NB, Li Y, Song JS (2022) Data-driven dynamic pricing and ordering with perishable inventory in a changing environment. Management Sci. 68(3):1938–1958.LinkGoogle Scholar
  • Kök AG, Fisher ML, Vaidyanathan R (2009) Assortment planning: Review of literature and industry practice. Agrawal N, Smith SA, eds. Retail Supply Chain Management: Quantitative Models and Empirical Studies (Springer, New York), 99–153.Google Scholar
  • Kur G, Rakhlin A, Guntuboyina A (2020) On suboptimality of least squares with application to estimation of convex bodies. Conf. Learn. Theory (PMLR, New York), 2406–2424.Google Scholar
  • Lei Y, Miao S, Momot R (2023) Privacy-preserving personalized revenue management. Management Sci. 70(7):4875–4892.LinkGoogle Scholar
  • Lei M, Liu S, Jasin S, Vakhutinsky A (2022b) Joint inventory and pricing for a one-warehouse multistore problem: Spiraling phenomena, near optimal policies, and the value of dynamic pricing. Oper. Res. 72(2):738–762.LinkGoogle Scholar
  • Lei D, Qi Y, Liu S, Geng D, Zhang J, Hu H, Shen ZJM (2022a) Pooling and boosting for demand prediction in retail: A transfer learning approach. Preprint, submitted September 10, https://doi.org/10.2139/ssrn.4490516.Google Scholar
  • Levi R, Perakis G, Uichanco J (2015) The data-driven newsvendor problem: New bounds and insights. Oper. Res. 63(6):1294–1306.LinkGoogle 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.LinkGoogle Scholar
  • Li X, Zheng Z (2023) Dynamic pricing with external information and inventory constraint. Management Sci. 70(9):5985–6001.Google Scholar
  • Li X, Zhao T, Arora R, Liu H, Hong M (2017) On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization. J. Mach. Learn. Res. 18(1):6741–6764.Google Scholar
  • Liang T, Rakhlin A, Sridharan K (2015) Learning with square loss: Localization through offset Rademacher complexity. Conf. Learn. Theory (PMLR, New York), 1260–1285.Google Scholar
  • Liu J, Qin H, Chou MC (2023) Pricing analytics with shape-restricted demands. Preprint, submitted November 25, https://doi.org/10.2139/ssrn.4643942.Google Scholar
  • Mendelson S (2015) Learning without concentration. J. ACM 62(3):1–25.CrossrefGoogle Scholar
  • Miao S, Chao X (2020) Dynamic joint assortment and pricing optimization with demand learning. Manufacturing Service Oper. Management 23(2):525–545.Google Scholar
  • Miao S, Chen X, Chao X, Liu J, Zhang Y (2019) Context-based dynamic pricing with online clustering. Preprint, submitted February 17, https://arxiv.org/abs/1902.06199.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.CrossrefGoogle Scholar
  • Nambiar M, Simchi-Levi D, Wang H (2019) Dynamic learning and pricing with model misspecification. Management Sci. 65(11):4980–5000.LinkGoogle Scholar
  • Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, vol. 31 (Curran Associates Inc., Red Hook, NY), 6638–6648.Google Scholar
  • Qiang S, Bayati M (2016) Dynamic pricing with demand covariates. Preprint, submitted April 14, https://doi.org/10.2139/ssrn.2765257.Google Scholar
  • Qin H, Simchi-Levi D, Wang L (2022) Data-driven approximation schemes for joint pricing and inventory control models. Management Sci. 68(9):6591–6609.LinkGoogle Scholar
  • Rakhlin A (2022) Mathematical statistics: A non-asymptotic approach. Lecture Notes, MIT (Spring), https://www.mit.edu/∼rakhlin/courses/mathstat/rakhlin_mathstat_sp22.pdf.Google Scholar
  • Rusmevichientong P, Shen ZJM, Shmoys DB (2010) Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Oper. Res. 58(6):1666–1680.LinkGoogle Scholar
  • Simchi-Levi D, Sun R, Wu MX, Zhu R (2023) Calibrating sales forecasts in a pandemic using competitive online nonparametric regression. Management Sci. 70(10):6502–6518.LinkGoogle Scholar
  • Talluri K, Van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.LinkGoogle Scholar
  • Tang J, Qi Z, Fang E, Shi C (2025) Offline feature-based pricing under censored demand: A causal inference approach. Manufacturing Service Oper. Management 27(2):535–553.LinkGoogle Scholar
  • Tappenden R, Richtárik P, Gondzio J (2016) Inexact coordinate descent: Complexity and preconditioning. J. Optim. Theory Appl. 170:144–176.CrossrefGoogle Scholar
  • Tseng P (2001) Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl. 109:475–494.CrossrefGoogle Scholar
  • van Eeden C (1958) Testing and estimating ordered parameters of probability distribution.Google Scholar
  • Wainwright MJ (2019) High-Dimensional Statistics: A Non-Asymptotic Viewpoint, vol. 48 (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Wang Y, Chen B, Simchi-Levi D (2021) Multimodal dynamic pricing. Management Sci. 67(10):6136–6152.LinkGoogle Scholar
  • Yang Y, Barron A (1999) Information-theoretic determination of minimax rates of convergence. Ann. Statist. 27(5):1564–1599. Google Scholar
  • Yang Y, Pesavento M, Luo ZQ, Ottersten B (2019) Inexact block coordinate descent algorithms for nonsmooth nonconvex optimization. IEEE Trans. Signal Process 68:947–961.CrossrefGoogle Scholar
  • Zhang C, Ma Y (2012) Ensemble Machine Learning: Methods and Applications (Springer, New York).CrossrefGoogle Scholar
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