Feature-Based Inventory Control with Censored Demand

References

• Agrawal S, Jia R (2019) Learning in structured MDPs with convex cost functions: Improved regret bounds for inventory management. Proc. 2019 ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 743–744.Google Scholar
• Ban G-Y (2020) Confidence intervals for data-driven inventory policies with demand censoring. Oper. Res. 68(2):309–326.Abstract, Google Scholar
• Ban G-Y, Keskin NB (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.Link, Google Scholar
• Ban G-Y, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
• Ban G-Y, Gallien J, Mersereau AJ (2019) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management 21(4):798–815.Link, Google Scholar
• Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276–294.Link, Google Scholar
• Bertsimas D, Kallus N (2019) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
• Bertsimas D, Thiele A (2006) A robust optimization approach to inventory theory. Oper. Res. 54(1):150–168.Link, Google Scholar
• Besbes O, Muharremoglu A (2013) On implications of demand censoring in the newsvendor problem. Management Sci. 59(6):1407–1424.Link, Google Scholar
• Bookbinder JH, Lordahl AE (1989) Estimation of inventory re-order levels using the bootstrap statistical procedure. IIE Trans. 21(4):302–312.Crossref, Google Scholar
• Bottou L, Bousquet O (2007) The tradeoffs of large scale learning. Platt J, Koller D, Singer Y, Roweis S, eds. Adv. Neural Inform. Processing Systems, vol. 20 (Curran Associates, Inc., Red Hook, NY), 161–168.Google Scholar
• Boyd S, Boyd SP, Vandenberghe L (2004) Convex Optimization (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Bu J, Simchi-Levi D, Wang C (2023) Context-based dynamic pricing with separable demand models. Preprint, submitted June 18, http://dx.doi.org/10.2139/ssrn.4140550.Google Scholar
• Burnetas AN, Smith CE (2000) Adaptive ordering and pricing for perishable products. Oper. Res. 48(3):436–443.Link, Google Scholar
• Chen B (2021) Data-driven inventory control with shifting demand. Production Oper. Management 30(5):1365–1385.Crossref, Google Scholar
• Chen N, Gallego G (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.Link, Google Scholar
• Chen B, Chao X, Ahn H-S (2019) Coordinating pricing and inventory replenishment with nonparametric demand learning. Oper. Res. 67(4):1035–1052.Abstract, Google Scholar
• Chen B, Chao X, Shi C (2021) Nonparametric learning algorithms for joint pricing and inventory control with lost sales and censored demand. Math. Oper. Res. 46(2):726–756.Link, Google Scholar
• Chen B, Chao X, Wang Y (2020a) Data-based dynamic pricing and inventory control with censored demand and limited price changes. Oper. Res. 68(5):1445–1456.Link, Google Scholar
• Chen W, Shi C, Duenyas I (2020b) Optimal learning algorithms for stochastic inventory systems with random capacities. Production Oper. Management 29(7):1624–1649.Crossref, Google Scholar
• Chen B, Jiang J, Zhang J, Zhou Z (2024) Learning to order for inventory systems with lost sales and uncertain supplies. Management Sci., ePub ahead of print March 4, https://doi.org/10.1287/mnsc.2022.02476.Link, Google Scholar
• Chen X, Owen Z, Pixton C, Simchi-Levi D (2022) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.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
• Cheung WC, Simchi-Levi D, Zhu R (2023) Nonstationary reinforcement learning: The blessing of (more) optimism. Management Sci. 69(10):5722–5739.Link, Google Scholar
• Chu LY, Shanthikumar JG, Shen Z-JM (2008) Solving operational statistics via a Bayesian analysis. Oper. Res. Lett. 36(1):110–116.Crossref, Google Scholar
• Chu LY, Feng Q, Shanthikumar JG, Shen Z-JM, Wu J (2024) Solving the price-setting newsvendor problem with parametric operational data analytics (ODA). Management Sci. Forthcoming.Link, Google Scholar
• Copas JB (1983) Regression, prediction and shrinkage. J. Roy. Statist. Soc. B 45(3):311–335.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 (2023) The framework of parametric and non-parametric operational data analytics (ODA). Production Oper. Management 32(9):2685–2703.Crossref, Google 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.Link, Google Scholar
• Flaxman AD, Kalai AT, Mcmahan HB (2005) Online convex optimization in the bandit setting: Gradient descent without a gradient. Proc. 16th Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 385–394.Google Scholar
• Godfrey GA, Powell WB (2001) An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution. Management Sci. 47(8):1101–1112.Link, Google Scholar
• Gong XY, Simchi-Levi D (2023) Bandits atop reinforcement learning: Tackling online inventory models with cyclic demands. Management Sci., ePub ahead of print October 26, https://doi.org/10.1287/mnsc.2023.4947.Link, 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, Inc., Red Hook, NY), 820–828.Google Scholar
• Hazan E (2016) Introduction to Online Convex Optimization, Foundations and Trends in Optimization, vol. 2, no. 3–4 (Now Foundations and Trends, Norwell, MA), 157–325.Crossref, Google Scholar
• Hazan E, Kalai A, Kale S, Agarwal A (2006) Logarithmic regret algorithms for online convex optimization. Lugosi G, Simon HU, eds. Internat. Conf. Comput. Learn. Theory (Springer, Berlin, Heidelberg), 499–513.Google Scholar
• Hoerl AE, Kennard RW (1970) Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1):55–67.Crossref, Google Scholar
• Huh WT, Rusmevichientong P (2009) A nonparametric asymptotic analysis of inventory planning with censored demand. Math. Oper. Res. 34(1):103–123.Link, Google Scholar
• Huh WT, Janakiraman G, Muckstadt JA, Rusmevichientong P (2009) An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand. Math. Oper. Res. 34(2):397–416.Link, Google Scholar
• Huh WT, Levi R, Rusmevichientong P, Orlin JB (2011) Adaptive data-driven inventory control with censored demand based on Kaplan-Meier estimator. Oper. Res. 59(4):929–941.Link, Google Scholar
• Jain A, Rudi N, Wang T (2015) Demand estimation and ordering under censoring: Stock-out timing is (almost) all you need. Oper. Res. 63(1):134–150.Link, Google Scholar
• James W, Stein C (1992) Estimation with quadratic loss. Kotz S, Johnson NL, eds. Breakthroughs in Statistics (Springer, New York), 443–460.Crossref, Google Scholar
• Kleinberg RD (2004) Nearly tight bounds for the continuum-armed bandit problem. Saul L, Weiss Y, Bottou L, eds. Adv. Neural Inform. Processing Systems, vol. 17 (MIT Press, Cambridge, MA), 697–704.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
• Lu X, Song J-S, Zhu K (2008) Analysis of perishable-inventory systems with censored demand data. Oper. Res. 56(4):1034–1038.Link, Google Scholar
• Mamani H, Nassiri S, Wagner MR (2017) Closed-form solutions for robust inventory management. Management Sci. 63(5):1625–1643.Link, Google Scholar
• Mersereau AJ (2015) Demand estimation from censored observations with inventory record inaccuracy. Manufacturing Service Oper. Management 17(3):335–349.Link, Google Scholar
• Miao S, Chao X (2022) Online personalized assortment optimization with high-dimensional customer contextual data. Manufacturing Service Oper. Management 24(5):2741–2760.Link, Google Scholar
• Nambiar M, Simchi-Levi D, Wang H (2019) Dynamic learning and pricing with model misspecification. Management Sci. 65(11):4980–5000.Link, 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
• Peng Z, Rong Y, Zhu T (2024) Transformer-based choice model: A tool for assortment optimization evaluation. Preprint, submitted December 10, http://dx.doi.org/10.2139/ssrn.4298996.Google Scholar
• Powell W, Ruszczyński A, Topaloglu H (2004) Learning algorithms for separable approximations of discrete stochastic optimization problems. Math. Oper. Res. 29(4):814–836.Link, Google Scholar
• Qi Z, Tang J, Fang E, Shi C (2022) Offline feature-based pricing under censored demand: A causal inference approach. Preprint, submitted February 21, http://dx.doi.org/10.2139/ssrn.4040305.Google Scholar
• Ruder S (2016) An overview of gradient descent optimization algorithms. Preprint, submitted September 15, https://doi.org/10.48550/arXiv.1609.04747.Google Scholar
• See C-T, Sim M (2010) Robust approximation to multiperiod inventory management. Oper. Res. 58(3):583–594.Link, Google Scholar
• Shalev-Shwartz S (2012) Online learning and online convex optimization. Foundations Trends Machine Learn. 4(2):107–194.Crossref, 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
• Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B 58(1):267–288.Crossref, Google Scholar
• Yuan H, Luo Q, Shi C (2021) Marrying stochastic gradient descent with bandits: Learning algorithms for inventory systems with fixed costs. Management Sci. 67(10):6089–6115.Link, 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 H, Chao X, Shi C (2020) Closing the gap: A learning algorithm for lost-sales inventory systems with lead times. Management Sci. 66(5):1962–1980.Link, Google Scholar
• Zhang L, Yang J, Gao R (2023) Optimal robust policy for feature-based newsvendor. Management Sci., ePub ahead of print June 2, https://doi.org/10.1287/mnsc.2023.4810.Google Scholar
• Zinkevich M (2003) Online convex programming and generalized infinitesimal gradient ascent. Proc. 20th Internat. Conf. Machine Learn. (ICML-03) (AAAI Press, Palo Alto, CA), 928–936.Google Scholar