Confidence Intervals for Data-Driven Inventory Policies with Demand Censoring

Published Online:https://doi.org/10.1287/opre.2019.1883

References

  • Agrawal N, Smith SA (1996) Estimating negative binomial demand for retail inventory management with unobservable lost sales. Naval Res. Logist. 43(6):839–861.CrossrefGoogle Scholar
  • Anupindi R, Dada M, Gupta S (1998) Estimation of consumer demand with stock-out based substitution: An application to vending machine products. Marketing Sci. 17(4):406–423.LinkGoogle Scholar
  • Arlotto A, Steele JM (2016) A central limit theorem for temporally nonhomogenous markov chains with applications to dynamic programming. Math. Oper. Res. 41(4):1448–1468.LinkGoogle Scholar
  • Azoury KS (1985) Bayes solution to dynamic inventory models under unknown demand distribution. Management Sci. 31(9):1150–1160.LinkGoogle Scholar
  • Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Besbes O, Muharremoglu A (2013) On implications of demand censoring in the newsvendor problem. Management Sci. 59(6):1407–1424.LinkGoogle Scholar
  • Bollapragada S, Morton TE (1999) A simple heuristic for computing nonstationary (s, s) policies. Oper. Res. 47(4):576–584.LinkGoogle Scholar
  • Burnetas AN, Smith CE (2000) Adaptive ordering and pricing for perishable products. Oper. Res. 48(3):436–443.LinkGoogle Scholar
  • Chen L, Mersereau AJ (2015) Analytics for operational visibility in the retail store: The cases of censored demand and inventory record inaccuracy. Agrawal N, Smith S, eds. Retail Supply Chain Management, International Series in Operations Research & Management Science, vol. 223 (Springer, Boston), 79–112.Google Scholar
  • Chen L, Mersereau AJ, Wang Z (2017) Optimal merchandise testing with limited inventory. Oper. Res. 65(4):968–991.LinkGoogle Scholar
  • Cheung WC, Simchi-Levi D (2019) Sampling-based approximation schemes for capacitated stochastic inventory control models. Math. Oper. Res. 44(2):377–766.LinkGoogle Scholar
  • Conrad S (1976) Sales data and the estimation of demand. J. Oper. Res. Soc. 27(1):123–127.CrossrefGoogle Scholar
  • Dai T, Jerath K (2013) Salesforce compensation with inventory considerations. Management Sci. 59(11):2490–2501.LinkGoogle Scholar
  • Federgruen A, Zipkin P (1984) An efficient algorithm for computing optimal (s, s) policies. Oper. Res. 32(6):1268–1285.LinkGoogle 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.LinkGoogle Scholar
  • Heese HS, Swaminathan JM (2010) Inventory and sales effort management under unobservable lost sales. Eur. J. Oper. Res. 207(3):1263–1268.CrossrefGoogle Scholar
  • Huh WT, Rusmevichientong P (2009) A nonparametric asymptotic analysis of inventory planning with censored demand. Math. Oper. Res. 34(1):103–123.LinkGoogle 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.LinkGoogle Scholar
  • Iglehart DL (1963) Optimality of (s, s) policies in the infinite horizon dynamic inventory problem. Management Sci. 9(2):259–267.LinkGoogle Scholar
  • Jain A, Rudi N, Wang T (2014) Demand estimation and ordering under censoring: Stock-out timing is (almost) all you need. Oper. Res. 63(1):134–150.LinkGoogle Scholar
  • Kunnumkal S, Topaloglu H (2008) Using stochastic approximation methods to compute optimal base-stock levels in inventory control problems. Oper. Res. 56(3):646–664.LinkGoogle Scholar
  • Levi R, Shi C (2013) Approximation algorithms for the stochastic lot-sizing problem with order lead times. Oper. Res. 61(3):593–602.LinkGoogle 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
  • Lovejoy WS (1990) Myopic policies for some inventory models with uncertain demand distributions. Management Sci. 36(6):724–738.LinkGoogle Scholar
  • Nahmias S (1994) Demand estimation in lost sales inventory systems. Naval Res. Logist. 41(6):739–758.CrossrefGoogle Scholar
  • Porteus EL (1971) On the optimality of generalized (s, s) policies. Management Sci. 17(7):411–426.LinkGoogle Scholar
  • Porteus EL (2002) Foundations of Stochastic Inventory Theory (Stanford University Press, Stanford, CA).CrossrefGoogle 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.LinkGoogle Scholar
  • Scarf H (1959a) Bayes solutions of the statistical inventory problem. Ann. Math. Statist. 30(2):490–508.CrossrefGoogle Scholar
  • Scarf H (1959b) The optimality of (s, S) policies in the dynamic inventory problem. Arrow KJ, Karlin S, Suppes P, eds. Mathematical Methods in the Social Science (Stanford University Press, Stanford, CA), 196–202.Google Scholar
  • Sethi SP, Cheng F (1997) Optimality of (s, s) policies in inventory models with markovian demand. Oper. Res. 45(6):931–939.LinkGoogle Scholar
  • Shapiro A, Dentcheva D, Ruszczyński AP (2009) Lectures on Stochastic Programming: Modeling and Theory, vol. 9 (SIAM, Philadelphia).CrossrefGoogle 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.LinkGoogle Scholar
  • Stein C (1972) A bound for the error in the normal approximation to the distribution of a sum of dependent random variables. Proc. Sixth Berkeley Sympos. Math. Statist. Probab., vol. 2 (University of California Press, Berkeley), 583–602.Google Scholar
  • Van der Vaart AW (2000) Asymptotic Statistics, vol. 3 (Cambridge University Press, Cambridge, UK).Google Scholar
  • Vulcano G, Van Ryzin G, Ratliff R (2012) Estimating primary demand for substitutable products from sales transaction data. Oper. Res. 60(2):313–334.LinkGoogle Scholar
  • Wecker WE (1978) Predicting demand from sales data in the presence of stockouts. Management Sci. 24(10):1043–1054.LinkGoogle 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.LinkGoogle Scholar
  • Zheng YS, Federgruen A (1991) Finding optimal (s, s) policies is about as simple as evaluating a single policy. Oper. Res. 39(4):654–665.LinkGoogle Scholar
  • Zipkin PH (2000) Foundations of Inventory Management (McGraw-Hill, New York).Google Scholar
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