Adaptive Inventory Control for Nonstationary Demand and Partial Information

References

• Agrawal Narendra, Smith Stephen A. Estimating negative binomial demand for retail inventory management with unobservable lost sales. Naval Res. Logist. Quart. (1996) 43(6):839–861Crossref, Google Scholar
• Ravi Anupindi, Morton Thomas E., Pentico David. The nonstationary stochastic lead-time inventory problem: Near myopic bounds, heuristics, and testing. Management Sci. (1996) 42(1):124–129Link, Google Scholar
• Arrow Kenneth J., Theodore Harris, Marshak Jacob. Optimal inventory theory. Econometrica (1951) 19(3):250–272Crossref, Google Scholar
• Azoury Katy S. Bayes solution to dynamic inventory models under unknown demand distributions. Management Sci. (1985) 31(9):1150–1160Link, Google Scholar
• Baum Leonard E., Petrie Ted, Soules George, Weiss Norman. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist. (1970) 41(1):164–171Crossref, Google Scholar
• Bean J. C., Smith and R. L. Conditions for the existence of planning horizons. Math. Oper. Res. (1984) 9(3):391–401Link, Google Scholar
• Bertsekas Dimitri P.Dynamic Programming and Optimal Control (1995) 1(Athena Scientific, Belmont, MA.) Google Scholar
• Dempster A. P., Laird N. M., Rubin D. B. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc., Series B (1977) 39:1–38Google Scholar
• Federgruen A., Schweitzer P. J., Tijms H. C. Contraction mappings underlying undiscounted Markov decision problems. J. Math. Anal. Appl. (1978) 65:711–730Crossref, Google Scholar
• Gallego Guillermo, Ryan Jennifer K., Simchi-Levi David. Minimax analysis for the discrete finite horizon inventory model (1996) (Columbia University, New York, NY) . Working paperGoogle Scholar
• Graves Stephen C.A single-item inventory model for a non-stationary demand process (1997) (Massachusetts Institute of Technology, Cambridge, MA) . Working paperGoogle Scholar
• Hadley G., Whitin T. M. An optimal final inventory model. Management Sci. (1961) 7:179–183Link, Google Scholar
• Hu C., Lovejoy W. S., Shafer S. L. Comparison of some suboptimal control policies in medical drug therapy. Oper. Res. (1996) 44(5):696–709Link, Google Scholar
• Kambhamettu Srinivas S.Parameter estimation for partially observable Markov decision processes (2000) (Department of Industrial and Systems Engineering, Auburn University, Auburn, AL) . Master's thesisGoogle Scholar
• Karlin Samuel. Dynamic inventory policy with varying stochastic demands. Management Sci. (1960) 6(3):231–258Link, Google Scholar
• Kurawarwala Abbas A., Matsuo Hirofumi. Forecasting and inventory management of short life-cycle products. Oper. Res. (1996) 44(1):131–150Link, Google Scholar
• Lariviere Martin A., Porteus Evan L.Informational dynamics and new product pricing (1995) (Stanford University, Stanford, CA) . Working paperGoogle Scholar
• Lee H. L., Nahmias S., Graves S. C., Kan Rinnooy A. H. G., Zipkin P. H. Single product, single-location models. Handbooks in Operations Research and Management Science: Logistics of Production and Inventory (1993) 4(Elsevier Science, Amsterdam, The Netherlands) 1–55Crossref, Google Scholar
• Leroux Brian G., Puterman Martin L. Maximum-penalized-likelihood estimation for independent and Markov-dependent mixture models. Biometrics (1992) 48:545–568Crossref, Google Scholar
• Lovejoy William S. Some monotonicity results for partially observed Markov decision processes. Oper. Res. (1987) 35(5):736–743Link, Google Scholar
• Lovejoy William S. Myopic policies for some inventory models with uncertain demand distributions. Management Sci. (1990) 36(6):724–738Link, Google Scholar
• Loverjoy William S. A survey of algorithmic methods for partially observed Markov decision processes. Ann. Oper. Res. (1990) 28:47–65Crossref, Google Scholar
• Loverjoy William S. Stopped myopic policies in some inventory models with generalized demand processes. Management Sci. (1992) 38(5):688–707Link, Google Scholar
• Loverjoy William S. Suboptimal policies with bounds, for parameter adaptive decision processes. Oper. Res. (1993) 41(3):583–599Link, Google Scholar
• Monahan George E. A survey of partially observable Markov decision processes: Theory models, and algorithms. Management Sci. (1982) 28(1):1–16Link, Google Scholar
• Morton T. E., Wecker W. E. Discounting, ergodicity and convergence for Markov decision processes. Management Sci. (1977) 23(8):890–900Link, Google Scholar
• Rabiner Lawrence R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE (1989) 77(2):257–285Crossref, Google Scholar
• Rhenius D. Incomplete information in Markovian decision models. Ann. Statist. (1974) 2:1327–1334Crossref, Google Scholar
• Scarf Herbert E. Bayes solution of the statistical inventory problem. Ann. Math. Statist. (1959) 30:490–508Crossref, Google Scholar
• Scarf Herbert E. Some remarks on Bayes solution to the inventory problem. Naval Res. Logist. Quart. (1960) 7:591–596Crossref, Google Scholar
• Smallwood Richard D., Sondik Edward J. The optimal control of partially observable Markov processes over a finite horizon. Oper. Res. (1973) 21:1071–1088Link, Google Scholar
• Song Jing-Sheng, Paul Zipkin. Inventory control in a fluctuating demand environment. Oper. Res. (1993) 41(2):351–370Link, Google Scholar
• Song Jing-Sheng. Managing inventory with the prospect of obsolescence. Oper. Res. (1996) 44(1):215–222Link, Google Scholar
• Veinott Arthur F., Scarf Herbert E., Gilford Dorothy M., Shelly Maynard W. Optimal stockage policies with nonstationary stochastic demands. Multistage Inventory Models and Techniques (1963) (Office of Naval Research Monograph Series on Mathematical Methods in Logistics, Stanford University Press, Stanford, CA) 85–115Google Scholar
• White Chelsea C., Scherer William T. Solution procedures and partially observed Markov decision processes. Oper. Res. (1989) 37(5):791–797Link, Google Scholar
• White Chelsea C., Scherer William T. Finite-memory suboptimal design for partially observed Markov decision processes. Oper. Res. (1994) 42(3):439–455Link, Google Scholar