Filtering of a Partially Observed Inventory System

The vast majority of work done on inventory system is based on the critical assumption of fully observed inventory level dynamics and demands. Modern technology, like the internet, offers a tremendous number of opportunities to businesses to collect imperfect but useful information on potential customers which helps them planning efficiently to meet future demands. For instance visits to commercial web sites provides the management of a business of a source of partial information on future demands. On the other hand it is often the case that it is not economically viable to fully observe the dynamics of inventory levels and only partial information is accessible to the management. In this article, using hidden Markov model techniques we estimate the inventory level as well as future demands of partially observed inventory system. The parameters of the model are updated via the EM algorithm.

[1]  NahmiasSteven Perishable Inventory Theory , 1982 .

[2]  Lakdere Benkherouf,et al.  A HIDDEN MARKOV MODEL FOR AN INVENTORY SYSTEM WITH PERISHABLE ITEMS , 1997 .

[3]  Steven Nahmias,et al.  Perishable Inventory Theory: A Review , 1982, Oper. Res..

[4]  Hau L. Lee,et al.  Lot Sizing with Random Yields: A Review , 1995, Oper. Res..

[5]  John B. Moore,et al.  Hidden Markov Models: Estimation and Control , 1994 .

[6]  C. R. Sox,et al.  Adaptive Inventory Control for Nonstationary Demand and Partial Information , 2002, Manag. Sci..

[7]  New York Dover,et al.  ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .

[8]  Lakdere Benkherouf,et al.  Recursive estimation of inventory quality classes using sampling , 2003, Adv. Decis. Sci..

[9]  S. Eddy Hidden Markov models. , 1996, Current opinion in structural biology.

[10]  Suresh P. Sethi,et al.  A Multiperiod Newsvendor Problem with Partially Observed Demand , 2007, Math. Oper. Res..

[11]  N. Erkip,et al.  Optimal inventory policies under imperfect advance demand information , 2004 .

[12]  Ed. McKenzie,et al.  SOME SIMPLE MODELS FOR DISCRETE VARIATE TIME SERIES , 1985 .

[13]  G. Monahan State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 1982 .

[14]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[15]  Suresh P. Sethi,et al.  Optimal Ordering Policies for Inventory Problems with Dynamic Information Delays , 2009 .

[16]  R. Fildes Journal of the Royal Statistical Society (B): Gary K. Grunwald, Adrian E. Raftery and Peter Guttorp, 1993, “Time series of continuous proportions”, 55, 103–116.☆ , 1993 .

[17]  Yves Dallery,et al.  Integrating advance order information in make-to-stock production systems , 2002 .

[18]  George E. Monahan,et al.  A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 2007 .

[19]  S. Sethi,et al.  Inventory Models with Markovian Demands and Cost Functions of Polynomial Growth , 1998 .

[20]  Lakdere Benkherouf,et al.  A stochastic jump inventory model with deteriorating items , 2000 .

[21]  Lakdere Benkherouf,et al.  M-ary detection of Markov-modulated Poisson processes in inventory models , 2002, Appl. Math. Comput..

[22]  Vijay S. Mookerjee,et al.  Purchasing demand information in a stochastic-demand inventory system , 1997 .

[23]  Suresh P. Sethi,et al.  Optimality of Base-Stock and (s, S) Policies for Inventory Problems with Information Delays , 2006 .

[24]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[25]  Mohamed Alosh,et al.  FIRST‐ORDER INTEGER‐VALUED AUTOREGRESSIVE (INAR(1)) PROCESS , 1987 .

[26]  Alain Bensoussan,et al.  On the Optimal Control of Partially Observed Inventory Systems , 2005 .

[27]  Robert J. Elliott,et al.  Measure Theory and Filtering: Introduction and Applications , 2004 .

[28]  Suresh P. Sethi,et al.  Partially Observed Inventory Systems: The Case of Zero-Balance Walk , 2007, SIAM J. Control. Optim..