Name
Abstract | ||
|---|---|---|
A critical assumption in the vast literature on inventory management has been that the current level of inventory is known to the decision maker. Some of the most celebrated results such as the optimality of base-stock policies have been obtained under this assumption. Yet it is often the case in practice that the decision makers have incomplete or partial information about their inventory levels. The reasons for this are many: Inventory records or cash register information differ from actual inventory because of a variety of factors including transaction errors, theft, spoilage, misplacement, unobserved lost demands, and information delays. As a result, what are usually observed are some events or surrogate measures, called signals, related to the inventory level. These relationships can provide the distribution of current inventory levels. Therefore, the system state in the inventory control problems is not the current inventory level, but rather its distribution given the observed signals. Thus, the analysis for finding optimal production or ordering policies takes place generally in the space of probability distributions. The purpose of this paper is to review some recent developments in the analysis of inventory management problems with incomplete information. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3182/20130619-3-RU-3018.00636 | IFAC Proceedings Volumes |
Keywords | DocType | Volume |
stochastic inventory problem,partial observations,rain check,Chebyshev polynomials,Fast Fourier Transforms | Conference | 46 |
Issue | ISSN | Citations |
9 | 1474-6670 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suresh Sethi | 1 | 1215 | 255.98 |
| Ruixia Shi | 2 | 11 | 3.64 |