TY - GEN
T1 - Control of Inventories with Markov Demand
AU - Bensoussan, Alain
PY - 2012
Y1 - 2012
N2 - We consider inventory control problems in discrete time. The horizon is infinite, and we consider discounted payoffs as well nondiscounted payoffs (ergodic control). We may have backlog or not. We may have set-up costs or not. In the traditional framework, the demand is modeled as a sequence of i.i.d. random variables. The ordering strategy is given by a base stock policy or an s, S policy, whether or not there is a set-up cost. We consider here the situation when the demand is modeled by a Markov chain. We show how the base stock policy and the s, S policy can be extended. © Springer-Verlag Berlin Heidelberg 2012.
AB - We consider inventory control problems in discrete time. The horizon is infinite, and we consider discounted payoffs as well nondiscounted payoffs (ergodic control). We may have backlog or not. We may have set-up costs or not. In the traditional framework, the demand is modeled as a sequence of i.i.d. random variables. The ordering strategy is given by a base stock policy or an s, S policy, whether or not there is a set-up cost. We consider here the situation when the demand is modeled by a Markov chain. We show how the base stock policy and the s, S policy can be extended. © Springer-Verlag Berlin Heidelberg 2012.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84893612646&origin=recordpage
U2 - 10.1007/978-3-642-29982-7_2
DO - 10.1007/978-3-642-29982-7_2
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783642299810
VL - 22
SP - 29
EP - 55
BT - Springer Proceedings in Mathematics and Statistics
PB - Springer New York
T2 - 9th Workshop on Stochastic Analysis and Related Topics
Y2 - 14 June 2010 through 15 June 2010
ER -