An inventory model for perishable items with stock and advertisement sensitive demand

Author(s):  
R. Roy Chowdhury ◽  
S.K. Ghosh ◽  
K.S. Chaudhuri
1999 ◽  
Vol 12 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Lakhdar Aggoun ◽  
Lakdere Benkherouf ◽  
Lotfi Tadj

In this paper, we propose a single-product, discrete time inventory model for perishable items. Inventory levels are reviewed periodically and units in stock have a maximum lifetime of M periods. It is assumed that the dynamics of the inventory level is driven by a parameter process (reflecting perishability) and demands. By observing the history of the inventory level we obtain the conditional distribution of the perishability parameter by using the change of measure techniques. A special case is also presented.


2003 ◽  
Vol 7 (4) ◽  
pp. 249-263 ◽  
Author(s):  
L. Aggoun ◽  
L. Benkherouf ◽  
A. Benmerzouga

In this paper we propose a new discrete time discrete state inventory model for perishable items of a single product. Items in stock are assumed to belong to one of a finite number of quality classes that are ordered in such a way that Class 1 contains the best quality and the last class contains the pre-perishable quality. By the end of each epoch, items in each inventory class either stay in the same class or lose quality and move to a lower class. The movement between classes is not observed. Samples are drawn from the inventory and based on the observations of these samples, optimal estimates for the number of items in each quality classes are derived.


2021 ◽  
pp. 226-234
Author(s):  
Fatma Ben Khalifa ◽  
Imen Safra ◽  
Chaaben Kouki ◽  
Zied Jemai

1997 ◽  
Vol 10 (4) ◽  
pp. 423-430 ◽  
Author(s):  
L. Aggoun ◽  
L. Benkherouf ◽  
L. Tadj

This paper deals with a parametric multi-period integer-valued inventory model for perishable items. Each item in the stock perishes in a given period of time with some probability. Demands are assumed to be random and the probability that an item perishes is not known with certainty. Expressions for various parameter estimates of the model are established and the problem of finding an optimal replenishment schedule is formulated as an optimal stochastic control problem.


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