Subscribe Now Subscribe Today
Science Alert
 
Blue
   
Curve Top
Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 7 | Page No.: 1228-1234
DOI: 10.3923/jas.2008.1228.1234
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Optimizing Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishments

Ata Allah Taleizadeh, Mir-Bahador Aryanezhad and Seyed Taghi Akhavan Niaki

Abstract:
Multi-periodic inventory control problems are mainly studied employing two assumptions. The first is the continuous review, where depending on the inventory level orders can happen at any time and the other is the periodic review, where orders can only happen at the beginning of each period. In this study, we relax these assumptions and assume that the periodic replenishments are stochastic in nature. Furthermore, we assume that the periods between two replenishments are independent and identically random variables. For the problem at hand, the decision variables are of integer-type and there are two kinds of space and service level constraints for each product. We develop a model of the problem in which a combination of back-order and lost-sales are considered for the shortages. Then, we show that the model is of an integer-nonlinear-programming type and in order to solve it, a search algorithm can be utilized. We employ a simulated annealing approach and provide a numerical example to demonstrate the applicability of the proposed methodology.
PDF Fulltext XML References Citation Report Citation
How to cite this article:

Ata Allah Taleizadeh, Mir-Bahador Aryanezhad and Seyed Taghi Akhavan Niaki , 2008. Optimizing Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishments. Journal of Applied Sciences, 8: 1228-1234.

DOI: 10.3923/jas.2008.1228.1234

URL: https://scialert.net/abstract/?doi=jas.2008.1228.1234

COMMENTS
24 June, 2009
Seyed Taghi Akhavan Niaki:
How come this paper has not been indexed in Scopus yet?
COMMENT ON THIS PAPER
 
 
 

 

 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 

Curve Bottom