In this study, single item dynamic inventory control model is analyzed. In this model the decision maker counts the inventory periodically, calculates the reorder point and the replenishment point, and decides to replenish the stock according to the inventory position. This calculation is difficult and requires complex mathematical transactions when the demand and lead time are stochastic. For this reason, in this study, the simulation method and genetic algorithms method are used to calculate the reorder point and replenishment point by using total cost function per period. In this function, the ordering cost, the holding cost and the penalty cost are taken into account. The results of these two methods are compared with classic method based on real data where the demand distribution is normal, and the lead time distribution is uniform. Thereafter, three cost calculations and their effects on reorder point and replenishment point are analyzed at two different levels.

Anahtar Kelimeler:
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In this study, single item dynamic inventory control model is analyzed. In this model the decision maker counts the inventory periodically, calculates the reorder point and the replenishment point, and decides to replenish the stock according to the inventory position. This calculation is difficult and requires complex mathematical transactions when the demand and lead time are stochastic. For this reason, in this study, the simulation method and genetic algorithms method are used to calculate the reorder point and replenishment point by using total cost function per period. In this function, the ordering cost, the holding cost and the penalty cost are taken into account. The results of these two methods are compared with classic method based on real data where the demand distribution is normal, and the lead time distribution is uniform. Thereafter, three cost calculations and their effects on reorder point and replenishment point are analyzed at two different levels

Keywords:
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Replenishment Point,
Reorder Point,
Inventory Control,
Simulation

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