Markov modülasyonlu talep bilgisi ile önceden sipariş modeli

Inventory management and control systems are one of the most studied subjects by researchers of industrial engineering, operations research and management. There is a stream in the inventory control literature focuses on the management of inventories of seasonal products. Inventory managers face a significant amount of uncertainty when they plan the inventories for the seasonal products. One of the methods that inventory managers might apply to reduce the uncertainty is to exploit advance demand information (ADI). The objective of this study is to develop a mathematical model to analyze ordering decisions of the inventory managers dealing with a seasonal product. For this reason we build a dynamic programming model where the inventory manager has multiple ordering opportunities prior to the season. Under multiple ordering opportunities he has two decisions to make: the timing of the orders and the order quantity for each ordering opportunity. Our purpose is to investigate the optimal ordering policy in such a situation. In such a context, the inventory manager has to analyze the tradeoff between ordering early with discounted prices and observing the economic conditions that are correlated with the demand he faces with. A number of works in the literature explores the correlation between demand of certain items and market indices. To take advantage of this demand correlation, we model the exogenous information process as a discrete time Markov process. We develop a dynamic ordering model where the retailer observes the information process. Moreover we characterize the structure of optimal ordering policy which maximizes the expected profit of the retailer. Furthermore we analyze three extensions of the advance dynamic ordering model. Finally, we present some numerical examples to compare the dynamic ordering policy with existing ordering policies.