Envanter zinciri modellerinde üretim ve dağıtım kararlarının koordinasyonu

This study is focused on the coordination of transportation and production policies between a supplier and a retailer in an inventory system. Both a deterministic model, in which the production and the demand rates of a specific product are known and constant, and a stochastic model, in which the production and the demand rates are random, are considered. In these models, the supplier makes the production, holds inventory and ships the products to the retailer to satisfy the external demand that arrives to the retailer. We analyze two versions of this problem. In the first model, we assume that the retailer also holds inventory and satisfies the customer orders immediately and in the second model, we assume that the retailer does not hold inventory but accumulates the customer orders and satisfies them at a later time, where the customers are willing to wait at the expense of a waiting cost. We investigate both a vendor-managed inventory (VMI) setting and a decentralized model (non-VMI setting). In the non-VMI model, the retailer manages its own inventory and sends orders to the supplier and the supplier determines its own production process and the amount to produce in an inventory replenishment cycle considering the order quantity of the retailer. However, in the vendor managed inventory setting, the supplier makes all the decisions and determines the length of the production and transportation cycles and the shipment quantities to the retailer. In this study, for the deterministic case, we determine the length of the optimal production and transportation cycles in both the VMI and non-VMI models and compare the costs of these models. Then, we analyze the stochastic case in which the demand and the production rates are random. We use an infinite-horizon dynamic programming approach and we show that the optimal production and transportation decisions in the stochastic case are complex and non-monotonic. Therefore, we also consider simpler policies such as time-based and quantity-based transportation policies, which are widely used in the industry, and we analyze the efficiency of these policies comparedto the optimal solution.