Most of the studies on inventory control reported in earlier contributions deal with the optimization problems minimizing an expected cost criterion such as the long-run average cost. However, when the plant engineers design the inventory systems in practical situations, they must take account of reliability into the inventory system as well as economical aspect, where reliability can be define...

Companies survive in saturated markets trying to be more productive and more efficient. In this context, to manage more accurately the finished goods inventories becomes critical for make to stock production systems companies. In this paper an inventory replenishment expert system with the objectives of improving quality service and reducing holding costs is proposed. The Inventory Replenishmen...

Product returns are often characterized by a dual uncertainty on time and quantity. In the literature on inventory management with product returns, best forecasts have been associated with methods that use the most information regarding product return history. In practice however, data is often scarce and unreliable. In this paper we investigate the impact of (mis)information on inventory perfo...

We study inventory control for rental operations in a closed-loop supply chain. In such a system (e.g., Netflix), customers create online queues in a service provider’s website to indicate the items that they would like to rent. Leveraging this advance demand information, we propose effective forecast models and formulate a multi-item inventory control problem. We prove that the (L,U) policy is...

To have an efficient control of a huge amount of inventory items, traditional approach is to classify the inventory into different groups. Different inventory control policies can then applied to different groups. The well-known ABC classification is simple-to-understand and easy-to-use. However, ABC analysis is based on only single measurement such as annual dollar usage. It has been recognize...

We consider a stochastic inventory control problem with Markovian capacity and the option of order rejection. We show its optimal policy to be the combination of a capacity-dependent modified base-stock production policy and a capacity-dependent critical-point order acceptance policy. When capacity is stochastically monotone, we show that the policy parameters change over the current capacity l...

Reinforcement Learning (RL) can solve practical sequential decision problems, even when structures of the problems are less understood. However, some sequential decision problems intrinsically have structural parts that are easily to formulate and distinguish from less understood parts. Exploiting this knowledge may help improve performance of RL. This study proposed and investigated an approac...

Inventory control is an important factor in reducing the operating costs of the supply chain. This study proposes using a fuzzy inventory controller to make the ordering decision for the members in a supply chain. Such a controller is expressed as a set of fuzzy rules, which allows the manager to summarize his/her experience of reasoning process as well as incorporate conventional proportional ...