Optimality of Myopic Policies in Disposing Excess Inventory

Myopic Inventory Policies in Continuous Inventory Systems with Emergency Orders

A make-to-order environment requires flexibility in the supply chain to facilitate fast response. Timely supply deliveries can be achieved in a cost-effective manner by having a portfolio of suppliers, or a portfolio of contracts with a single supplier, with different lead times and associated costs. In this paper we address the inventory management of such a system and evaluate the savings ass...

Myopic Inventory Policies Using Individual Customer Arrival Information

1 We investigate optimality of myopic policies using the single-unit decomposition approach in inventory management. We derive, under certain conditions, closed-form replenishment decisions, that we call a base-probability policy. That is, the order associated with a given customer is placed if and only if its arrival probability within the lead-time is higher than a threshold.

Optimality of Zero-Inventory Policies for Unreliable Manufacturing Systems

On Optimality of a Class of Dynamic Myopic Policies for Continuous-Time Replenishment with Periodic Updates

This paper is motivated by inventory problems arising in supply chains characterized by continuous replenishment programs based on information exchanged (reviewed) only intermittently between a manufacturing system (supplier) and a customer (retailer). When the replenishment is once-per-period, rather than at any point of time, a well-known result is the optimality of the so-called myopic base-...

Optimality of (s, S) Policies in Inventory Models with Markovian Demand

This paper is concerned with a generalization of classical inventory models (with xed ordering costs) that exhibit (s; S) policies. In our model, the distribution of demands in successive periods is dependent on a Markov chain. The model includes the case of cyclic or seasonal demand. The model is further extended to incorporate some other realistic features such as no ordering periods and stor...