Myopic inventory policies using individual customer arrival information

We investigate optimality of myopic policies using the single-unit decomposition approach in inventory management. We derive, under certain conditions, closed-form replenishment decisions, which 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.inventory management; base-stock policies; myopic policies;

A characterization of optimal base-stock levels for a continuous-stage serial supply chain

In this paper, we present a continuous model to optimize multi-echelon inventory management decisions under stochastic demand. Observing that in such continuous system it is never optimal to let orders cross, we decompose the general problem into a set of single-unit sub-problems that can be solved in a sequential fashion. When shipping and inventory holding costs are linear in the stage, we show that it is optimal to move the unit associated with the k-th next customer if and only if the inventory unit is held in an echelon located within a given interval. This optimal policy can be interpreted as an echelon base-stock policy such that the base-stock is initially increasing and then decreasing in the stage. We also characterize the optimal policy when costs are piecewise-constant. Finally, we study the sensitivity of the optimal base-stock levels to the cost structures.multi-echelon; optimal control; unit-tracking decomposition;

Myopic Inventory Policies Using Individual Customer Arrival Information

In this paper, we investigate the optimality of myopic inventory replenishment policies in a periodic-review single-echelon system, with nonstationary, correlated, stochastic demand and cost, and nonincreasing stochastic prices. Using the single-unit decomposition approach, we provide certain general conditions on the demand and cost processes under which a myopic policy is optimal. Under these conditions, the optimal policy is a myopic state-dependent base-stock policy, which can be expressed in closed form as a base-probability policy. Specifically, the order associated with a given customer should be placed if and only if its arrival probability within the leadtime is higher than a threshold. Our results generalize earlier conditions for the optimality of myopic policies. Namely, we show that myopic policies can be optimal even when the demand is correlated or stochastically decreasing.state-dependent base-stock policies, single-unit decomposition approach