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.