Models and Algorithms for Distribution Problems with Uncertain Demands

Abstract

We consider the problem of distributing goods from one or more plants through a set of warehouses in anticipation of forecasted customer demands. Two results are provided in this paper. First, we present a methodology for approximating stochastic distribution problems that are computationally tractable for problems of realistic size. Comparisons are made to standard deterministic formulations and shown to give superior results. Then, we compare logistics networks with varying degrees of redundancy represented by the number of warehouses which serve each customer. Overlapping service regions for warehouses provides additional flexibility to handle real-time demands. We quantify the expected savings that might result from such strategies.