Cyclic Stochastic Inventory Routing Planning with Reorder Points and Recourse Decision for an Application in Medical Supply

Kurzfassung/Abstract

Drug availability in clinics is essential for patient services, whose demand for medication on the other hand is uncertain. Thus, clinics need to have a variety of medication available, which lead to high inventory holding costs. In Germany, it is common for a larger central clinic to take over the procurement of drugs and distribute them to smaller surrounding clinics, which results in a two-echelon network structure. The clinics, however, operate according to their own inventory policy as they plan independent of each other. Additionally, these inventory policies include instant replenishment orders to avoid shortages. As these instant replenishment orders only include few medication, these can be performed by a variety of vehicles, including vans, cabs, and aerial drones.

We present a two-stage stochastic program for a multi-product two-echelon inventory routing problem with stochastic demands under consideration of inventory policies. We decide on the cost-optimal cyclic delivery patterns and reorder points for the clinics with instant replenishment orders performed by drones as recourse decision in case of shortages. Due to the problem’s complexity caused by the interdependencies of inventory policies in the two echelons, we develop a specialized adaptive large neighborhood search that includes an algorithm for determining local optimal reorder points for the routing in each iteration. We present a case study at a large German clinic, which is responsible for serving surrounding clinics and plans to integrate drone instead of truck deliveries as emergency resupply decision. Our integrated approach not only leads to cost savings of 58.4% for the surrounding clinics but also 15.9% for the central clinic. By using drone delivery compared to van delivery, average stock of medication at surrounding clinics can be reduced by at least 1.7% to 100% for expensive and rarely needed medication, which results in a total cost decrease of 29.8% while maintaining medication availability.