Drone Network Design for Emergency Resupply of Pharmacies and Ambulances

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Abstract
For patients' treatments, pharmacies and ambulances are equipped with multiple drugs. If there is a medication shortage, the pharmacies are replenished via emergency deliveries, which is a road-based transport. On the other hand, ambulances transport the patient to a nearby hospital as fast as possible. These processes result in high costs, emissions, and delays in patients' treatments. They can be improved by aerial drones transporting the required medication to the pharmacy or ambulance in the field. Drones have great potential here as only a few medications need to be transported, and drones can travel fast, at low variable costs, and emissions caused, but contrary, drones incur high fixed costs. We consider a problem setting where a wholesaler, who is the supplier of multiple pharmacies, plans to integrate drones for emergency delivery of medicines to serve both pharmacies and ambulances at service. We evaluate varying drone network designs for emergency resupply with economic, ecological, and service-oriented key performance indicators. The drone network design includes the decision on the number of drones, batteries, transport boxes, and the location of spare batteries. Based on a case study, we apply a management decision support framework proposed in the literature to define and solve our drone network design problem. Within this framework, a discrete-event simulation model is developed to evaluate various design alternatives. The model accounts for stochastic seasonal demands and weather conditions, as well as daylight-dependent flight bans for drones. We give detailed insights on the relevant stakeholders and the supply chain of the emergency resupply of pharmacies and ambulances and present multiple managerial insights. With unchanged demand, cost savings of up to 19.6% are achieved with a supply rate and service level of at least 91.4%. If, on the other hand, demand increases and drone properties are improved, dependent on the drone network, costs can be reduced by up to 51.7% with a supply rate and service level of more than 99%.

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