Unmanned Aerial Vehicle Assisted Healthcare Resource Allocation in Disasters

The fast response to a disaster is a key factor in rescuing victims who are trapped in the affected areas. The high amount of casualties as well as life and medical resource allocation cause the complexity of disaster rescuing. This paper concentrates on developing a multi-objective (MO) optimization model and adopts an algorithm named Probabilistic Solution Discovery Algorithm (PSDA) to generate a set of Pareto solutions on account of (i) the affected location, (ii) the number of victims in the affected location, (iii) the amount of resource, including food, water, and medicine, (iv) the location of the resource, (v) the deployment of UAVs. PSDA is used to solve the MO model, each of the Pareto solutions is an emergency rescuing strategy. A study case is provided to validate the perspectives. The results of resource allocation generated with the five aforementioned factors have confirmed the effectiveness of the proposed solution.