Novel Applications of Optimization Models in Drone Routing and Scheduling

Drone technologies can have a positive impact on surveillance, emergency response, and delivery. Many existing optimization models in drone routing and scheduling focus on minimizing the cost or time required to complete a mission. This study explores novel applications of drones for healthcare delivery and structural inspection considering the physics of battery consumption that are often ignored in the Operations Research community. The COVID-19 pandemic has affected everyone in ways never imagined and various social distancing measures are in place to reduce the spread of viruses. If at-home testing kits are safely and quickly delivered to a patient, it can potentially reduce human contact and positively affect disease spread before, during, and after diagnosis. Hence, the first subject of this thesis proposes testing kit delivery schedules using drones based on the Mothership and Drone Routing Problem (MDRP). Optimization models and a decomposition-based solution methodology are developed to solve the complex model. The performance on virus spread reduction rate was measured by the ‘R’ method. Computational results show that the proposed approach (R = 0.002) resulted in considerably lower infection risk compared to the face-to-face testing practice (R = 0.0153). The second subject of this thesis introduces drone path planning for structural inspection considering the physics of battery consumption. The short battery duration of drones remains a major problem for small drones. Considering the shape of large structures, drones have a variety of flight dynamics during a mission, in which certain moves require a faster battery consumption than others. However, these factors have not been thoroughly considered in the existing routing models. Hence, this study examines different aspects of routing drones to cover multiple inspection points distributed on a three-dimensional structure. Both MIP models (labelled as SFD and MEC) are developed to obtain optimal routing strategies for both the shortest distance and the minimum battery consumption. Numerical results show that the optimal solutions form these two models produce different paths. Understanding that each decision maker may have different preference between those two objectives, a bi-objective optimization model has been developed to find an efficient frontier of solutions to satisfy the decision maker’s preference.