Optimization of Drone and Truck Operations for Socially Optimal Disaster Relief

In post-disaster scenarios, the distribution of relief supplies is a very complex process due to lacking the relief supplies and transportation means to completely and promptly satisfy survivors’ needs. The recent technological development of unmanned aerial technologies positions drones as potentially advantageous technology to support disaster relief distribution processes. Despite their limited capacity to carry goods, drones can access remote areas and attain faster speeds than road vehicles typically used to deliver relief supplies. This thesis develops a mixed-integer non-linear model comprising the routing, location, and allocation decisions for the distribution of relief supplies to survivors. The mathematical formulation minimizes a social cost function comprising logistic and suffering costs. The former costs are associated with trucks and drones transporting relief supplies and setting up points of distribution (PODs). The latter costs are associated with survivors’ mobility and deprivation. Supplies can be transported via multiple deliveries from distribution centers to PODs. Survivors have heterogeneous needs based on their vulnerability. Individuals who cannot walk to their preferred POD stay at their initial location and have the supplies delivered. Trucks can also carry drones to PODs, and drones can independently fly to the staying survivors. The formulation integrates multiple complex parts, such as the hybrid vehicle fleet, deprivation costs, and survivor mobility. This research contributes to disaster relief distribution operations by introducing a drone fleet to complement the traditional truck network, further prioritizing vulnerable survivors and reducing social costs.