Inverting the TruckDrone Network Problem to Find Best Case Configuration Many industries are looking for ways to economically use truckrailship fitted with drone technologies to augment the xe2x80x9clast milexe2x80x9d delivery effort. While drone technologies abound, few, if any studies look at the proper configuration of the drone based on significant features of the problem: delivery density, operating area, drone range, and speed. Here, we first present the truckdrone problem and then invert the network routing problem such that the best case drone speed and range are fitted to the truck for a given scenario based on the network delivery density. By inverting the problem, a business can quickly determine the drone configuration (proper drone range and speed) necessary to optimize the delivery system. Additionally, we provide a more usable version of the truckdrone routing problem as a mixed integer program that can be easily adopted with standardized software used to solve linear programming. Furthermore, our computational metaheuristics and experiments conducted in support of this work are available for download. The metaheuristics used herein surpass current bestinclass algorithms found in literature.

Routing a Fleet of Automated Vehicles in a Capacitated Transportation Network Routing of a fleet of automated unitoccupancy vehicles in a capacitated transportation network is an emerging problem that needs to be addressed to realize largescale automated transportation systems. We adopt an existing networkflowbased model for the problem and present a new reformulation based on DantzigWolfe decomposition. This reformulation allows us to apply the column generation solution technique which, in turn, enables us to solve largescale problem instances with tens of thousands of requests on networks with thousands of links. We empirically compare our method to the stateoftheart approach on several standard benchmark instances and find that the computational time of our solution approach scales qualitatively better in all tested problem instance parameters: namely, in the size of the transportation network, in the magnitude of demand intensity, and in the number of demand flows.