The Drone-Assisted Traveling Salesman Problem with Robot Stations

In this paper, we study the Drone-Assisted Traveling Salesman Problem with Robot Stations (TSP-D-RS). Specifically, we assume that there is a single truck that is equipped with a drone, and one or more potential sites of stations that might accommodate some robots. The TSP-D-RS asks for a valid route of the truck as well as feasible utilization of the drone and robots, such that all customers are served and minimal delivery time (makespan) or cost is accomplished. We provide a Mixed Integer Linear Programming formulation of the problem and perform a detailed numerical study. Through our numerical results, it is revealed that our formulation can be effectively addressed by a state-of-the-art solver. In addition, we demonstrate that optimizing the makespan coincides with reduced costs. In contrast, optimizing the operational costs might increase the makespan significantly. Furthermore, depending on the objective function, the operational utilization of the vehicles differs

An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem

In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO). The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops

Parallel drone scheduling vehicle routing problems with collective drones

We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behaviour of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently. In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the \emph{Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones} and we introduce for the first time the variant with multiple trucks, called the \emph{Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones}. For the latter variant, we propose two Constraint Programming models and a Mixed Integer Linear Programming model. An extensive experimental campaign leads to state-of-the-art results for the problem with one truck and some understanding of the presented models' behaviour on the version with multiple trucks. Some insights about future research are finally discussed

Trajectory Design of Laser-Powered Multi-Drone Enabled Data Collection System for Smart Cities

This paper considers a multi-drone enabled data collection system for smart cities, where there are two kinds of drones, i.e., Low Altitude Platforms (LAPs) and a High Altitude Platform (HAP). In the proposed system, the LAPs perform data collection tasks for smart cities and the solar-powered HAP provides energy to the LAPs using wireless laser beams. We aim to minimize the total laser charging energy of the HAP, by jointly optimizing the LAPs’ trajectory and the laser charging duration for each LAP, subject to the energy capacity constraints of the LAPs. This problem is formulated as a mixed-integer and non-convex Drones Traveling Problem (DTP), which is a combinatorial optimization problem and NP-hard. We propose an efficient and novel search algorithm named DronesTraveling Algorithm (DTA) to obtain a near-optimal solution. Simulation results show that DTA can deal with the large scale DTP (i.e., more than 400 data collection points) efficiently. Moreover, the DTA only uses 5 iterations to obtain the nearoptimal solution whereas the normal Genetic Algorithm needs nearly 10000 iterations and still fails to obtain an acceptable solution

On The Continuous Coverage Problem for a Swarm of UAVs

Unmanned aerial vehicles (UAVs) can be used to provide wireless network and remote surveillance coverage for disaster-affected areas. During such a situation, the UAVs need to return periodically to a charging station for recharging, due to their limited battery capacity. We study the problem of minimizing the number of UAVs required for a continuous coverage of a given area, given the recharging requirement. We prove that this problem is NP-complete. Due to its intractability, we study partitioning the coverage graph into cycles that start at the charging station. We first characterize the minimum number of UAVs to cover such a cycle based on the charging time, the traveling time, and the number of subareas to be covered by the cycle. Based on this analysis, we then develop an efficient algorithm, the cycles with limited energy algorithm. The straightforward method to continuously cover a given area is to split it into N subareas and cover it by N cycles using N additional UAVs. Our simulation results examine the importance of critical system parameters: the energy capacity of the UAVs, the number of subareas in the covered area, and the UAV charging and traveling times.We demonstrate that the cycles with limited energy algorithm requires 69%-94% fewer additional UAVs relative to the straightforward method, as the energy capacity of the UAVs is increased, and 67%-71% fewer additional UAVs, as the number of subareas is increased.Comment: 6 pages, 6 figure

Optimization Approaches for the Traveling Salesman Problem with Drone

The fast and cost-efficient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last-mile to their customers. One technology-enabled opportunity that recently has rec