Coordinating drones with mothership vehicles: The mothership and drone routing problem with graphs

Amorosi, Lavinia; Puerto, Justo; Valverde, Carlos

doi:10.1016/j.cor.2021.105445

Cited by 25 publications

(16 citation statements)

References 24 publications

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“…An interesting topic is how to find an optimal balance between routes with multiple stops for pickups of cargo that may increase transport volumes and, thus, improve the cost-volume relationship, and how to assess the disadvantage of such solutions in relation to less time savings. There is rich literature on optimizing complex drone routes [29,30,[57][58][59][60]. Although most of these studies have addressed current short-range electric drones, the principles applied are also appropriate if drone route optimization intends to cover more service locations with more powerful drones in the future.…”

Section: Discussionmentioning

confidence: 99%

A Conceptual Approach to Time Savings and Cost Competitiveness Assessments for Drone Transport of Biologic Samples with Unmanned Aerial Systems (Drones)

Johannessen

2022

Drones

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Unmanned aerial vehicles (UAVs, drones) are expected to save transport time and improve service reliability for transport of biologic samples, but few studies have evaluated the potential time savings of such services. The total transport time defined as time from sample ready for transport until arrival at the laboratory was used to assess the absolute and relative time savings of drones compared with ground transport, using ground distances from 4–7 km (urban model) to 179–262 km (rural district routes) with one to eight daily scheduled trips. Costs of existing ground transport were allocated to drone flight times as a proxy for drone cost competitiveness. Time savings were less than 20–30% in the urban model but 65–74% in the rural routes using drone speeds of 100 km/h, but the time between trips (route frequencies) and drone speeds influenced the relative time savings substantially. Cost of time gains per number of samples was less favorable using drones in the rural models due to lower transport volumes. This research concludes that drone solutions provide marginal gains for short-distance transports, whereas time savings are more promising in long transport models with appropriate scheduling and sufficiently high drone speeds.

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Section: Discussionmentioning

confidence: 99%

A Conceptual Approach to Time Savings and Cost Competitiveness Assessments for Drone Transport of Biologic Samples with Unmanned Aerial Systems (Drones)

Johannessen

2022

Drones

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“…The [25] approach is generally simplistic, does not maximize coverage, and does not account for the impact of wind. And, finally, the work [26] analyzes a mother ship-orchestrated UAV swarm for wireless communications, where the goal is to minimize the overall weighted distance traveled by the mother ship for UAV recharge. This work does not provide a realistic overview of the capital expenses, and omits the impact of wind.…”

Section: Uavs A) Service Scenariomentioning

confidence: 99%

Sustainable Wireless Services With UAV Swarms Tailored to Renewable Energy Sources

Donevski

Virgili

Babu

et al. 2023

IEEE Trans. Smart Grid

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Unmanned Aerial Vehicle (UAV) swarms are often required in off-grid scenarios, such as disaster-struck, war-torn or rural areas, where the UAVs have no access to the power grid and instead rely on renewable energy. Considering a main battery fed from two renewable sources, wind and solar, we scale such a system based on the financial budget, environmental characteristics, and seasonal variations. Interestingly, the source of energy is correlated with the energy expenditure of the UAVs, since strong winds cause UAV hovering to become increasingly energy-hungry. The aim is to maximize the cost efficiency of coverage at a particular location, which is a combinatorial optimization problem for dimensioning of the multivariate energy generation system under non-convex criteria. We have devised a customized algorithm by lowering the processing complexity and reducing the solution space through sampling. Evaluation is done with condensed real-world data on wind, solar energy, and traffic load per unit area, driven by vendor-provided prices. The implementation was tested in four locations, with varying wind or solar intensity. The best results were achieved in locations with mild wind presence and strong solar irradiation, while locations with strong winds and low solar intensity require higher Capital Expenditure (CAPEX) allocation.

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“…First, in logistics, constrained by load weights and endurance distances, drones generally coordinate with trucks or other kinds of motherships. Second, the drone routing problems are generally formatted as IP [27,28] and MILP [26,[29][30][31][32][33][34][35][36][37][38] models. Considering various performance metrics, e.g., cost, distance, energy consumption, and consumer satisfaction, the drone routing problems can be formulated and analyzed by multi-objective optimization models [39,40].…”

Section: Drone-based Routing Problemsmentioning

confidence: 99%

“…Considering various performance metrics, e.g., cost, distance, energy consumption, and consumer satisfaction, the drone routing problems can be formulated and analyzed by multi-objective optimization models [39,40]. Fourth, the drone routing problems generally couple drones and other devices, e.g., trucks [26,31,[36][37][38][39][40][41][42] and motherships [33,34], which makes the models challenging in algorithm development. Various algorithms, including meta-and math-heuristics [29][30][31][32][33][34][35], have been studied considering the problem and model features.…”

Section: Drone-based Routing Problemsmentioning

confidence: 99%

“…Fourth, the drone routing problems generally couple drones and other devices, e.g., trucks [26,31,[36][37][38][39][40][41][42] and motherships [33,34], which makes the models challenging in algorithm development. Various algorithms, including meta-and math-heuristics [29][30][31][32][33][34][35], have been studied considering the problem and model features.…”

Section: Drone-based Routing Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Drone-Based Emergent Distribution of Packages to an Island from a Land Base

Tian

et al. 2023

Drones

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An island logistics system is vulnerable in emergency conditions and even isolated from land logistics. Drone-based distribution is an emerging solution investigated in this study to transport packages from a land base to the islands. Considering the drone costs, drone landing platforms in islands, and incorporation into the island ground distribution system, this study categorizes the direct, point-to-point, and cyclic bi-stage distribution modes: in the direct mode, the packages are distributed from the drone base station to the customers directly by drones; in the point-to-point mode, the packages are transported to the drone landing platform and then distributed to the customers independently; in the cyclic mode, the packages are distributed from a drone landing platform by a closed route. The modes are formulated, and evaluation metrics and solution methods are developed. In the experiments based on an island case, the models and solution methods are demonstrated, compared, and analyzed. The cyclic bi-stage distribution mode can improve drone flying distance by 50%, and an iterative heuristic algorithm can further improve drone flying distance by 27.8%, and the ground costs by 3.16%, average for the settings of twenty to sixty customers and two to four drone landing platforms. Based on the modeling and experimental studies, managerial implications and possible extensions are discussed.

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Coordinating drones with mothership vehicles: The mothership and drone routing problem with graphs

Cited by 25 publications

References 24 publications

A Conceptual Approach to Time Savings and Cost Competitiveness Assessments for Drone Transport of Biologic Samples with Unmanned Aerial Systems (Drones)

A Conceptual Approach to Time Savings and Cost Competitiveness Assessments for Drone Transport of Biologic Samples with Unmanned Aerial Systems (Drones)

Sustainable Wireless Services With UAV Swarms Tailored to Renewable Energy Sources

Drone-Based Emergent Distribution of Packages to an Island from a Land Base

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