Towards Intelligent Aerial Logistics: A UAV Routing Algorithm for Industrial Transportation Networks

TeCoR-UAV: A Two-Stage Topology Extraction and Cooperative Routing Algorithm for Low-Altitude Logistics

TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application.

Advances in truck–drone cooperative delivery routing and coordination: a review

Unmanned Aerial Vehicle (UAV)–vehicle cooperative delivery has emerged as a promising paradigm for addressing the growing complexity of urban and regional logistics. By combining the capacity and network coverage of ground vehicles with the flexibility and accessibility of UAVs, such systems offer new opportunities to improve delivery efficiency, responsiveness, and sustainability across diverse operational contexts. In recent years, research in this field has expanded rapidly, producing a wide range of models, algorithms, and application-driven studies. This paper provides a comprehensive review of UAV–vehicle cooperative delivery research, synthesizing advances in system configurations, coordination mechanisms, modeling approaches, and solution methodologies, including optimization-based, heuristic, hybrid, and learning-driven frameworks. Empirical evidence from major application domains, such as urban parcel logistics, emergency and humanitarian operations, infrastructure inspection, and on-demand services, is examined to reveal how system design and operational conditions jointly shape performance. Beyond summarizing existing work, this review identifies fundamental modeling, operational, and sustainability challenges that continue to hinder large-scale deployment, including synchronization fragility, energy and uncertainty management, regulatory constraints, and scalability. By consolidating fragmented research strands into a unified analytical perspective, this review aims to support the transition of UAV–vehicle cooperative delivery from isolated pilot studies toward robust, scalable, and policy-relevant transportation systems.

Multi-UAVs cooperative task allocation and path planning for low-altitude logistics

To address the challenges of collaborative task allocation and path planning for multiple logistics unmanned aerial vehicles (UAVs) in urban low-altitude environments, this paper proposes a bilevel nested joint optimization method based on reinforcement learning and a graph search algorithm to enhance the efficiency of collaborative last-mile delivery by multiple logistics UAVs while reducing flight risks. The proposed method constructs a bilevel architecture system based on a task allocation and decision-making model and a path planning model. The upper-level model holistically considers the demands of three stakeholders—government (safety), customers (timeliness), and UAV enterprises (economy)—at the macro level. Then, based on real-time order information and UAV status, a multi-objective optimization and constraint model is constructed under complex dynamic environments. A multi-agent proximal policy optimization algorithm is employed to achieve rapid dynamic task allocation and decision-making. The lower-layer model utilizes the upper-level allocation results combined with detailed environmental information to plan safe and efficient flight paths for each UAV at the micro level. It employs an improved jumping-point search algorithm for refined path optimization. A loop feedback mechanism is designed to facilitate information exchange between layers, thereby coupling the task allocation and path planning processes to achieve collaborative optimization of upper- and lower-level task allocation and decision-making. This method effectively addresses complex logistics delivery scenarios, enhancing the overall efficiency and robustness of the delivery system. Simulation experiments comprehensively consider path influences from flexible open-area delivery, varying numbers of distribution centers and UAVs, and on-demand rush orders. Tests conducted in medium- and high-density environments demonstrate the proposed model and algorithm’s significant superiority in dynamic complex scenarios. Even when confronted with complex environments and dynamic order scenarios, it consistently generates highly applicable UAV flight paths.

An Optimization of the Two-Echelon Vehicle Routing Problem with Drones Considering Truck Travel Time and Carbon Emissions

The Two-Echelon Vehicle Routing Problem with Drones (2E VRP-D) model can initiate flights from the truck, complete several deliveries to different customer locations, and then rendezvous with the truck again. In addition to economic benefits, logistics providers must consider the environmental impacts of the order-fulfillment process. A novel multi-objective optimization framework is established in this study to simultaneously minimize the total time required for truck travel while also reducing total carbon emissions. Due to restrictions in payload capacity and battery energy limits, drones need to work alongside trucks to deliver services effectively. A dynamic energy consumption model is applied for the drone, where energy use changes based on the loading rate, enabling a more realistic representation of actual operations. This complex problem is addressed using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) with two approaches: the Giant Chromosome (GC) and K-means methods. Routing plans for both trucks and drones are then constructed using a novel heuristic algorithm. Overall, the K-means method delivers better average objective values, reflecting enhanced exploitation performance. Conversely, the GC method produces a higher Hypervolume (HV), indicating superior convergence and coverage of the Pareto front, supported by a lower spacing value, while K-means achieves a slightly better spread. These outcomes contribute to improving logistics operations and informing government policy decisions.

Optimizing multi-UAV mission scheduling for army logistics supply chains using metaheuristic algorithms

The increasing integration of Unmanned Aerial Vehicles (UAVs) into US Army logistics operations has introduced complex multi-objective scheduling challenges that conventional optimization methods struggle to address efficiently. This study presents a metaheuristic-based optimization framework for scheduling fleets of military UAVs tasked with last-mile resupply missions in dynamic, contested operational environments. A hybrid approach combining a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is proposed to minimize total mission completion time, fuel consumption, and operational risk while satisfying strict military delivery constraints. The model incorporates stochastic demand, no-fly-zone avoidance, payload capacity limitations, and UAV endurance parameters derived from US Army field logistics doctrine. Computational experiments were conducted on simulated battlefield scenarios with fleet sizes ranging from 5 to 50 UAVs across terrain grids representing forward operating bases. The proposed GA-ACO hybrid achieved an average improvement of 23.4% in mission completion time and 18.7% in fuel efficiency compared with single-algorithm baselines, while reducing the constraint violation rate to 1.8%. The framework also demonstrated superior adaptability to real-time route re-planning under dynamic threat scenarios and converged within the 10-minute Army tactical planning window for fleets of up to 50 UAVs. These findings suggest that hybrid metaheuristic optimization offers a robust and scalable approach to multi-UAV mission scheduling, with significant implications for enhancing Army supply-chain agility and operational readiness.

Genetic programming-assisted automatic heuristics design for dynamic vehicle routing with drones considering time window