Simulation results demonstrate that the proposed method significantly improves delivery efficiency and solution quality in complex mountainous environments while ensuring trajectory feasibility and operational safety, and provides a scalable and practical optimization framework for low-altitude logistics network planning under complex constraints.
Abstract
With the rapid growth in e-commerce demand, increasing pressure on same-day delivery, and rising last-mile logistics costs, UAV-based logistics systems have emerged as a promising solution for efficient transportation in complex environments. In mountainous regions, however, irregular terrain, limited infrastructure accessibility, and strict flight constraints significantly increase the difficulty of logistics planning. To address these challenges, this study proposes a two-layer collaborative optimization framework for multi-center UAV logistics delivery systems. At the lower level, a multi-center site selection model was developed to determine the optimal distribution center locations and assign task areas. A trajectory cost matrix was constructed by comprehensively considering multiple constraints. The model was solved using a hybrid strategy that combines chaotic initialization and local enhancement based on the elite saDE method to improve the Starfish Optimization Algorithm, called the Mixed-Strategy Improved Starfish Optimization Algorithm (MISFOA), thereby generating feasible three-dimensional flight trajectories between local nodes. At the upper level, an improved Adaptive Large Neighborhood Search (IALNS) algorithm is applied to perform UAV mission assignment and route scheduling within each distribution center, based on the trajectory cost matrix pre-calculated at the lower level. The proposed framework achieves effective information exchange and hierarchical coupling between center selection and scheduling at the distribution level, thereby enabling unified optimization of the multi-center location and coordinated dispatch system. Simulation results demonstrate that the proposed method significantly improves delivery efficiency and solution quality in complex mountainous environments while ensuring trajectory feasibility and operational safety. This model provides a scalable and practical optimization framework for low-altitude logistics network planning under complex constraints.
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.
Buyang Ding, Weijun Ni, Yixing Luo et al.· Electronics· 0 citations
This study investigates the integration of UAVs as an alternative transportation mode within industrial environments, focusing on the rapid delivery of critical spare parts in large-scale production facilities, and highlights the role of UAVs as a complementary transportation layer in controlled industrial networks.
Konstantinos Kolonas, S. Ponis, Michalis Fragkoulakis et al.· Future Transportation· 0 citations
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.
Zongwei Li, Guang Zhang, Heyun Gao· Journal of Vibration and Con...· 0 citations
A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population, and the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set.
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.
Yang Liu, Yi Fei, Changxi Ma· European Transport Research...· 0 citations
Simulation of three typical UAV dispatching problems shows that greedy algorithm has better optimization in resource utilization and the convergence of the simulated annealing algorithm is better under the complex constraints.
Jiaming Wang, Jing Guo, Ning Du et al.· SAE technical paper series· 0 citations