This paper investigates the joint scheduling problem of unmanned aerial vehicles (UAVs) and trucks for community logistics, where UAVs act as service providers for last-mile delivery and trucks serve as mobile storage platforms for drone deployment. To address the complexity of decision variables, this paper proposes a three-stage solution scheme that divides the problem into the following: (1) UAV mission set generation via clustering, (2) truck-drone route planning, and (3) collaborative scheduling via a Mixed-Integer Linear Programming (MILP) model. The MILP model, solved exactly using Gurobi, optimizes truck movements and drone operations to minimize total delivery time, representing the core contribution. In the experimental section, to verify the correctness and effectiveness of the proposed Mixed-Integer Linear Programming (MILP) model, comparative experiments were conducted against a heuristic algorithm based on empirical intuitive decision-making. The solution results of experiments with different scales indicate that the joint scheduling model outperforms the scheduling strategies based on empirical experience across various scenario sizes. Additionally, multiple experiments conducted under different parameter settings within the same scenario successfully demonstrated that the model can stably be solved without deteriorating results when parameters change. Furthermore, this study observed that the relationship between the increase in the number of drones and the decrease in the total consumed time is not a simple linear relationship. This phenomenon is speculated to be due to the periodic patterns exhibited by the drone scheduling sequence, which align with the average duration of individual tasks.

Airport slot allocation is a key short-term solution to address airport capacity constraints, and it has long been a focus of research in the field of air traffic management. The existing studies primarily consider constraints such as airport capacity and flight operations, optimizing the slot allocation of arrival and departure flights to maximize the utilization of airport resources. This study proposes an airline fairness index based on a demand-side value system and addresses the problem of flight slot allocation by developing a tri-objective model. The model simultaneously considers the maximum slot deviation, total slot deviation, and airline fairness. Additionally, dynamic capacity constraints using rolling time windows and constraints on slot migration during peak periods are incorporated. The $\epsilon$-constraint method is employed in conjunction with a large-neighborhood search heuristic to solve a two-stage optimization process, yielding an efficient allocation scheme. The experimental results show that the introduction of rolling capacity constraints effectively resolves the issue of continuous overcapacity that arises when only a fixed capacity is considered. Additionally, the proposed airline fairness index, based on a demand-side value system, can significantly improve fairness during the slot allocation process. By sacrificing at most 16% of the total displacement, it is possible to reduce the unfairness index by nearly 80%.

The air transport system can be considered to be a complex network with airports as vertices and direct flights as edges. Research in this area contributes to the optimisation of the airline network and the sustainable development in transportation. This study chose Air China as an example to discover the dynamics of the airline network topologically. Serving as a critical agent of social and economic connections between cities, the airline network structure evolves over time. However, Air China maintains its multicentric and hierarchical structure and forms a mature point-to-point network with codeshare partners. This research also extracts key players at the airport level and investigates the topological structure of highly connected cliques. The results show that the combination of airports in the cliques may be affected by the airline capacity, traffic rights and interline cooperation. Meanwhile, smaller airports appear more often in cliques than hub airports, which can be interpreted and justified with slot limits at mega-airports. The weighted clique percolation method provides new insights to detecting overlapping communities, which can be characterized by geographical constraints. The shared vertices in the combined codeshare network indicate the possible hub shifting in the constantly changing aviation sector.

Complex networks have encouraged scholars to develop an effective method for abstracting and optimizing aviation networks. However, researchers often overlook the aviation network’s temporal attribute and treat it as a static network. Aviation networks have strong temporal characteristics and the dynamic connection cannot be realistically described by a static network. It is necessary to more accurately and realistically represent these connections during the operation of an aviation network. This study explored temporal structures of the Chinese aviation temporal network (CATN) based on flight schedules and actual operational time data. Temporal networks based on time windows were represented to analyze the temporal topology features and robustness of the CATN. The results demonstrated the following: (1) based on the spatial-temporal aviation network, there is a morning departure peak (7:00–8:00) and an evening arrival peak at the airline hub (20:00–21:00); (2) examining the centrality of each airport in the CATN at various time intervals exposed fluctuations in their rankings, which could not be identified by a static network, and (3) the robustness of the CATN was found to be unaffected by time windows, but it displayed poor resilience against deliberate attacks, particularly when subjected to betweenness and closeness attacks, which target the network’s shortest paths. For obtaining a greater understanding of the operating situation of civil aviation, displaying the topological features and robustness of the temporal network is of great importance.

Community detection in a complex network is an ongoing field. While the air transport network has gradually formed as a complex system, the topological and geographical characteristics of airline networks have become crucial in understanding the network dynamics and airports’ roles. This research tackles the highly interconnected parts in weighted codeshare networks. A dataset comprising ten major international airlines is selected to conduct a comparative analysis. The result confirms that the clique percolation method can be used in conjunction with other metrics to shed light on air transport network topology, recognizing patterns of inter- and intra-community connections. Moreover, the topological detection results are interpreted and explained from a transport geographical perspective, with the physical airline network structure. As complex as it may seem, the airline network tends to be a relatively small system with only a few high-order communities, which can be characterized by geographical constraints. This research also contributes to the literature by capturing new insights regarding the topological patterns of the air transport industry. Particularly, it reveals the wide hub-shifting phenomenon and the possibility of airlines with different business models sharing an identical topology profile.

This study develops an MTSP model for multi-UAV delivery optimization from a central hub, proposing a hybrid algorithm that integrates genetic simulated annealing-enhanced clustering with an improved Hopfield neural network to minimize the total flight distance. The proposed methodology initially employs an enhanced fuzzy C-means clustering technique integrated with genetic simulated annealing (GSA) to effectively partition the MTSP formulation into multiple discrete traveling salesman problem (TSP) instances. The subsequent phase implements an enhanced Hopfield neural network (HNN) architecture incorporating three key modifications: data normalization procedures, adaptive step-size control mechanisms, and simulated annealing integration, collectively improving the TSP solution quality and computational efficiency. The proposed algorithm’s effectiveness is validated through comprehensive case studies, demonstrating significant performance improvements in the computational efficiency and solution quality compared to conventional methods. The results show that during clustering, the improved clustering algorithm is more stable in its clustering effect. With regard to path optimization, the improved neural network algorithm has a higher computational efficiency and makes it easier to obtain the global optimal solution. Compared with the genetic algorithm and ant colony algorithm, its iteration times, path length, and delivery time are reduced to varying degrees. To sum up, the hybrid optimization algorithm has obvious advantages for solving a multi-UAV collaborative distribution path optimization problem.