# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 2 State of the Art  
To date, research on common distribution networks has mainly focused on traditional ground distribution facilities, and most of them carry out theoretical modeling with distribution timeliness and economy as the main objectives. [PERSON] pointed out the regional characteristics of logistics centers (which are used to perform regional logistics activities, such as storage, transportation, and infrastructure services, and whose layout planning is affected by cost and benefit issues), proposed the three-phase method for solving the location of logistics centers, and analyzed the robustness of the algorithm [2]. [PERSON]. [PERSON] et al. proposed cross-docked logistics and transportation modes, and for this type of logistics and distribution modes, a modern multi-criteria group decision making (MCGDM) model was proposed to solve the results of calculating the layout of cross-docked logistics centers [3]. [PERSON] et al. first used the K-means clustering algorithm, customer clustering, to solve pickup and delivery point siting and customer group division [4]. Next, on the basis of the optimal solution, they built a single-vehicle-yard multi-model collection and distribution integrated VRP model using a scanning algorithm and an ant colony algorithm to solve the problem of vehicle distribution routes [4]. [PERSON] comprehensively investigated factors influencing the operating costs and resilience of the network based on common distribution and the integration and reorganization of courier terminal distribution resources and established a courier terminal common distribution service center multi-objective siting model [5]. Based on complex network theory to determine the importance of alternative nodes, [PERSON] established a multi-objective siting model for the urban express terminal crowdsourcing service station by considering three objectives, namely the enterprise operation cost, customer satisfaction, and carbon emission, and proposed a genetic algorithm for model solving [6]. These studies provide a theoretical basis for the distribution center-siting problem, but none of them consider the convergent siting of UAV distribution centers. Research related to the siting of UAV logistics and distribution facilities has mainly considered pure UAV airport siting. [PERSON]. [PERSON] et al. argued that drone logistics is an important avenue for surface traffic congestion and developed a landing site-siting model that targeted the total cost, took into account drone endurance and multiple takeoff and landing facilities, and validated it using San Francisco as a case study [7]. [PERSON] et al. concluded that mixed-integer programming performs well in the generalized facility-siting problem and used mixed-integer programming to optimize the siting of logistics drone vertical-landing sites with and without capacity constraints [8]. [PERSON] et al. examined vertical-takeoff and vertical-landing electric UAVs and considered multiple distribution modes. They optimized the siting of takeoff and landing locations under the premise of a restricted number of such sites to maximize demand coverage [9]. [PERSON] et al. focused on the airport-siting problem of UAV urban instant delivery and established a mixed-integer planning model for UAV fully automated airport siting. They used the improved regular grid method for pre-siting and optimized the solution through Tyson partitioning [10]. [PERSON] et al. established a two-layer planning model for the site-path optimization of UAV delivery relay stations on islands and combined K-means clustering with an improvedsimulated annealing algorithm to solve the problem [11]. [PERSON] et al. introduced airspace environmental constraints in establishing their multilevel-logistics UAV landing point site selection and allocation model [12; 13]. [PERSON] et al. designed a drone delivery service system based on drone delivery preference survey data and evaluated the benefits of the delivery enterprise through preliminary financial analysis.