Express, LTL express and vehicle transport are the three main modes of transport in the road freight industry. Express customers are extremely scattered and have timeliness requirements and market barriers higher than LTL express and vehicle transport. The current concentration is far ahead of the vehicle transport and LTL-express market ^[1][2][1,2]. LTL express mainly covers 30–3000 kg of goods, including industrial products, semi-finished products, parts and components, etc. The customers are mainly enterprise-type customers such as manufacturing enterprises, distributors and retailers. Customers are scattered, and logistics service providers need to provide services such as loading and carpooling, transshipment distribution, and distribution. The process is more complicated than vehicle transportation, and the market concentration is low grade ^[3][4][3,4].

LTL express represents a large market and highly dispersed market pattern. In 2023, China’s road freight logistics market will reach RMB 6 trillion, accounting for 50% of China’s logistics costs. Of this, the market size of express delivery, less-than-truckload express and vehicle transportation is about RMB 0.9 trillion, RMB 1.5 trillion and RMB 3.2 trillion, respectively. The average annual compound growth rate in the past five years was 30, 5.6 and 0.6% [3], respectively. The less-than-truckload express market is highly fragmented, with more than 200,000 market participants [5]. However, the unified market of less-than-truckload is not enough, and the allocation of logistics resources is unreasonable and insufficient. The multimodal transportation system requires improvement, particularly in addressing the low efficiency levels related to cross-mode transport and cross-operation link convergence and conversion; the standardization of transport units is not high. The whole-chain operation efficiency is not satisfactory, and the cost is huge. The stock logistics infrastructure network’s support and guidance capability for the industrial layout and domestic demand consumption under the new development pattern is not adequate [6]. Addressing the need for adaptability in logistics services requires tackling the challenges of surplus low-end service supply and shortage of high-end service supply ^[7][8][9][7,8,9]. The freight transport structure needs to be optimized, as the proportion of long-distance transport of bulk road cargo is still high. The logistics industry operates at a large scale, yet it has not fully realized the benefits of economies of scale. This is especially true in the highway freight market, where competition is highly homogeneous and unfair practices are prevalent. As a result, the industry’s level of competitiveness needs to be improved. The modern logistics system is not organized, intensive, networked or socialized, and the backbone logistics infrastructure network is not optimal. The LTL-express transportation network location problem (LTLTNLP) needs to be solved [10].

2. Urban Distribution Network Location Problem

Most of the research on the allocation system has been carried out in the urban allocation system from the perspective of social benefit and enterprise benefit. With the development of e-commerce and information technology, the logistics center can usually achieve the purpose of efficient operation through the information operation of the enterprise [11], thereby reducing the unnecessary waste of resources caused by distribution, and saving costs to the enterprise itself. With the process of urbanization, the number of urban distribution vehicles and the distribution distance increases [12], which leads to the need for multiple objects in the urban distribution system to cooperate to achieve system optimization. On the one hand, with the deepening of joint distribution research, several scholars have established joint distribution systems for different industries and made preliminary progress [13]. The concept of distribution is used in terminal distribution transactions in e-commerce. On the other hand, the construction of terminal joint distribution stations can promote the sustainable development of terminal distribution networks [14]. Yuan [15] used data from the Los Angeles area of the United States; they examined the spatial distribution of warehouses and adjacent facilities related to different demographic and socio-economic characteristics, and found that the relationship between storage facilities and activities in the urban distribution networks and household income was uneven. Yang et al. [16] showed that research on the economic attributes of freight, land use, road infrastructure and road-network variables can help transportation planners to understand truck dynamics related to traffic safety [17] and develop operational measures to mitigate the impact of growth. The existing research on the location of urban logistics facilities and their impact on externalities often focuses on the start and end of freight activities within the metropolitan area, while ignoring the role of freight with origin or destination outside the metropolitan area [18]. Sakai et al. [19] applied data from the Paris region of France to collect characterization data on the location and activities of logistics facilities, revealing the key location characteristics that affect the location of logistics facilities, such as zoning regulations, wholesale work accessibility, population density and highway accessibility, and the heterogeneity of the impact of these characteristics on activity categories.

3. The Location Problem of Outlets in Urban Distribution Network

Up to now, researchers have made significant progress in the study of distribution-outlet location selection. Specifically, they have fully demonstrated the various factors and costs that affect the location selection of distribution outlets. Additionally, they have applied various technical methods to optimize the location-selection model and enhance the efficiency of the distribution network. Location cost has been resolved. In the static location problem of the logistics network, scholars applied both qualitative and quantitative methods. At the method level, a multicriteria decision-making method for urban network location planning under uncertainty is proposed, which can provide decision-makers with a network location method reference at the network construction level [20]. The location model constructed by mathematical equations can be applied to the location optimization of the urban logistics network, providing a good application method for intercity network coordination [21]. The logistics industry has undergone a number of innovations, including transportation algorithms, distribution equipment. At the same time, the warehousing industry has experienced changes in the scale and location of facilities, for instance, the establishment of a large number of large warehouses in the suburbs of the city [22]. With the rise of the city circle, many warehouse layouts have changed, and the environmental impact associated with warehousing activities has been increasing in recent decades. This research focuses on the uneven distribution of warehousing facilities in disadvantaged communities and explores how this difference is caused by the interaction between various socio-economic processes [23]. Holl and Mariotti [24] analyzed the impact of highway development on the performance of Spanish logistics sector enterprises, and analyzed the panel data with instrumental variable estimation. The results showed that the highway has a significant impact on the performance of logistics enterprises, but it has important spatial heterogeneity.

4. The Impact of Urban Distribution Nodes on the Network

Scholars have begun to pay attention to the influence of distribution network nodes and the relationship between nodes on the network structure, and quantitatively analyze the relationship between vehicles through social network analysis theory and complex network theory. Basically, study of the road network to deepen the understanding of the network structure [25]. Intercity vehicle traffic strategies and traffic management, as well as an effective emergency facility layout and rational configuration can provide new ideas for logistics enterprises [26] for cost savings in network transportation by identifying the most critical transportation routes in the urban logistics network [27]. In the study of network dynamic positioning, for example, heuristic algorithms are often used to solve discrete dynamic network localization problems [28]. The localization of hybrid facilities is also used to verify the effectiveness of the algorithm [29]. Scholars can design by the method of uncertain dynamic location problem [29], which greatly enriches the solutions of logistics location selection. The main quantitative methods for solving the location problem with the different constraints mentioned above include the gravity center method [30], transportation planning method [31], cluster method ^{[32][33][34][35]}[32,33,34,35], CFLP method [36], etc. However, most of the scholars use a heuristic algorithm or heuristic extension algorithm [37]. The literature mainly studies the optimization of dynamic and static urban network layouts and multi-objective optimization, and the optimization of the urban network layout optimization model algorithm. There are few studies on the location of the urban network of LTL-express enterprises. The existing research on the optimization of the urban network layout provides the basis of this paper, and more research is urgently needed on the optimization of the urban network layout for LTL-express enterprises. With the consideration of the various influencing factors of the LTL-express enterprises in the common distribution environment, the minimization of the total distribution cost was taken as the goal, and the current situation of network construction was taken as the constraint condition of network location selection. At the same time, the search range of the initial antibody group was expanded, and the random probability of the selection of cross and diversity evaluation parameters was set, which improved the traditional elite individual retention strategy. Finally, an example was given to verify the accuracy and effectiveness of the proposed algorithm.