Material storage warehouse-distribution center-demand point coupling optimization-oriented material distribution center site selection method and device

Abstract

The invention discloses a material storage warehouse-distribution center-demand point coupling optimization-oriented material distribution center site selection method and device, and relates to the field of traffic road networks, and the method comprises the steps: generating a plurality of blocks according to road network data; constructing a demand point driven multi-distribution center site selection model by taking the block as a demand point of material distribution and taking the lowest total transportation cost from the distribution center to the demand point as a target; constructing a multi-distribution-center site selection optimization model by taking the service bearing capacity difference minimization of the distribution centers as a target; constructing a multi-distribution center site selection model driven by dynamic material allocation by taking the lowest transportation cost of dynamic material allocation as a target; and integrating the models and carrying out optimization solution to obtain the optimal position of each distribution center. According to the method, the optimal site selection scheme of the distribution center of the research area is determined from multiple aspects of the transportation cost from the distribution center to the demand point, the service bearing capacity difference of the distribution center and the transportation cost of dynamic material allocation.

Description

Technical Field

[0001] The present application relates to the field of transportation road networks, and in particular to a material distribution center site selection method and device for material storage depot-distribution center-demand point coupling optimization. Background Art

[0002] The dynamic dispatch of emergency relief supplies during urban emergencies has become a critical issue that urgently needs to be addressed. Given the vast and complex nature of urban systems, the dynamic allocation and efficient dispatch of various emergency supplies during major public health emergencies is a crucial fundamental scientific issue that requires urgent research. The location of distribution centers is crucial for the efficient dispatch of emergency supplies.

[0003] Traditional distribution center location selection methods primarily consider the relationship between distribution centers and demand points and determine the distribution center's location by controlling transportation costs. Research on distribution center location selection is increasingly active, employing methods such as the center of gravity method, fuzzy clustering, and artificial neural networks to analyze the problem of distribution center location under various constraints. The center of gravity method is not suitable for selecting a single distribution center; location selection models proposed from a market competition perspective are susceptible to market dynamics; randomization is used to establish stochastic mathematical models, but the solution is difficult; fuzzy optimization models proposed from a nonlinear multi-objective optimization perspective are computationally expensive; and bi-level programming models and heuristic algorithms pose difficulties in coordinating the upper and lower levels. In summary, existing location selection methods are unable to meet the complex needs of logistics. The development of new, efficient, and adaptable distribution center location selection technologies is urgent to improve the efficiency and competitiveness of logistics distribution. Summary of the Invention

[0004] The purpose of this application is to provide a material distribution center site selection method and device for material storage warehouse-distribution center-demand point coupling optimization, which can accurately determine the optimal site selection plan for the material distribution center in the study area.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization, including:

[0007] Obtaining road network data of a study area, and generating a plurality of non-overlapping blocks based on the road network data; the blocks are polygons surrounded by at least one main road edge and a plurality of non-main road edges;

[0008] Taking the generated blocks as the demand points for material distribution and the goal of minimizing the total transportation cost from the distribution center to the demand points, a multi-distribution center location selection model driven by demand points is constructed.

[0009] With the goal of minimizing the differences in the service carrying capacity of distribution centers, a multi-distribution center location optimization model is constructed; the service carrying capacity of the distribution center is calculated based on the betweenness of the distribution center and the scale of material distribution;

[0010] Based on the spatial distribution of material storage depots and demand points in the study area, and with the goal of minimizing the transportation cost of dynamic material allocation, a multi-distribution center location selection model driven by dynamic material allocation is constructed. The transportation cost of dynamic material allocation includes the total transportation cost from material storage depots to distribution centers and the total transportation cost from distribution centers to demand points.

[0011] By integrating the multi-distribution center location selection model driven by demand points, the multi-distribution center location optimization model, and the multi-distribution center location selection model driven by dynamic material allocation, a multi-distribution center location selection model for coupled optimization of material storage depots, distribution centers, and demand points was derived.

[0012] The optimization algorithm is used to solve the multi-distribution center location selection model for material storage depot-distribution center-demand point coupling optimization, and the optimal location scheme for each distribution center is obtained.

[0013] In the second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned material distribution center site selection method for coupling optimization of material storage depots, distribution centers, and demand points.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization.

[0015] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned material distribution center site selection method for material storage warehouse-distribution center-demand point coupling optimization.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] This application provides a material distribution center site selection method and device for material storage depot-distribution center-demand point coupling optimization. It not only focuses on the connection between distribution center and demand point, but also considers the impact of material storage depot on distribution center. At the same time, it also takes into account the differentiation of service carrying capacity of distribution center, dynamically combines the betweenness of distribution center with distribution scale, quantifies the hub role and task load of distribution center in the road network, and realizes the balanced distribution of tasks among multiple distribution centers. This application determines the optimal site selection plan for distribution center in the study area from multiple aspects, including the transportation cost from distribution center to demand point, the difference in service carrying capacity of distribution center and the transportation cost of dynamic allocation of materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is an application environment diagram of a material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization in one embodiment of the present application;

[0020] Figure 2 A flow chart of a material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization provided in one embodiment of the present application;

[0021] Figure 3 A schematic diagram of the technical concept of a material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization provided in one embodiment of the present application;

[0022] Figure 4 A schematic diagram of a technical route for a material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization provided in one embodiment of the present application;

[0023] Figure 5 A block diagram provided for an embodiment of the present application;

[0024] Figure 6 A distribution center site selection plan determined based on a traditional distribution center site selection model provided in one embodiment of the present application;

[0025] Figure 7 A distribution center location plan determined by the distribution center location model proposed in this application, provided in one embodiment of this application;

[0026] Figure 8A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] Developing a multi-distribution center location model that supports the topological coupling and analysis of urban material storage depots, distribution centers, material demand points, and the underlying road network is a key scientific topic in fields such as geographic information systems (GIS), operations research, and transportation logistics. The location of distribution centers is particularly crucial for the dispatch of emergency supplies throughout the city, especially for large settlements subject to mandatory control measures. The location of distribution centers fundamentally alters the network's topology, thereby determining the efficiency of its logistics.

[0029] GIS technology offers significant advantages in spatial network analysis, particularly site selection, and is widely used in urban facility site selection. Within the field of geographic information science, facility site selection is a key focus of GIS network analysis, used to address the problem of maximizing service areas and employing algorithms such as Genetic Algorithms (GAs). Emergency service site selection integrates multiple criteria using GIS and multi-criteria decision-making methods. With the development of optimization theory, advanced intelligent algorithms are being used for site selection. While extensive research has utilized operations research and GIS theory to address distribution center site selection, the relationships and interactions between various elements within complex urban systems (such as road network information, distribution of material reserves, distribution of demand points, and population size and structure at these points) remain under investigation. This requires in-depth analysis from a complex systems perspective, and complex networks provide the theoretical and methodological support for this purpose. Leveraging the significant advantages of complex network theory in complex system modeling, network structure, and dynamic behavior analysis will provide scientific support for the rational planning of distribution centers.

[0030] This application focuses on the site selection of multiple urban emergency supply distribution centers and proposes a comprehensive approach. In addition to using GIS technology to extract neighborhood information from the city's basic road network, it also introduces the concept of distribution service carrying capacity as a supplement to build a model and establish an objective function to optimize site selection. Furthermore, it considers the distribution of material reserves and establishes a material allocation model. This coupled three-pronged model for distribution center site selection accurately determines the optimal site for a distribution center.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] The material distribution center site selection method for material storage warehouse-distribution center-demand point coupling optimization provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal communicates with the server via a network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated with the server, or placed in the cloud or on other servers. The terminal can send the road network data of the study area to the server. After receiving the road network data, the server generates several non-overlapping blocks based on the data. Using the generated blocks as demand points for material distribution, the server constructs a demand-point-driven multi-distribution center location model with the goal of minimizing the total transportation cost from distribution centers to demand points. A multi-distribution center location optimization model is constructed with the goal of minimizing the service carrying capacity differences between distribution centers. Based on the spatial distribution of material reserves and demand points in the study area, a multi-distribution center location model driven by dynamic material allocation is constructed with the goal of minimizing the transportation cost of dynamic material allocation. The demand-point-driven multi-distribution center location model, the multi-distribution center location optimization model, and the multi-distribution center location model driven by dynamic material allocation are integrated to derive a multi-distribution center location model for coupled optimization of material reserves, distribution centers, and demand points. The multi-distribution center location model for coupled optimization of material reserves, distribution centers, and demand points is solved using an optimization algorithm to determine the optimal location solutions for each distribution center. The server can then provide feedback to the terminal on the optimal location solutions for each distribution center. In addition, in some embodiments, the material distribution center site selection method for material storage warehouse-distribution center-demand point coupling optimization can also be implemented independently by the server or terminal. For example, the terminal can directly perform material distribution center site selection for material storage warehouse-distribution center-demand point coupling optimization based on the road network data of the study area, or the server can obtain the road network data of the study area from the data storage system and perform material distribution center site selection for material storage warehouse-distribution center-demand point coupling optimization.

[0033] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or as a cloud server.

[0034] In an exemplary embodiment, Figures 2 to 4 As shown, a material distribution center site selection method for material storage warehouse-distribution center-demand point coupling optimization is provided. The method is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server in FIG. 1 is taken as an example to illustrate the method, including the following steps 101 to 106. Among them:

[0035] Step 101: Obtain road network data of a study area, and generate a number of non-overlapping blocks based on the road network data; the block is a polygon surrounded by at least one main road edge and a number of non-main road edges.

[0036] Step 102 , using the generated blocks as demand points for material distribution and taking the lowest total transportation cost from the distribution center to the demand point as the goal, construct a multi-distribution center location selection model driven by demand points.

[0037] Step 103 , with the goal of minimizing the difference in service carrying capacity of distribution centers, construct a multi-distribution center location optimization model; the service carrying capacity of the distribution center is calculated based on the betweenness of the distribution center and the scale of material distribution.

[0038] Step 104, based on the spatial distribution of material storage depots and the spatial distribution of demand points in the study area, with the goal of minimizing the transportation cost of dynamic material allocation, construct a multi-distribution center location selection model driven by dynamic material allocation; the transportation cost of dynamic material allocation includes the total transportation cost from material storage depots to distribution centers and the total transportation cost from distribution centers to demand points.

[0039] Step 105: Integrate the multi-distribution center location selection model driven by demand points, the multi-distribution center location optimization model, and the multi-distribution center location selection model driven by dynamic material allocation to obtain a multi-distribution center location selection model for material storage depot-distribution center-demand point coupling optimization.

[0040] Step 106 , using an optimization algorithm to solve the multi-distribution center location model for material storage depot-distribution center-demand point coupling optimization, and obtain the optimal location plan for each distribution center.

[0041] By implementing steps 101 through 106 above, compared to traditional distribution center location models, this method not only focuses on the connection between distribution centers and demand points but also considers the impact of material reserves on distribution centers. Furthermore, it innovatively introduces a distribution service carrying capacity model, dynamically combining the betweenness of distribution centers with the scale of distribution. This method quantifies the hub role and task load of nodes in the network, achieving balanced task distribution across multiple distribution centers.

[0042] In another exemplary embodiment of the present application, in step 101, there are main basic roads such as main roads, secondary roads, branch roads, and small roads in the road network. To divide the blocks in the road network, it is necessary to define the area surrounded by roads as blocks based on the segmentation results of the road network. Each block is naturally divided by roads, and the spatial range within the block is determined by the geometric information of the road. The present application uses GIS technology to extract block information from the urban basic road network and establish demand point data for material distribution. The block is the basic component unit of the urban structure formed by roads. When the city implements closed management and controls residential areas, residential areas, etc., citizens cannot enter and exit the residential area at will, so the minimum demand unit corresponding to material distribution is the block. Although the difference in block volume and demand may be large, the demand point data extracted according to the definition of the block is unique, and the network model and subsequent algorithms established thereby are definite and reliable. Therefore, it is reasonable to use the block as the demand unit for distribution, which can achieve scientific and refined management of material distribution. The road network topology structure is constructed using GIS topology theory, and all non-overlapping polygon elements are extracted. The polygons after screening and combination constitute the block data of the city. As Figure 5 As shown, Figure 5 The area enclosed by main and secondary roads and residential roads is called a block. A block is defined as an area enclosed by intersecting line elements. By overlaying POI population distribution data, the population density of each block can be further analyzed. Red areas indicate dense populations, while green areas indicate sparse populations. By calculating block population density, data support can be provided for the site selection of emergency material distribution centers, thereby achieving coupled optimization of material storage depots, distribution centers, and demand points. The specific process of generating a block is as follows:

[0043] (1): Download road network data from OpenStreetMap and convert it to a .shp file type in ArcGIS. Read the road network line features from the .shp file and break the line segments based on the intersection points. The field marked as primary indicates the main road.

[0044] (2): Combine all road lines into closed polygons. Due to the characteristics of the block, it must rely on the main road to exist. The polygon containing the main road is topologically combined with other polygons to meet the requirements of the city-scale block to generate the block, and establish demand points on the main road it depends on. The material demand of each block is determined according to the city. The material demand of the block is:

[0045]

[0046] Among them, u j is the demand for materials in the block, is the population size, and ρ is the amount of supplies required per person per day (here we assume that each person needs 1 kg of supplies per day).

[0047] Based on the above, in step 101, the road network data of the study area is obtained, and a number of blocks are generated based on the road network data, specifically including:

[0048] (1) Obtain road network data of the study area.

[0049] (2) Read all road lines in the road network data.

[0050] (3) Combine all road lines into several polygons.

[0051] (4) Topologically combine polygons containing the edges of the main roads with other polygons not containing the edges of the main roads to obtain several blocks; the blocks meet the requirements of the preset size. One block corresponds to one demand point.

[0052] In another exemplary embodiment of the present application, based on the above content, in step 102, the generated blocks are used as demand points for material distribution, and the total transportation cost from the distribution center to the demand point is minimized. A demand point-driven multi-distribution center location selection model is constructed, specifically including:

[0053] (1) Assign attributes to each road in the road network based on the road network data; the attributes include length, travel time, and transportation time.

[0054] (2) Determine the transportation time constraints between the demand point and the distribution center.

[0055] (3) Determine the location range of the distribution center for each demand point based on the attributes assigned to each road in the road network and the transportation time constraints.

[0056] (4) Select a distribution center within the distribution center location range of each demand point, and take the lowest total transportation cost from the distribution center to the demand point as the goal, and build a multi-distribution center location model driven by demand points.

[0057] Based on demand point data and a basic road network model, a mathematical model for the location of multiple distribution centers is established to meet the minimum cost between demand points and distribution centers. First, the basic road network is converted into a network dataset, and attributes (such as length, travel time, and transportation time) are assigned to each road. Then, based on transportation time constraints (such as arrival within 10 minutes), the location range of the distribution center corresponding to each demand point is determined. A multi-distribution center location model is constructed while meeting the location range. The expression of the multi-distribution center location model driven by demand points is:

[0058]

[0059] Where C iis the construction model of distribution center i (it varies from city to city and is unified as 1 here); z i Is whether to select distribution center i (0 or 1); d ij is the transportation cost from demand point j to distribution center i, which varies from city to city and is unified as the road distance here; ij Indicates whether demand point j is served by distribution center i, with a value of 0 or 1; n is the total number of distribution centers; where y ij ≤z i .

[0060] In another exemplary embodiment of the present application, in step 103, from the perspective of network traffic flow, the flow of materials is from the material storage warehouse to the distribution center and then to the demand point. The material flow process can be analyzed using complex network theory. The distribution center is the material transfer node, so the betweenness centrality of all distribution centers can be calculated. i Betweenness is an indicator used in complex network theory to measure the importance of nodes or edges in a road network. It reflects the ability of nodes or edges in the road network to control traffic flow or information transmission in the entire road network, as well as the degree of centralization or clustering strength of nodes in the network path selection process. For the calculation of node betweenness:

[0061]

[0062] Among them, b i is the betweenness centrality of node i in the road network corresponding to the distribution center, s and t are any two nodes in the road network except node i, σ st is the total number of shortest paths from node s to node t, σ st (i) is the number of shortest paths from node s to node t that passes through node i.

[0063] Estimate the facility scale of each distribution center based on the material demand of the demand points. Suppose the estimated material demand scale of each demand point corresponding to distribution center i is {u1,u2,…,u m} (i.e. m demand points), so the total material distribution scale of distribution center i is U i The estimate is:

[0064]

[0065] Service carrying capacity refers to the comprehensive ability of a distribution center in a material distribution network, reflecting its efficiency and load level in handling material distribution tasks. It combines the importance of a distribution center in the network (measured by betweenness) and the scale of material distribution it undertakes, and is used to evaluate the task load of a distribution center and optimize its site selection. On this basis, this application introduces the concept of distribution service carrying capacity, which is defined as a function of the betweenness of a distribution center and its distribution scale. It should be pointed out that the physical meaning and calculation units of betweenness and distribution scale are different, and thus the dimensions and data levels of the data may also be different, so the two must be normalized:

[0066]

[0067] Among them, max b represents the maximum betweenness centrality of each distribution center; maxU represents the maximum distribution scale of the distribution center. and They represent the normalized betweenness centrality and distribution scale of distribution center i respectively.

[0068] Therefore, the service carrying capacity κ of distribution center i i Defined as:

[0069]

[0070] Where α and β are adjustable parameters, α ≥ 0 and β ≥ 0, which are used to adjust the degree of dependence of service carrying capacity on betweenness and distribution scale.

[0071] α is the betweenness weight, which measures the contribution of the distribution center's hub importance in the network topology to service carrying capacity; β is the distribution scale weight, which measures the contribution of the distribution center's material handling volume to service carrying capacity. α and β are used to adjust the dependence of service carrying capacity on betweenness and distribution scale. Through parametric design (e.g., adjustable α and β weights), customized requirements for different city sizes and disaster types can be supported.

[0072] Formula (7) describes the carrying capacity of the distribution center, κ i The larger the value, the heavier the task of the distribution center. To ensure that all distribution centers can be efficiently utilized, the location of the distribution center should be optimized from the perspective of balanced service carrying capacity. Then, the objective function is established under the constraint of minimizing the difference in distribution service carrying capacity:

[0073]

[0074] Where, κ i is the service carrying capacity of the ith distribution center; is the average service capacity of all distribution centers; n is the total number of distribution centers.

[0075] Further optimization of multiple distribution centers is achieved by minimizing the differences in service carrying capacity. ξ(κ i ) By minimizing the difference in service capacity, tasks can be balanced across multiple distribution centers, rather than selecting a single κ i The smallest distribution center ensures that the task load of all distribution centers is relatively balanced. Table 1 shows the calculation of the service carrying capacity of three distribution centers (candidate points).

[0076] Table 1 Calculation of service carrying capacity

[0077]

[0078] Among them, the ξ(κ i )=0.055, ξ(κ i )=0.02, ξ(κ i )=0.008. The optimal solution is combination 3, although the κ of distribution center C i It is not the minimum, but its combination with B achieves the best balance. i The smallest distribution center B will lead to other distribution centers κ i Higher, the overall benefit is greater, ξ(κ i ) can avoid the local optimal trap: if only κ is selected i The smallest distribution center may cause other distribution centers to be overloaded, so the service carrying capacity is an important factor to be considered when selecting the site for multiple distribution centers.

[0079] Based on the above, in step 103, a multi-distribution center location optimization model is constructed with the goal of minimizing the differences in the service carrying capacity of distribution centers. Specifically, the model includes:

[0080] (1) In the road network of the study area, calculate the betweenness centrality of the nodes corresponding to the distribution center.

[0081] (2) For each distribution center, the material distribution scale of each distribution center is determined based on the material demand of each demand point served by the distribution center.

[0082] (3) Determine the service carrying capacity of each distribution center based on its betweenness and material distribution scale.

[0083] (4) With the goal of minimizing the differences in the service carrying capacity of distribution centers, a multi-distribution center location optimization model is constructed.

[0084] In another exemplary embodiment of the present application, in step 104, a mathematical model describing the dynamic allocation of materials between material reserves and distribution centers is established, taking into account the spatial distribution of material reserves within the city. This model then constructs a multi-distribution center location selection model for urban emergency materials that is optimized for the coupled optimization of material reserves, distribution centers, and demand points. The dynamic allocation model for materials consists of a dynamic inventory, an objective function, and dynamic constraints. The dynamic inventory is:

[0085]

[0086] The objective function is:

[0087]

[0088] Where c ki d ij are the unit transportation costs from material storage warehouse k to distribution center i and from distribution center i to demand point j respectively; h i is the inventory holding cost of distribution center i; x kit is the transportation volume from material storage warehouse k to distribution center i at time t, y ijt is the transportation volume from distribution center i to demand point j at time t; I it is the inventory level of distribution center i at time t.

[0089] Finally, the dynamic constraints are satisfied. That is, the requirements are satisfied: Capacity Limitation: Non-negativity: x kit ,y ijt ,I it ≥0.

[0090] Among them, D jt represents the demand quantity of demand point j; S i represents the maximum inventory capacity of distribution center i.

[0091] In another exemplary embodiment of the present application, in step 105, the demand point-driven distribution center location model is coupled with the material reserve warehouse-driven distribution center location model to create an urban emergency material multi-distribution center location model for material reserve warehouse-distribution center-demand point coupling optimization:

[0092]

[0093] x kit ,y ijt ,I it ≥0,z i ∈{0,1} (15)

[0094] In this application, based on the urban road network structure, GIS technology is used to extract block data; according to the urban population distribution data, the population size of each area is determined, and the blocks are used as the demand points for material distribution; the concept of distribution service carrying capacity is introduced, and the betweenness centrality and distribution scale of the distribution center are studied using complex network theory to construct a mathematical function that characterizes the distribution service carrying capacity, and from the perspective of balanced service carrying capacity, a multi-distribution center site selection optimization method is proposed under the condition of minimizing the difference in service carrying capacity; on this basis, the relationship between material storage depots and distribution centers is further studied, and a mathematical model that describes the dynamic allocation problem of materials between the two is established; finally, a coupling problem of "material storage depot-distribution center-demand point" is formed, and then a multi-distribution center site selection model for urban emergency materials is established for the coupling optimization of material storage depot-distribution center-demand point.

[0095] The solution of this application has the following advantages:

[0096] (1) Strong comprehensiveness: The traditional distribution center location selection model mainly analyzes the spatial relationship between the distribution center and the demand point, and lacks unified modeling and analysis of the distribution center, material storage depot, demand point, basic road network, etc. For the dynamic dispatch of urban emergency materials under major public health emergencies, a large number of demand points and various facilities should be modeled as a complex system from the overall level, and the optimal dispatch of emergency materials under the linkage mechanism within and outside the city should be solved through in-depth analysis of the system structure and dynamic behavior. This method breaks through the current distribution center location selection theory's lack of understanding of the facility network dynamic process, and analyzes the coupling process of distribution centers, demand points, material storage depots, and urban road networks in a unified complex spatial network system, thereby revealing the interaction mechanism between different facilities.

[0097] (2) High sensitivity: This method shows higher sensitivity than traditional distribution center location models when applied to complex road networks. Its results show more significant differences and more accurately reflect the differences in location centers under the influence of distribution service carrying capacity.

[0098] (3) Solve the key scientific problem of achieving unified modeling, expression, and analysis based on data such as urban basic road network models, and use complex network theory to construct a coupled optimization site selection model, which will help improve the modeling and analysis capabilities of site selection theory for the topological structure of spatial networks and the cognitive laws of the dynamic scheduling process of materials. By deeply studying the interaction mechanism between distribution centers, demand points, and material storage depots on complex urban road networks, a reasonable and adaptive multi-distribution center site selection model will be constructed, thereby improving the efficiency of the overall dynamic scheduling of urban materials and ensuring that emergency materials can be delivered to demand points efficiently and accurately.

[0099] This application uses a comparative experiment of road networks in various regions to verify the effectiveness of the method of this application. The comparison results are shown in Table 2. Figure 6 and Figure 7 shown.

[0100] Table 2 Comparison of optimization models

[0101] parameter Traditional Model The model proposed in this paper side 2364 2364 Building network nodes 10690 10690 Build network edges 12006 12006 neighborhood 72 38 distribution center 50 12 Coverage 70.95％ 78.66％ Violation 2.99％ 2.93％

[0102] In another exemplary embodiment of the present application, the present application also provides an application scenario, which applies the above-mentioned material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization. Specifically: the material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization provided in this embodiment can be applied in urban emergency material distribution scenarios. The scenario includes a data collection link and a distribution center site selection link; the data collection link is used to collect road network data of the study area; the distribution center site selection link is used to perform material distribution center site selection for material storage depot-distribution center-demand point coupling optimization based on the collected road network data, and obtain the optimal site selection plan for multiple distribution centers. . The material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization provided in this embodiment belongs to the distribution center site selection link.

[0103] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store material distribution center location data for material storage depot-distribution center-demand point coupling optimization. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a material distribution center location method for material storage depot-distribution center-demand point coupling optimization is implemented.

[0104] Those skilled in the art will understand that Figure 8The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0105] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0106] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0107] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0108] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0109] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0110] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A material distribution center site selection method for material storage warehouse-distribution center-demand point coupling optimization, characterized in that: include: Obtaining road network data of a study area, and generating a number of non-overlapping blocks based on the road network data; The block is a polygon surrounded by at least one main road edge and several non-main road edges; Taking the generated blocks as the demand points for material distribution and the goal of minimizing the total transportation cost from the distribution center to the demand points, a multi-distribution center location selection model driven by demand points is constructed. With the goal of minimizing the differences in the service carrying capacity of distribution centers, a multi-distribution center location optimization model is constructed; the service carrying capacity of the distribution center is calculated based on the betweenness of the distribution center and the scale of material distribution; Based on the spatial distribution of material storage depots and demand points in the study area, and with the goal of minimizing the transportation cost of dynamic material allocation, a multi-distribution center location selection model driven by dynamic material allocation is constructed. The transportation cost of dynamic material allocation includes the total transportation cost from material storage depots to distribution centers and the total transportation cost from distribution centers to demand points. By integrating the multi-distribution center location selection model driven by demand points, the multi-distribution center location optimization model, and the multi-distribution center location selection model driven by dynamic material allocation, a multi-distribution center location selection model for coupled optimization of material storage depots, distribution centers, and demand points was derived. The optimization algorithm is used to solve the multi-distribution center location selection model for material storage depot-distribution center-demand point coupling optimization, and the optimal location scheme for each distribution center is obtained.

2. The material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization according to claim 1 is characterized in that: Obtain the road network data of the study area and generate several blocks based on the road network data, specifically including: Obtain road network data for the study area; Read all road lines in the road network data; Combine all road lines into several polygons; A polygon containing the edge of a main road is topologically combined with other polygons not containing the edge of a main road to obtain a plurality of blocks; the blocks meet the requirements of a preset size area.

3. The material distribution center site selection method for material storage warehouse-distribution center-demand point coupling optimization according to claim 1 is characterized in that: Taking the generated blocks as the demand points for material distribution and minimizing the total transportation cost from the distribution center to the demand points as the goal, a multi-distribution center location selection model driven by demand points is constructed, specifically including: Assigning attributes to each road in the road network according to the road network data; the attributes include length, travel time and transport time; Determine the transportation time constraints between demand points and distribution centers; Determine the distribution center location range for each demand point based on the attributes assigned to each road in the road network and the transportation time constraints; A distribution center is selected within the distribution center location range of each demand point, and the total transportation cost from the distribution center to the demand point is minimized, and a multi-distribution center location selection model driven by demand points is constructed.

4. The material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization according to claim 3 is characterized in that: The expression of the multi-distribution center location model driven by demand points is: Where C i is the construction model of distribution center i; z i Whether to select distribution center i, the value is 0 or 1; d ij is the transportation cost from demand point j to distribution center i; y ij Indicates whether demand point j is served by distribution center i, with a value of 0 or 1; n is the total number of distribution centers; y ij ≤z i .

5. The material distribution center site selection method for material storage warehouse-distribution center-demand point coupling optimization according to claim 1 is characterized in that: With the goal of minimizing the differences in the service carrying capacity of distribution centers, a multi-distribution center location optimization model is constructed, specifically including: In the road network of the study area, the betweenness centrality of the nodes corresponding to the distribution centers is calculated; For each distribution center, the material distribution scale of each distribution center is determined based on the material demand of each demand point served by the distribution center; Determine the service carrying capacity of each distribution center based on its betweenness and material distribution scale; With the goal of minimizing the differences in the service carrying capacity of distribution centers, a multi-distribution center location optimization model is constructed.

6. The material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization according to claim 5 is characterized in that: The expression of the multi-distribution center location optimization model is: Where, κ i is the service carrying capacity of the ith distribution center; is the average service capacity of all distribution centers; n is the total number of distribution centers.

7. The material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization according to claim 1 is characterized in that: The expression of the multi-distribution center location model driven by dynamic material allocation is: Where c ki d ij are the unit transportation costs from material storage warehouse k to distribution center i and from distribution center i to demand point j respectively; h i is the inventory holding cost of distribution center i; x kit is the transportation volume from material storage warehouse k to distribution center i at time t, y ijt is the transportation volume from distribution center i to demand point j at time t; I it is the inventory level of distribution center i at time t.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the material distribution center site selection method for material storage depot-distribution center-demand point coupling optimization according to any one of claims 1 to 7 is implemented.

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