Logistics distribution system and method for urban subway-truck combined transportation

Abstract

The invention discloses a logistics distribution system and method for urban subway-truck combined transportation, and belongs to the technical field of underground logistics, and the system comprises a logistics distribution center, a community station, a subway transfer center, a subway distribution line and a truck distribution line. According to the invention, the system employs the optimized K-means clustering algorithm and the P-median site selection model to establish the metro transfer center, and employs a mode of cooperative distribution of metro and lorry, thereby achieving the cooperative distribution of the metro and the lorry, improving the distribution efficiency of the metro and the lorry, and improving the distribution efficiency of the metro and the lorry, and improving the distribution efficiency of the metro and the lorry, and improving the distribution efficiency of the metro and the lorry. Compared with an independent mode, the distribution time efficiency is greatly improved, the distribution demand quantity and the site selection result are combined, the position of a subway transfer point is directly influenced, the subway logistics transportation cost is reduced, and the logistics network and the subway network are matched as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of underground logistics, and in particular to a logistics distribution system and method for urban subway-truck combined transport. Background Art

[0002] With the innovative development of new e-commerce models, the express delivery industry is also experiencing rapid growth. The surge in freight demand has had a severe negative impact on urban transportation and the environment, creating a significant conflict between the growth of express delivery and urban transportation. Statistics show that delivery times within cities have increased by as much as 30%. Currently, subways, as a vital component of public transportation, are becoming increasingly well-connected. Their abundant subway tunnels, established network structure, and comprehensive infrastructure make them efficient, frequent, and punctual. Furthermore, subway station locations take into account factors such as population density and resident needs, aligning them with freight delivery. Utilizing idle subway space for freight delivery can both increase subway utilization and reduce the need for trucks. However, current methods of using subways for logistics transportation often suffer from high costs and a mismatch between logistics and subway networks. Based on this, this invention proposes a logistics distribution system and method for urban subway-truck intermodal transport. Summary of the Invention

[0003] The purpose of the present invention is to provide a logistics distribution system and method for urban subway-truck intermodal transport, which establishes a subway transfer center by using an optimized K-means clustering algorithm and a P-median site selection model, reduces the cost of subway logistics transportation, and matches the logistics network with the subway network as much as possible.

[0004] To achieve the above-mentioned objectives, the present invention provides a logistics distribution system for urban subway-truck intermodal transport, which includes a logistics distribution center, a residential community site, a subway transfer center, a subway distribution line, and a truck distribution line. After the target residential community site of the goods is determined, the goods are transported from the distribution center along the subway distribution line to the subway transfer center to which the target residential community site belongs, and then delivered to the residential community site via the truck distribution line.

[0005] Preferably, the location of the subway transfer center is obtained by adding the K-means clustering algorithm and the P-median location model with the service radius.

[0006] Preferably, there are several groups of cell sites, and each group of cell sites is provided with a subway transfer center.

[0007] Preferably, the logistics distribution center is based in large and medium-sized cities and is a place for storage, transportation, packaging, processing, loading and unloading, and handling of goods. The community station is a place for collecting and storing goods. The subway transfer center will distribute the packages processed by express companies and logistics distribution centers to the corresponding subway station entrances and exits according to their type and weight.

[0008] The present invention also provides a logistics distribution method for urban subway-truck combined transport, comprising the following steps:

[0009] S1. Obtain cluster centers by adding service radius to the K-means clustering algorithm.

[0010] S2. Based on the cluster center, a P-median location selection model is established to obtain the P-median, which is the location of the subway transfer center;

[0011] S3. Determine the target residential site to which the goods need to be delivered, as well as the subway transfer center near the target residential site;

[0012] S4. Transport the goods from the distribution center to the subway station entrance that is closest to the distribution center and directly connects to the subway transfer center of the target community station;

[0013] S5. Transport the goods to the subway transfer center via the subway distribution line, and then transport them to the target community site via the truck distribution line to complete the delivery.

[0014] Preferably, the process of obtaining the cluster center in S1 is as follows:

[0015] S11, dividing all cell sites into K groups of initial clusters according to the distance between the cell sites;

[0016] S12. Select K initial cluster centroids from the K groups of initial clusters and set the service radius;

[0017] S13, respectively calculating the distances from the cell sites to the K initial cluster centroids, and assigning them to the cluster closest to the initial cluster centroid, to obtain K new clusters;

[0018] S14, selecting new K initial cluster centroids in the new K groups of clusters and continuing the calculation;

[0019] S15. Until the clusters of all sites no longer change, K-means clustering is completed to obtain K groups of clusters and K cluster centers.

[0020] Preferably, the process of obtaining the subway transfer center in S2 is as follows:

[0021] S21, dividing the K groups of clusters obtained in S1 into K groups and calculating the subway transfer center of each group of clusters;

[0022] S22, selecting a subway station near the cluster center of each cluster as a candidate point for the subway transfer center;

[0023] S23. Based on the distribution of cell sites in each cluster and the freight demand, a P-median location selection model is established to calculate the minimum cost from each candidate point to each site. The process is as follows:

[0024]

[0025] Where M represents the set of demand points i in the sub-region, i∈M, M={1, 2, ..., m}, N represents the set of candidate subway transfer centers j in the sub-region, j∈N, N={1, 2, ..., n}, q i represents the average daily express parcel volume of the i-th demand point, l ij represents the distance from the i-th demand point to the j-th candidate subway transfer center, s ij represents the unit transportation cost from the i-th demand point to the j-th candidate subway transfer center, y ij is a decision variable, when y ij =1, indicating that the package of demand point i is provided with transit service by node j. ij =0, indicating that demand point i is not served by node j;

[0026] S24. Constrain the P-median location model. The constraints are as follows:

[0027]

[0028]

[0029] y ij ≤x j , i∈M, j∈N;

[0030] y ij ∈{0, 1}, i∈M, j∈N;

[0031] where x j Represented as a 0-1 decision variable, x j =1, indicating that a transfer center is set up at point j, x j = 0, indicating that no transfer center is set at point j, j∈N, p represents the number of subway transfer centers set in the sub-area, p≤n;

[0032] S25, inputting the logistics distribution cost from each candidate point of the subway transfer center to the cell site of the cluster where it is located, the distribution demand of each cell site, and the distance from the candidate point to each cell into the P-median location selection model;

[0033] S26. Use the Gurobi interpreter to optimize the P-median location selection model to obtain a solution that minimizes the total cost. The candidate subway transfer center point in this solution is the required subway transfer center.

[0034] Preferably, in S1, the service radius is added to the K-means clustering algorithm to optimize the K-means clustering algorithm. The optimized K-means clustering algorithm can obtain a suitable K value, which is no longer randomly selected.

[0035] Therefore, the present invention adopts a logistics distribution system and method of urban subway-truck intermodal transport with the above structure, establishes a subway transfer center by using the optimized K-means clustering algorithm and the P-median site selection model, and uses the subway and truck collaborative distribution method. Compared with the "single" mode, it greatly improves the delivery timeliness and reduces the transportation cost. It also combines the distribution demand with the site selection results, directly affects the location of the subway transfer point, and matches the logistics network with the subway network as much as possible. The amount of demand will largely change the subway transfer point. At the same time, it reasonably utilizes the advantages of the subway to a large extent and avoids waste of resources.

[0036] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a distribution system for a logistics distribution system and method for urban subway-truck combined transport according to the present invention;

[0038] Figure 2 This is a distribution flow chart of a logistics distribution system and method for urban subway-truck combined transport according to the present invention;

[0039] Figure 3 This is a flow chart of an optimized K-means clustering algorithm for a logistics distribution system and method for urban subway-truck intermodal transport according to the present invention;

[0040] Figure 4 This is a schematic diagram of site selection simulation using a P-median site selection model for a logistics distribution system and method for urban subway-truck combined transport according to the present invention. DETAILED DESCRIPTION

[0041] Example

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0045] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the present invention.

[0046] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0047] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0048] like Figures 1-4 As shown, the present invention provides a logistics distribution system for urban subway-truck intermodal transport, including a logistics distribution center, a residential community site, a subway transfer center, a subway distribution line and a truck distribution line. After the target residential community site of the goods is determined, the goods are transported from the distribution center along the subway distribution line to the subway transfer center to which the target residential community site belongs, and then delivered to the residential community site via the truck distribution line.

[0049] The location of the subway transfer center is obtained by adding the K-means clustering algorithm and the P-median location model with service radius.

[0050] There are several groups of cell sites, and each group of cell sites is equipped with a subway transfer center.

[0051] Logistics distribution centers are located in large and medium-sized cities and operate facilities for the storage, transportation, packaging, processing, loading and unloading, and handling of goods. They are generally equipped with advanced logistics management information systems. Their primary function is to facilitate the faster and more economical flow of goods. Centralized storage improves logistics coordination. Organic connections accelerate logistics, shorten circulation times, and reduce costs. Appropriate processing based on needs and rational utilization of supply improve economic efficiency.

[0052] The community site is a place for collecting and storing goods, which is equivalent to a post station. The subway transfer center will distribute the packages processed by express companies and logistics distribution centers to the corresponding subway station entrances and exits according to their type and weight, so that they can be delivered to their destination faster.

[0053] The present invention also provides a logistics distribution method for urban subway-truck combined transport, comprising the following steps:

[0054] S1. By adding the service radius to the K-means clustering algorithm to obtain the cluster center, the optimized K-means clustering algorithm can obtain the appropriate K value instead of random selection.

[0055] S11, dividing all cell sites into K groups of initial clusters according to the distance between the cell sites;

[0056] S12. Select K initial cluster centroids from the K groups of initial clusters and set the service radius;

[0057] S13, respectively calculating the distances from the cell sites to the K initial cluster centroids, and assigning them to the cluster closest to the initial cluster centroid, to obtain K new clusters;

[0058] S14, select new K initial cluster centroids in the new K groups of clusters and continue calculation;

[0059] S15. Until the clusters of all sites no longer change, K-means clustering is completed to obtain K groups of clusters and K cluster centers.

[0060] S2. Based on the cluster center, a P-median location selection model is established to obtain the P-median, i.e., the location of the subway transfer center;

[0061] S21, dividing the K groups of clusters obtained in S1 into K groups and calculating the subway transfer center of each group of clusters;

[0062] S22, selecting a subway station near the cluster center of each cluster as a candidate point for the subway transfer center;

[0063] S23. Based on the distribution of cell sites in each cluster and the freight demand, a P-median location selection model is established to calculate the minimum cost from each candidate point to each site. The process is as follows:

[0064]

[0065] Where M represents the set of demand points i in the sub-region, i∈M, M={1, 2, ..., m}, N represents the set of candidate subway transfer centers j in the sub-region, j∈N, N={1, 2, ..., n}, q i represents the average daily express parcel volume of the i-th demand point, l ij represents the distance from the i-th demand point to the j-th candidate subway transfer center, s ij represents the unit transportation cost from the i-th demand point to the j-th candidate subway transfer center, y ij is a decision variable, when y ij =1, indicating that the package of demand point i is provided with transit service by node j. ij =0, indicating that demand point i is not served by node j, minZ represents the minimum cost;

[0066] S24. Constrain the P-median location model. The constraints are as follows:

[0067]

[0068] y ij ≤x j , i∈M, j∈N;

[0069] x j ∈{0, 1}, j∈N;

[0070] y ij ∈{0, 1}, i∈M, j∈N;

[0071] where x j Represented as a 0-1 decision variable, x j =1, indicating that a transfer center is set up at point j, x j = 0, indicating that no transfer center is set at point j, j∈N, p represents the number of subway transfer centers set in the sub-area, p≤n;

[0072] The first constraint indicates that each demand point is served by only one subway transfer center. The second constraint indicates that p transfer centers are selected from n candidate points. The third constraint indicates that service can only be provided to point i if point j becomes a transfer center. The fourth and fifth constraints represent the value range constraints of the variables.

[0073] S25, inputting the logistics distribution cost from each candidate point of the subway transfer center to the cell site of the cluster where it is located, the distribution demand of each cell site, and the distance from the candidate point to each cell into the P-median location selection model;

[0074] S26. Use the Gurobi interpreter to optimize the P-median location selection model to obtain a solution that minimizes the total cost. The candidate subway transfer center point in this solution is the required subway transfer center.

[0075] S3. Determine the target residential site to which the goods need to be delivered, as well as the subway transfer center near the target residential site;

[0076] S4. Transport the goods from the distribution center to the subway station entrance that is closest to the distribution center and directly connects to the subway transfer center of the target community station;

[0077] S5. Transport the goods to the subway transfer center via the subway distribution line, and then transport them to the target community site via the truck distribution line to complete the delivery.

[0078] Therefore, the present invention adopts a logistics distribution system and method of urban subway-truck intermodal transport with the above structure, establishes a subway transfer center by using the optimized K-means clustering algorithm and the P-median site selection model, and uses the subway and truck collaborative distribution method. Compared with the "single" mode, it greatly improves the delivery timeliness and reduces the transportation cost. It also combines the distribution demand with the site selection results, directly affects the location of the subway transfer point, and matches the logistics network with the subway network as much as possible. The amount of demand will largely change the subway transfer point. At the same time, it reasonably utilizes the advantages of the subway to a large extent and avoids waste of resources.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A logistics distribution system for urban subway-truck combined transport, characterized by: It includes logistics distribution centers, community sites, subway transfer centers, subway distribution lines and truck distribution lines. After the target community site of the goods is determined, the goods will be transported from the distribution center along the subway distribution line to the subway transfer center to the target community site, and then delivered to the community site via the truck distribution line.

2. The urban subway-truck combined transport logistics distribution system according to claim 1, characterized in that: The location of the subway transfer center is obtained by adding the K-means clustering algorithm and the P-median location selection model with service radius.

3. The urban subway-truck combined transport logistics distribution system according to claim 2 is characterized by: There are several groups of cell sites, and each group of cell sites is provided with a subway transfer center.

4. The urban subway-truck combined transport logistics distribution system according to claim 3 is characterized by: The logistics distribution center is based in large and medium-sized cities and is a place for storage, transportation, packaging, processing, loading and unloading, and handling of goods. The community station is a place for collecting and storing goods. The subway transfer center will distribute the parcels processed by express companies and logistics distribution centers to the corresponding subway station entrances and exits according to their type and weight.

5. A logistics distribution method for urban subway-truck combined transport, applied to an urban subway-truck combined transport logistics distribution system as claimed in any one of claims 1 to 4, characterized in that: The following steps are involved: S1, by adding the service radius to the K-means clustering algorithm, the cluster center is obtained; S2. According to the cluster center, a P-median location selection model is established to obtain the P-median, i.e., the location of the subway transfer center; S3, determining the target community site to which the goods need to be delivered, and the subway transfer center near the target community site; S4, transporting the goods from the distribution center to the subway station entrance that is closest to the distribution center and directly reaches the subway transfer center to which the target community station belongs; S5. The goods are transported to the subway transfer center via the subway distribution line, and then transported to the target community site via the truck distribution line to complete the delivery.

6. The urban subway-truck combined transport logistics distribution method according to claim 5 is characterized in that: The process of obtaining the cluster center in S1 is as follows: S11, dividing all cell sites into K groups of initial clusters according to the distances between the cell sites; S12, selecting K initial cluster centroids from the K groups of initial clusters, and setting the service radius; S13, respectively calculating the distances from the cell sites to the K initial clustering centroids, and assigning them to the cluster closest to the initial clustering centroid, to obtain K new clusters; S14, selecting new K initial cluster centroids in the new K groups of clusters, and continuing the calculation; S15. Until the clusters of all sites no longer change, K-means clustering is completed to obtain K groups of clusters and K cluster centers.

7. The urban subway-truck combined transport logistics distribution method according to claim 6 is characterized in that: The process of obtaining the subway transfer center in S2 is as follows: S21, dividing the K groups of clusters obtained in S1 into K groups and calculating the subway transfer center of each group of clusters; S22. Select the subway station near the cluster center of each cluster as the candidate point of the subway transfer center: S23. According to the distribution of cell sites in each group of clusters and the freight demand, a P-median location selection model is established to calculate the minimum cost from each candidate point to each site. The process is as follows: Where M represents the set of demand points i in the sub-region, i∈M, M={1, 2, …, m}, N represents the set of candidate subway transfer centers j in the sub-region, j∈N, N={1, 2, …, n}, q i represents the average daily express parcel volume of the i-th demand point, l ij represents the distance from the i-th demand point to the j-th candidate subway transfer center, s ij represents the unit transportation cost from the i-th demand point to the j-th candidate subway transfer center, y ij is a decision variable, when y ij =1, indicating that the parcel of demand point i is provided with transshipment service by node j. ij =0, indicating that demand point i is not served by node j, and minZ indicates the minimum cost; S24. Constrain the P-median site selection model. The constraints are as follows: and ij ≤x j ,i∈M,j∈N; x j ∈{0,1},j∈N; and ij ∈{0, 1}, i∈M, j∈N; where x j Represented as a 0-1 decision variable, x j =1, indicating that a transfer center is set at point j, x j =0, indicating that no transfer center is set at point j, j∈N, p represents the number of subway transfer centers set in the sub-area, p≤n; S25, inputting the logistics distribution cost from the candidate point of each subway transfer center to the cell site of the cluster where it is located, the distribution demand of each cell site, and the distance from the candidate point to each cell into the P-median location selection model; S26. Use the Gurobi interpreter to optimize the P-median site selection model and obtain the solution that minimizes the total cost. The candidate point of the subway transfer center in this solution is the required subway transfer center.

8. The urban subway-truck combined transport logistics distribution method according to claim 7 is characterized in that: In S1, the service radius is added to the K-means clustering algorithm to optimize the K-means clustering algorithm. The optimized K-means clustering algorithm can obtain a suitable K value, which is no longer randomly selected.

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