Logistics supply relation network growth simulator

Abstract

The invention discloses a logistics supply relation network growth simulator comprising a data receiving module used for receiving a network total node number N and a complete network node number n0, n0lt; n; the processing module is used for continuously adding new nodes and continuously disappearing old nodes; and the model generation module is used for generating a supply relation network G with the same number as the actual network nodes, and by adopting the technical scheme of the invention, the supply network growth is more comprehensively and accurately simulated.

Description

Technical Field

[0001] The invention belongs to the technical field of logistics supply relationship network, and in particular relates to a logistics supply relationship network growth simulator. Background Art

[0002] A network is an organic collection of nodes and the connections between them (called edges). These networks usually have complex topological structures and dynamic behaviors. Networks are widely present in nature and human society. For example, the relationships between people constitute social networks, the connections between articles constitute scientific citation networks, and the connections between transportation stations constitute transportation networks.

[0003] The network growth simulation method is a method to simulate and study actual networks. The network growth simulation method can help researchers understand and analyze the formation and evolution of complex networks, reveal the relationship between network structure and function, and predict the future development trend and evolution path of the network by simulating the growth mechanism, thus providing a basis for the design and optimization of actual networks.

[0004] The existing growth simulation methods of logistics supply relationship networks mainly use the BA network model to simulate the logistics supply relationship network or the network model based on individual selection behavior to simulate the logistics supply relationship network. The BA network model is based on the complete network graph. A new node with m edges is added to the network in each period. m is less than the number of nodes in the complete network graph, and the connection is made according to the node's preference, that is, the probability of the new node connecting to the existing node is proportional to the degree of the existing node. New nodes are continuously introduced until the network reaches the required number of nodes. The network model based on individual selection behavior is based on the complete network graph. A new node with m edges is added to the network in each period. m is less than the number of nodes in the complete network graph, and the connection is made according to the node's preference. The operations of generating new edge connections and deleting some edges before the original nodes in the network are added to simulate the addition of new nodes and the elimination of old nodes.

[0005] The above supply relationship network growth simulation methods all have certain limitations. The supply network simulation using the BA network model only considers the introduction of new nodes. However, in reality, there are not only new roles entering the supply chain, but also roles exiting. The supply chain model based on individual selection behavior takes into account both the introduction of new nodes and the elimination of old nodes. However, in reality, the various roles in the supply chain will not absolutely increase or exit, that is, there is a large error in simulating the growth network according to 100% addition or deletion of edges. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a logistics supply relationship network growth simulator to simulate the supply network growth more comprehensively and accurately.

[0007] To achieve the above object, the present invention adopts the following technical solution:

[0008] A logistics supply relationship network growth simulator, comprising:

[0009] The data receiving module is used to receive the total number of network nodes N and the number of complete network nodes n0, where n0 <N;

[0010] A processing module is used to make new nodes continue to join and old nodes continue to disappear;

[0011] The model generation module is used to generate a supply relationship network G with the same number of nodes as the actual network.

[0012] Preferably, the data processing module comprises generating a complete network graph G0 formed by n0 nodes, that is, each node i in G0 is directly connected to all other nodes; and adding a node n on the basis of the network graph G0. t And connect with the original nodes in the network and eliminate the old nodes; the operation is repeated until the number of nodes N required by the network G is reached.

[0013] As a preference, the new node n t The probability of connecting to the original node is:

[0014]

[0015] Among them, π i is the selection probability, k i is the degree of the node, α represents the tendency to regulate the edge connection, α>0.

[0016] As a preference, for adding a new node n t , calculate π i Arrange them in order from large to small, select the first m nodes and the new node n t connect.

[0017] As a preferred option, when the network is set to generate l new edges with an 80% probability, the original node i is replaced by π i as an endpoint of the line.

[0018] As a preference, in the elimination of old nodes, l' edges will not be absolutely deleted, and a probability of 50% is set for deletion.

[0019] As a preferred method, the original l' connections in the network are deleted, and the original node i is replaced by π i ' is an endpoint of the deleted link,

[0020]

[0021] Among them, π i ' is the probability of selective deletion, N is the total number of network nodes when deleting the connection, N=n0+t, and: m>0,l>0,l'≥0,m+l>l'.

[0022] As a preferred embodiment, the error formula between the simulated network and the actual network is:

[0023]

[0024] in, represents the degree of the simulated network, represents the degree of the real network, C represents the clustering coefficient of the simulated network, and C true represents the clustering coefficient of the real network, represents the average path length of the simulated network, Represents the average path length of the real network.

[0025] The present invention takes into account both the entry of new nodes and the elimination of original nodes. At the same time, it also takes into account that the increase and decrease of edges in the real supply chain network do not change with a 100% probability. Therefore, through the preliminary estimation of the real model, a certain percentage is set to simulate the change of edges in the network, so as to more accurately simulate the growth of the network and eliminate the errors generated by the BA network model and the network model based on individual selection behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0027] Figure 1 This is a schematic diagram of the structure of a logistics supply relationship network growth simulator according to an embodiment of the present invention;

[0028] Figure 2 It is the actual network diagram in the experiment of the present invention;

[0029] Figure 3 It is the complete graph G0 formed in the experiment of the present invention. DETAILED DESCRIPTION

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] Embodiment 1:

[0033] As Figure 1 shown, an embodiment of the present invention provides a logistics supply relationship network growth simulator, including: a receiving unit and a processing terminal; the receiving unit includes: a data receiving module; the processing terminal includes a data processing module and a model generation module; the data receiving module is connected to the data processing module, and the data processing module is connected to the model generation module; the data receiving module is used to receive the total number of network nodes N and the number of nodes n0 in a complete network, where n0 < N; the processing module is used to enable new nodes to be continuously added and old nodes to be continuously disappeared; the model generation module is used to generate a supply relationship network G with the same number of nodes as the actual network nodes.

[0034] Further, the data processing module includes generating a complete network graph G0 formed by n0 nodes, that is, each node i in G0 is directly connected to all other nodes; and on the basis of the network graph G0, a new node n t is added and connected to the original nodes in the network and the old nodes are eliminated; the operation is continuously cycled until the required number of nodes N of the network G is reached;

[0035] Further, the probability that the new node n t is connected to the original nodes is:

[0036]

[0037] where π i is the selection probability, k i is the degree of the node, and α represents the regulation of the edge connection tendency, α > 0.

[0038] For the addition of the new node n t , calculate π i and arrange it in descending order, and select the first m nodes to be connected to the new node n t .

[0039] Further, when there are l new edges generated before the original nodes in the network, with π iThe probability of generation, π i The larger it is, the easier it is for node i to be selected.

[0040] Furthermore, new edges will not absolutely be generated between existing nodes in the network, and an 80% probability of generating new edges is set.

[0041] Furthermore, in the elimination of old nodes, l' edges will not be absolutely deleted, and a probability of 50% is set for deletion.

[0042] Furthermore, the original l' connections in the network are deleted, and the original node i is replaced by π i ' is an endpoint of the deleted link,

[0043]

[0044] Among them, π i ' is the probability of selective deletion, N is the total number of network nodes when deleting the connection, N=n0+t, and: m>0,l>0,l'≥0,m+l>l'.

[0045] The following is an experimental verification of a company's business data at the end of the year, and a real network is constructed based on the data. The structural diagram of the complex network is as follows: Figure 2 shown.

[0046] Furthermore, the data receiving module receives N=132, n0=13.

[0047] Furthermore, the data processing module generates a complete network graph G0 according to n0=13, such as Figure 3 As shown, and based on the network graph G0, node n is t Add new nodes and connect them to the original nodes in the network, and eliminate old nodes. Repeat the operation until the required number of nodes in network G reaches 132.

[0048] Finally, the error formula between the simulated network and the actual network is defined as:

[0049]

[0050] in, represents the degree of the simulated network, represents the degree of the real network, C represents the clustering coefficient of the simulated network, and C true represents the clustering coefficient of the real network, represents the average path length of the simulated network, Represents the average path length of the real network.

[0051] As shown in Table 1, there are errors between the networks generated by different simulation methods and the actual network. After calculation, it can be seen that when m=6 and α=0.1, the error of the network model is the smallest. It can be seen that the simulation method proposed in the present invention can effectively reduce the error generated by network simulation and improve the simulation accuracy.

[0052] Table 1

[0053]

[0054] The logistics supply relationship network simulated by the embodiment of the present invention has a high degree of complexity and adaptability. It can effectively depict the complex connections and dependencies between nodes in the supply chain, reflecting the heterogeneity and scale-free characteristics between nodes. By simulating the dynamic growth of nodes and the formation of links, it helps to analyze and optimize the structure and operation of the supply chain, and improve the responsiveness and resilience of the supply chain. This model is not only suitable for small-scale supply networks, but also can cope with the challenges of global supply chains, providing managers with decision support and forecasting capabilities. To overcome the limitations of simulated supply relationship networks, the embodiment of the present invention considers the entry and elimination of nodes, taking into account both the entry of new nodes and the elimination of original nodes. At the same time, it also takes into account that the increase and decrease of edges in the real supply chain network does not change with a probability of 100%. By setting a certain probability value to increase and decrease edges, the limitations of previous network models are overcome.

[0055] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A logistics supply relationship network growth simulator, characterized in that: include: The data receiving module is used to receive the total number of network nodes N and the number of complete network nodes n0, where n0 <N; A processing module is used to make new nodes continue to join and old nodes continue to disappear; The model generation module is used to generate a supply relationship network G with the same number of nodes as the actual network.

2. The logistics supply relationship network growth simulator according to claim 1, characterized in that: The data processing module includes generating a complete network graph G0 formed by n0 nodes, that is, each node i in G0 is directly connected to all other nodes; and adding a node n on the basis of the network graph G0. t And connect with the original nodes in the network and eliminate the old nodes; the operation is repeated until the number of nodes N required by the network G is reached.

3. The logistics supply relationship network growth simulator as claimed in claim 2, characterized in that: New node n t The probability of connecting to the original node is: Among them, π i is the selection probability, k i is the degree of the node, α represents the tendency to regulate the edge connection, α>0.

4. The logistics supply relationship network growth simulator as claimed in claim 3, characterized in that: For adding a new node n t , calculate π i Arrange them in order from large to small, select the first m nodes and the new node n t connect.

5. The logistics supply relationship network growth simulator as claimed in claim 4, characterized in that: When the network is set to generate l new edges with an 80% probability, the original node i is replaced by π i as an endpoint of the line.

6. The logistics supply relationship network growth simulator as claimed in claim 5, characterized in that: In the elimination of old nodes, l' edges will not be deleted absolutely, and a 50% probability of deletion is set.

7. The logistics supply relationship network growth simulator according to claim 6, characterized in that: The original l' connections in the network are deleted, and the original node i is replaced by π i ' is an endpoint of the deleted link, Among them, π i ' is the probability of selective deletion, N is the total number of network nodes when deleting the connection, N=n0+t, and: m>0,l>0,l'≥0,m+l>l'.

8. The logistics supply relationship network growth simulator as claimed in claim 7, characterized in that: The error formula between the simulated network and the actual network is: in, represents the degree of the simulated network, represents the degree of the real network, C represents the clustering coefficient of the simulated network, and C true represents the clustering coefficient of the real network, represents the average path length of the simulated network, Represents the average path length of the real network.

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