Complex supply chain network toughness simulation method and system under climate risk impact

Abstract

The invention discloses a complex supply chain network toughness simulation method and system under climate risk impact, and relates to the field of climate risk assessment. Quantifying the impact proportion of the climate risk to the local area, and measuring the influence degree by adopting the proportion of different climate risk impact degrees; constructing a complex supply chain network, taking countries and regions involved in upstream and downstream links of a supply chain as nodes, and taking corresponding trade volumes as edges; analyzing key node features of the complex supply chain network by adopting node degree, node strength and core degree indexes; carrying out toughness inspection on the complex supply chain network in different climate risk disturbance modes; and in combination with cascade failure simulation, the influence category and toughness change of the toughness of the complex supply chain network under the climate risk impact are measured. According to the method, through analysis of key indexes such as node importance and link reliability under climate risk impact, weak links in the supply chain network are accurately identified, and quantitative support and policy enlightenment are provided for targeted formulation of risk coping strategies.

Description

Technical Field

[0001] The present invention relates to the field of supply chain risk assessment, and more specifically to a method and system for simulating the resilience of a complex supply chain network under climate risk impacts. Background Art

[0002] In today's complex and ever-changing web of economic and trade connections, localized climate risks such as hurricanes and floods can have varying degrees of impact on upstream and downstream trade links in the supply chain. Increasingly frequent trade frictions and rapidly expanding trade volumes have further exacerbated supply chain risks. The deepening of globalization has enabled supply risks in a single country or region to spread not only through direct trade links, but also through the complex web of trade connections between countries and regions. As supply chain risks have significantly intensified, examining the resilience of complex supply chain networks to climate risks and analyzing the impact scale and transmission process of supply crises in different regions will help provide quantitative support and policy implications for accurately identifying the evolution of complex supply chain networks under the influence of climate risks and the cascading transmission of risks. Traditional supply chain assessment methods struggle to effectively address these risks, and methods for simulating the resilience of complex supply chain networks under climate risk shocks urgently need to be expanded. Summary of the Invention

[0003] In view of this, the present invention provides a method and system for simulating the resilience of complex supply chain networks under climate risk shocks. It comprehensively considers various factors in the supply chain network, and uses methods such as complex network construction and quantitative model analysis to quantitatively evaluate the scope of cascading risk transmission and the degree of resilience change of the supply chain when it is exposed to climate risks.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for simulating the resilience of complex supply chain networks under climate risk shocks includes the following steps:

[0006] Use the proportion of different climate risk impact levels to measure the economic and social impact of disaster-affected countries and regions;

[0007] Construct a complex supply chain network, with the countries and regions involved in the upstream and downstream links of the supply chain as nodes and the corresponding trade volumes as edges, and represent it with a matrix;

[0008] Node degree, node strength and coreness indicators are used to analyze the key node characteristics of complex supply chain networks;

[0009] Under different climate risk disturbance scenarios, the resilience of complex supply chain networks under climate risk is tested in combination with the degree of node impact;

[0010] Combined with cascading failure simulation, the impact scope of the resilience of complex supply chain networks under climate risk shocks is measured, and the degree of risk disturbance of complex supply chain networks is identified.

[0011] Optionally, quantify the impact of climate risk in local areas on key trade links in complex supply chains. For different climate risk scenarios w, the probability of occurrence Pw can be expressed by the product of the probability of the values, and the degree of impact measured in proportion to its economic and social impact;

[0012] Alternatively, the complex supply chain network is represented by a matrix G = (V i ,V j ,A,W); where V i ={v1,v2...v n} and V j ={v1,v2...v n} respectively represent the countries and regions involved in exporting and importing products, constituting nodes in the trade network; A is the directed adjacency matrix of different industry trade networks, and W is the weight matrix of different industry trade networks.

[0013] Optional, node degree refers to the number of direct edges of a node in the network. Node degree is divided into node out-degree and node in-degree, which correspond to the number of export and import connections of a country or region, respectively. The calculation formula is as follows:

[0014]

[0015] Where: and Represent node out-degree, node in-degree and node degree respectively. Using network density Average clustering coefficient and the average path length etc. to measure the overall characteristics of the trade network.

[0016] Optional indicator W of the process of functional degradation after the complex supply chain is disturbed R Defined as:

[0017]

[0018] Where: R represents the remaining nodes in the network after some nodes fail, T [t] represents the global product trade volume in year t; W R It reflects the weight of the trade volume of the remaining nodes in the total trade volume after some nodes fail. R The rate of decline is negatively correlated with the resilience of the network. The higher the network resilience, the faster the W R The slower the rate of decline.

[0019] Alternatively, the spread of the supply crisis is considered as a cascading failure on a weighted directed network, which is a large-scale avalanche of the network caused by a node failure; when all nodes in the network are in a normal state, the total import and export trade volume of node i is defined as the initial load capacity T of node i i , the initial load capacity of the edge from node i to node j is the export value w ij When the state of node i is abnormal, that is, the export volume of node i decreases by α, the weight w of all edges connected to the export of node i will decrease by α; if the import volume of any normal node j connected to the abnormal node changes by αw ij Exceeding node capacity T j β is an adjustable parameter ranging from 0 to 1, then node j will also become abnormal; repeat the above steps until there are no new abnormal nodes in the network, and measure the impact scope and degree of resilience change of the complex supply chain network under climate risk shocks in combination with the node status.

[0020] A complex supply chain network resilience simulation system under climate risk impacts, including:

[0021] Risk Perturbation Module: used to quantify the impact of climate risks in local areas on key trade links in complex supply chains;

[0022] Network construction module: used to construct complex supply chain networks, with countries and regions involved in the upstream and downstream links of the supply chain as nodes, and the corresponding trade volumes as edges, and represented by a matrix;

[0023] Node identification module: used to analyze the node characteristics of complex supply chain networks using node degree, node strength and coreness indicators;

[0024] Resilience Testing Module: This module is used to test the resilience of complex supply chain networks under different climate risk disturbance scenarios, taking into account the degree of node impact;

[0025] Cascade simulation module: used to measure the impact scope of the resilience of complex supply chain networks under a climate risk shock by combining cascading failures, and to identify the degree of risk disturbance in complex supply chain networks.

[0026] The above technical solution demonstrates that, compared to existing technologies, the present invention discloses a method and system for simulating the resilience of complex supply chain networks under climate risk impacts. This method quantitatively assesses the ability of supply chains to maintain key functions, rapid diffusion, and relative impact when exposed to climate risk. By analyzing key indicators such as node importance and link reliability, it can accurately identify weak links in the supply chain network, providing a scientific basis for developing targeted risk response strategies and enhancing sustainable development capabilities in complex risk environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0028] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] The embodiment of the present invention discloses a method for simulating the resilience of a complex supply chain network under climate risk impact. Figure 1 As shown, the following steps are included:

[0031] Step 1: Use the ratio of different climate risk impact levels to measure the economic and social impact of disaster-affected countries and regions;

[0032] Step 2: Build a complex supply chain network, using the countries and regions involved in the upstream and downstream links of the supply chain as nodes and the corresponding trade volumes as edges, and represent it using a matrix;

[0033] Step 3: Use indicators such as node degree, node strength and coreness to analyze the node characteristics of the complex supply chain network;

[0034] Step 4: Under different climate risk disturbance models, the resilience of complex supply chain networks under climate risk is tested in combination with the degree of node impact;

[0035] Step 5: Combine cascading failures to measure the impact scope and changes in network resilience of complex supply chain networks under climate risk shocks.

[0036] Furthermore, in step 1, the impact ratio of different degrees of climate risk in local areas on key trade links in complex supply chains is introduced. For different climate risk scenarios w, the probability of occurrence Pw can be expressed by the product of the value probabilities. The impact degree measured in proportion to its economic and social impact;

[0037] Furthermore, in step 2, the complex supply chain network is represented by a matrix G = (V i ,V j,A,W); where V i ={v1,v2...v n} and V j ={v1,v2...v n} respectively represent the countries and regions involved in the upstream and downstream links of the supply chain, constituting nodes in the trade network; A is the directed adjacency matrix of different industry trade networks, and W is the weight matrix of different industry trade networks.

[0038] Furthermore, node degree refers to the number of direct edges of a node in the network. Node degree is divided into node out-degree and node in-degree, which correspond to the number of export and import connections of a country or region respectively. The calculation formula is as follows:

[0039]

[0040] Where: and Represent node out-degree, node in-degree and node degree respectively. Using network density Average clustering coefficient and the average path length etc. to measure the overall characteristics of the trade network.

[0041] Furthermore, the indicator W of the process of functional loss after the complex supply chain is disturbed is R Defined as:

[0042]

[0043] Where: R represents the remaining nodes in the network after some nodes fail, T [t] represents the global product trade volume in year t; W R It reflects the weight of the trade volume of the remaining nodes in the total trade volume after some nodes fail. R The rate of decline is negatively correlated with the resilience of the network. The higher the network resilience, the faster the W R The slower the rate of decline.

[0044] Furthermore, the spread of the supply crisis can be viewed as a cascading failure on a weighted directed network, where a large-scale avalanche of the network is caused by a node failure. When all nodes in the network are in a normal state, the total import and export trade volume of node i is defined as the initial load capacity T of node i. i , the initial load capacity of the edge from node i to node j is the export value w ij When the state of node i is abnormal, that is, the export volume of node i decreases by α, the weight w of all edges connected to the export of node i will decrease by α. If the import volume of any normal node j connected to the abnormal node changes by αw ij Exceeding node capacity T jIf the value of β (β is an adjustable parameter ranging from 0 to 1) is exceeded, node j will also become abnormal. Repeat the above steps until there are no new abnormal nodes in the network. Combined with the node status, the impact scope of the complex supply chain network under climate risk shocks and the degree of change in node resilience are measured.

[0045] This embodiment also discloses a complex supply chain network resilience simulation system under climate risk impact, including:

[0046] Risk Perturbation Module: Used to introduce different degrees of impact of climate risks in local areas on key trade links in complex supply chains;

[0047] Network construction module: used to construct complex supply chain networks, with countries and regions in the upstream and downstream links of the supply chain as nodes and corresponding trade volumes as edges, and represented by a matrix;

[0048] Node identification module: used to analyze the node characteristics of complex supply chain networks using node degree, node strength and coreness indicators;

[0049] Resilience Testing Module: This module is used to test the resilience of complex supply chain networks under different climate risk disturbance modes, taking into account the degree of node impact;

[0050] Cascade simulation module: used to measure the impact scope of the resilience of complex supply chain networks under a climate risk shock by combining cascading failures, and to identify the degree of risk disturbance in complex supply chain networks.

[0051] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0052] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulating the resilience of complex supply chain networks under climate risk shocks, characterized by: The following steps are involved: Use the proportion of different climate risk impact levels to measure the economic and social impact of disaster-affected countries and regions; A complex supply chain network is constructed using the countries and regions involved in the upstream and downstream links of the supply chain as nodes and the corresponding trade volumes as edges; Node degree, node strength and coreness indicators are used to analyze the key node characteristics of complex supply chain networks; Under different climate risk disturbance scenarios, the resilience of complex supply chain networks under climate risk is tested in combination with the degree of node impact; Combined with cascading failure simulation, the impact scope of the resilience of complex supply chain networks under climate risk shocks is measured, and the degree of risk disturbance of complex supply chain networks is identified.

2. The method for simulating the resilience of a complex supply chain network under climate risk shocks according to claim 1 is characterized in that: The impact ratio of different degrees of climate risk on key trade links in complex supply chains is introduced. For different climate risk scenarios w, the probability of occurrence Pw is expressed by the product of the value probabilities. The impact degree Measured in proportion to its economic and social impact.

3. The method for simulating the resilience of a complex supply chain network under climate risk shocks according to claim 1 is characterized in that: The matrix of complex supply chain network is represented as G=(V i ,V j ,A,W); Among them, V i ={v1,v2...v n } and V j ={v1,v2...v n } respectively represent the countries and regions involved in exporting and importing products, constituting nodes in the trade network; A is the directed adjacency matrix of different industry trade networks, and W is the weight matrix of different industry trade networks.

4. The method for simulating the resilience of a complex supply chain network under climate risk shocks according to claim 1 is characterized in that: Node degree refers to the number of direct edges connected to a node in the network. Node degree is divided into node out-degree and node in-degree, which correspond to the connection strength of a country or region's supply chain. The calculation formula is as follows: Where: and Represent node out-degree, node in-degree and node degree respectively; network density is used Average clustering coefficient and the average path length etc. to measure the overall characteristics of the trade network.

5. The method for simulating the resilience of a complex supply chain network under climate risk shocks according to claim 1 is characterized in that: The indicator W of the function loss process after the complex supply chain network is disturbed R Defined as: Where: R represents the remaining nodes in the network after some nodes fail, T [t] represents the global product trade volume in year t; W R It reflects the weight of the trade volume of the remaining nodes in the total trade volume after some nodes fail. R The rate of decline is negatively correlated with the resilience of the network. The higher the network resilience, the faster the W R The slower the rate of decline.

6. The method for simulating the resilience of a complex supply chain network under climate risk shocks according to claim 1 is characterized in that: The spread of the supply crisis is considered as a cascading failure on a weighted directed network, which is a large-scale network avalanche caused by a node failure. When all nodes in the network are in a normal state, the total import and export trade volume of node i is defined as the initial load capacity T of node i. i , the initial load capacity of the edge from node i to node j is the export value w ij When the state of node i is abnormal, that is, the export volume of node i decreases by α, the weight w of all edges connected to the export of node i will decrease by α; if the import volume of any normal node j connected to the abnormal node changes by αw ij Exceeding node capacity T j β is an adjustable parameter ranging from 0 to 1, then node j will also become abnormal; repeat the above steps until there are no new abnormal nodes in the network, and measure the impact scope of the complex supply chain network under risk shock and the degree of change in node resilience based on the node status.

7. A complex supply chain network resilience simulation system under climate risk impact, characterized by: include: Risk Perturbation Module: used to quantify the impact of climate risks in local areas on key trade links in complex supply chains; Network construction module: used to construct complex supply chain networks, with countries and regions that export and import products as nodes and corresponding trade volumes as edges, represented by a matrix; Node identification module: used to analyze the key node characteristics of complex supply chain networks using node degree, node strength and coreness indicators; Resilience Testing Module: This module is used to test the resilience of complex supply chain networks under different climate risk disturbance scenarios, taking into account the degree of node impact; Cascade simulation module: used to combine cascading failures to measure the impact scope and resilience changes of complex supply chains under climate risk shocks.

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