Carbon emission-based method and system for optimizing a scrap vehicle recycling supply chain

Abstract

The application discloses a carbon emission-based end-of-life vehicle recycling supply chain optimization method and system, comprising collecting data of each link of the end-of-life vehicle recycling supply chain, calculating cost and benefit data of end-of-life vehicle recycling and disassembly, and monitoring carbon emission data of each link of the end-of-life vehicle recycling supply chain; an evaluation index system is established based on the data, the dimensions include financial performance indicators, environmental performance indicators, technology and innovation performance indicators, management performance indicators, social and benefit performance indicators, based on an optimization algorithm, an optimization scheme is generated in combination with the evaluation index system, and the optimization scheme is fed back to the end-of-life vehicle recycling supply chain to complete end-of-life vehicle recycling supply chain performance evaluation and optimization. The method designed in the application can comprehensively consider resource recycling efficiency, carbon emission and economic benefit to realize green and sustainable development of the end-of-life vehicle recycling industry.

Description

Technical Field

[0001] This invention relates to the field of end-of-life vehicle recycling, specifically to a carbon emission-based supply chain optimization method for end-of-life vehicle recycling, and also to a carbon emission-based supply chain optimization system for end-of-life vehicle recycling. Background Technology

[0002] Traditional end-of-life vehicle recycling supply chain performance optimization systems and methods suffer from numerous shortcomings in terms of performance, functionality, cost, and carbon emissions. For example, traditional systems fail to comprehensively consider multiple dimensions of indicators, such as environmental and social impacts; they often neglect hidden costs, such as carbon emission costs and environmental remediation costs, leading to poor actual cost control; and they lack carbon emission monitoring and management functions, exhibiting serious deficiencies in carbon emission management within the supply chain management system. These systems typically cannot effectively assess and control carbon emissions within the supply chain, and they often overlook the application and promotion of low-carbon technologies during optimization, resulting in poor carbon emission control. These shortcomings limit the practical application effectiveness of the systems. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for optimizing the supply chain of end-of-life vehicle recycling based on carbon emissions. Through intelligent decision support and optimization algorithms, it improves the resource recycling efficiency of the supply chain, reduces carbon emissions, enhances economic benefits, and achieves a comprehensive improvement in supply chain performance.

[0004] To achieve the above functions, this invention designs a carbon emission-based supply chain optimization method for end-of-life vehicle recycling, executing the following steps S1-S7 to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain:

[0005] Step S1: Collect data from each link in the end-of-life vehicle recycling supply chain;

[0006] Step S2: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, calculate the cost and revenue data for end-of-life vehicle recycling and dismantling;

[0007] Step S3: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, monitor the carbon emission data of each link in the end-of-life vehicle recycling supply chain;

[0008] Step S4: Based on data from each link of the end-of-life vehicle recycling supply chain, cost and revenue data for end-of-life vehicle recycling and dismantling, and carbon emission data from each link of the end-of-life vehicle recycling supply chain, establish an evaluation index system. Dimensions include financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators to characterize the cost, efficiency, and revenue of each end-of-life vehicle recycling and dismantling in each dimension. Also, construct comprehensive evaluation indicators, including comprehensive carbon emission performance and comprehensive green supply chain performance, to characterize the comprehensive performance of each end-of-life vehicle recycling and dismantling.

[0009] Step S5: For the end-of-life vehicle recycling supply chain, generate an optimization scheme based on a multi-objective optimization algorithm and an evaluation index system; The steps are as follows: Step A. Establish decision variables, including whether to adjust the layout of recycling stations, whether to upgrade the dismantling line, whether to adjust the reuse path of parts, whether to replace diesel forklifts with electric forklifts, and whether to add visualization equipment; each decision variable takes the value of 0 or 1, when the value is 1, it means that the corresponding decision is yes, and when the value is 0, it means that the corresponding decision is no. Step B. Based on each decision variable, establish the objective function using a single-objective weighted approach. The specific formula is as follows: ; In the formula, Represents minimizing the objective function , Indicates overall carbon emission performance. Indicates the overall performance of the green supply chain; , They are respectively , Corresponding objective function weights; For the objective function, the following constraints are introduced: Tempo constraints: ; in, Represents the beat coefficient. Indicates the time constraint threshold. and Based on the specific end-of-life vehicle recycling supply chain environment; Indicate whether to upgrade to a semi-automatic disassembly line. , This indicates an upgrade to a semi-automatic disassembly line. This indicates that the manual disassembly line should be maintained; This indicates whether to upgrade to a fully automated disassembly line. , This indicates an upgrade to a fully automated disassembly line. This indicates that the manual disassembly line should be maintained; , They represent , The beat constraint weight; Carbon emission constraints: ; in, Indicates the carbon emission coefficient. This represents the carbon emission constraint threshold. and Based on the specific end-of-life vehicle recycling supply chain environment; Indicate whether to replace diesel forklifts with electric forklifts. , This indicates the replacement of diesel forklifts with electric forklifts. This indicates that electric forklifts will not be used to replace diesel forklifts; , , They represent , , Carbon emission constraint weights; Cost constraint: Total cost ≤ C; where C represents the cost constraint threshold. Payback period constraint: Payback period ≤ D; where D represents the payback period constraint threshold. Step C. Based on a multi-objective optimization algorithm, and with the comprehensive carbon emission performance not exceeding a preset threshold as a hard constraint, solve the objective function to obtain the decision variables that satisfy the constraints;

[0010] Step S6: Based on the generated optimization scheme, provide the corresponding decision-making for each participant in the end-of-life vehicle recycling supply chain.

[0011] Step S7: Based on the actual operating data and user feedback of the optimization plan and corresponding decisions, dynamically adjust the optimization plan to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain.

[0012] As a preferred embodiment of the present invention, the financial performance indicators mentioned in step S4 include: waste vehicle recycling cost X1, transportation cost X2, warehousing cost X3, environmental protection equipment usage cost X4, dismantling and processing cost X5, waste disposal cost X6, carbon emission cost X7, material sales revenue X8, dismantled parts sales revenue X9, and carbon emission revenue X1. 10 .

[0013] As a preferred embodiment of the present invention: the environmental performance indicators mentioned in step S4 include: end-of-life vehicle recycling rate X 11 Material reuse rate X 12 Degree of impact on the surrounding environment X 13Waste disposal compliance X 14 Total carbon emissions X 15 Carbon emissions per unit product X 16 Carbon emission intensity X 17 .

[0014] As a preferred embodiment of the present invention: the technical and innovation performance indicators mentioned in step S4 include: disassembly technology level X 18 The degree of application of low-carbon technologies X 19 Investment in carbon emission technology innovation X 20 Carbon emission reduction technology improvement effect X 21 .

[0015] As a preferred embodiment of the present invention: the management performance indicators mentioned in step S4 include: the status of carbon emission management system construction X 22 Carbon emission target setting and achievement status X 23 Carbon emission data monitoring and reporting quality X 24 Supplier carbon emission management requirements X 25 Customer carbon emission requirement response capability X 26 .

[0016] As a preferred embodiment of the present invention: the social and benefit performance indicators mentioned in step S4 include: customer satisfaction X 27 Employee learning rate X 28 Social impact of carbon emissions X 29 Reverse logistics management level X 30 .

[0017] As a preferred embodiment of the present invention: the comprehensive evaluation index mentioned in step S4 includes: comprehensive carbon emission performance X 31 Green supply chain comprehensive performance X 32 .

[0018] As a preferred technical solution of the present invention: the comprehensive carbon emission performance X 31 The calculation is as follows:

[0019] ;

[0020] In the formula, Indicates total carbon emissions. This indicates the carbon emissions per unit of product. Indicates carbon emission intensity; , , They represent the total carbon emissions. Carbon emissions per unit product Carbon emission intensity Corresponding weights, and satisfying ;

[0021] The aforementioned green supply chain comprehensive performance X 32 The calculation is as follows:

[0022] ;

[0023] In the formula, This represents the score of the i-th performance indicator. This represents the weight corresponding to the i-th performance indicator, where i=1, This indicates the weight corresponding to the financial performance indicators; when i=2, This indicates the weight corresponding to the environmental performance indicators; when i=3, This indicates the weights corresponding to the technology and innovation performance indicators; when i=4, This indicates the weight corresponding to the management performance indicators; when i=5, This indicates the weights corresponding to social and benefit performance indicators; and satisfies the following conditions: ;

[0024] The scores for each performance indicator are calculated as follows:

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] in, , , , , These are the scores for financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators, respectively. This indicates that the score of the j-th performance indicator is normalized; This represents the weight of the score for the j-th performance indicator.

[0031] This invention also designs a carbon emission-based end-of-life vehicle recycling supply chain optimization system. The carbon emission-based end-of-life vehicle recycling supply chain optimization method is implemented based on the following modules:

[0032] Data acquisition module: For the end-of-life vehicle recycling supply chain, it collects data from each link of the end-of-life vehicle recycling supply chain and calculates the cost and revenue data of end-of-life vehicle recycling and dismantling;

[0033] Carbon footprint monitoring module: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, monitor the carbon emission data of each link in the end-of-life vehicle recycling supply chain;

[0034] Performance Evaluation Module: Based on data from each link of the end-of-life vehicle recycling supply chain, cost and revenue data for end-of-life vehicle recycling and dismantling, and carbon emission data from each link of the end-of-life vehicle recycling supply chain, an evaluation indicator system is established. Dimensions include financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators to characterize the cost, efficiency, and revenue of each end-of-life vehicle recycling and dismantling in each dimension. A comprehensive evaluation indicator is also constructed, including comprehensive carbon emission performance and comprehensive green supply chain performance, to characterize the overall performance of each end-of-life vehicle recycling and dismantling.

[0035] Optimization Algorithm Module: For the end-of-life vehicle recycling supply chain, based on a multi-objective optimization algorithm and combined with an evaluation index system, optimization solutions are generated;

[0036] Decision support module: Based on the generated optimization scheme, it provides corresponding decision-making support for all participants in the end-of-life vehicle recycling supply chain.

[0037] Feedback and Adjustment Module: Based on actual operational data and user feedback regarding optimization plans and decisions, the module dynamically adjusts the optimization plans to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain.

[0038] As a preferred technical solution of the present invention: tolerance levels are set for comprehensive carbon emission performance and comprehensive green supply chain performance respectively. If the difference between the comprehensive carbon emission performance or comprehensive green supply chain performance and the corresponding tolerance level is greater than a preset threshold, the optimization algorithm module is automatically triggered to run again and generate a new optimization scheme.

[0039] Beneficial Effects: This invention designs a method and system for optimizing the supply chain of end-of-life vehicle recycling based on carbon emissions. It comprehensively considers multiple dimensions of indicators, including financial, environmental, technological, managerial, and social aspects, to achieve comprehensive optimization of supply chain performance and demonstrates corresponding advantages.

[0040] 1. Comprehensiveness: This invention constructs a comprehensive indicator system that considers not only traditional financial and efficiency indicators but also incorporates indicators from multiple dimensions such as environment, society, and technology. This comprehensive optimization method can more fully evaluate supply chain performance and provide a more comprehensive basis for corporate decision-making.

[0041] 2. Dynamism: This invention employs advanced data processing and analysis technologies, such as big data analytics and machine learning, enabling real-time processing and analysis of dynamic data within the supply chain. This dynamism allows the system to promptly reflect the actual operational status of the supply chain, thereby improving the accuracy and timeliness of decision-making.

[0042] 3. Collaboration: This invention establishes a supply chain collaboration mechanism, enabling data sharing and resource integration among upstream and downstream enterprises. This collaboration not only improves the operational efficiency of the supply chain but also enhances the competitiveness and market responsiveness of enterprises.

[0043] 4. Cost Control: This invention achieves effective cost control by optimizing each link in the supply chain. This cost control includes not only direct costs but also implicit and long-term costs, thereby improving the company's economic efficiency and sustainable development capabilities.

[0044] 5. Carbon Emission Management: This invention achieves effective assessment and control of carbon emissions in the supply chain by introducing carbon emission monitoring and management functions. This carbon emission management function not only helps enterprises reduce carbon emissions but also aligns with current environmental policies and market demands. Attached Figure Description

[0045] Figure 1 This is a flowchart of a carbon emission-based supply chain optimization method for end-of-life vehicle recycling, provided by an embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of the performance evaluation index system for the green supply chain of end-of-life vehicle recycling provided in an embodiment of the present invention.

[0047] Figure 3 This is a schematic diagram of a carbon emission-based end-of-life vehicle recycling supply chain optimization system provided in an embodiment of the present invention. Detailed Implementation

[0048] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0049] The carbon emission-based end-of-life vehicle recycling supply chain optimization method provided in this embodiment of the invention refers to... Figure 1 Perform the following steps S1-S7 to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain:

[0050] Step S1: Collect data from each link in the end-of-life vehicle recycling supply chain;

[0051] Step S2: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, calculate the cost and revenue data for end-of-life vehicle recycling and dismantling;

[0052] Step S3: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, monitor the carbon emission data of each link in the end-of-life vehicle recycling supply chain;

[0053] Step S4: Based on data from each link of the end-of-life vehicle recycling supply chain, cost and revenue data for end-of-life vehicle recycling and dismantling, and carbon emission data from each link of the end-of-life vehicle recycling supply chain, establish an evaluation index system. Dimensions include financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators to characterize the cost, efficiency, and revenue of each end-of-life vehicle recycling and dismantling in each dimension. Also, construct comprehensive evaluation indicators, including comprehensive carbon emission performance and comprehensive green supply chain performance, to characterize the comprehensive performance of each end-of-life vehicle recycling and dismantling.

[0054] Reference Figure 2 This invention constructs a comprehensive performance evaluation index system for the green supply chain of end-of-life vehicle recycling. It not only covers traditional multi-dimensional indicators such as financial, efficiency, environmental, social, and technological aspects, but also introduces carbon emission-related indicators, such as total carbon emissions, carbon emissions per unit of product, and carbon emission intensity. This system encompasses multiple dimensions including financial, environmental, technological, managerial, and social aspects. This more comprehensive index system can more accurately assess the performance of the green supply chain for end-of-life vehicle recycling, as detailed below:

[0055] The aforementioned financial performance indicators include: 1. End-of-life vehicle recycling cost; 2. Transportation cost; 3. Warehousing cost; 4. Environmental protection equipment usage cost; 5. Dismantling and processing cost; 6. Waste disposal cost; 7. Carbon emission cost; 8. Material sales revenue; 9. Dismantled parts sales revenue; 10. Carbon emission revenue. 10 .

[0056] The environmental performance indicators mentioned include: end-of-life vehicle recycling rate X 11 Material reuse rate X 12 Degree of impact on the surrounding environment X 13 Waste disposal compliance X 14 Total carbon emissions X 15 Carbon emissions per unit product X 16 Carbon emission intensity X 17 .

[0057] The aforementioned technology and innovation performance indicators include: dismantling technology level X 18 The degree of application of low-carbon technologies X 19 Investment in carbon emission technology innovation X 20 Carbon emission reduction technology improvement effect X21 .

[0058] The management performance indicators mentioned include: the status of carbon emission management system construction X 22 Carbon emission target setting and achievement status X 23 Carbon emission data monitoring and reporting quality X 24 Supplier carbon emission management requirements X 25 Customer carbon emission requirement response capability X 26 .

[0059] The aforementioned social and benefit performance indicators include: Customer satisfaction X 27 Employee learning rate X 28 Social impact of carbon emissions X 29 Reverse logistics management level X 30 .

[0060] The comprehensive evaluation indicators include: comprehensive carbon emission performance X 31 Green supply chain comprehensive performance X 32 .

[0061] The detailed evaluation indicator system is shown in Tables 1-6 below:

[0062] Table 1. Financial Performance Indicator System

[0063] Table 2. Environmental Performance Indicator System

[0064] Table 3. Technology and Innovation Performance Indicator System

[0065] Table 4. Management Performance Indicator System

[0066] Table 5. Social and Benefit Performance Indicator System

[0067] Table 6. Comprehensive Evaluation Index System

[0068] The aforementioned comprehensive carbon emission performance X 31 It is a comprehensive evaluation that takes into account indicators such as total carbon emissions, carbon emissions per unit of product, and carbon emission intensity. Its calculation uses a weighted reciprocal model, as shown in the following formula:

[0069] ;

[0070] In the formula, Represents total carbon emissions (tons) tons of carbon dioxide equivalent (the smaller the better); Indicates carbon emissions per unit of product (tons) / vehicle), the smaller the better; Indicates carbon emission intensity (tons) ( / ten thousand yuan revenue), the smaller the better; , , They represent the total carbon emissions. Carbon emissions per unit product Carbon emission intensity Corresponding weights, and satisfying It can be determined by AHP, entropy weight method or expert scoring; taking the reciprocal of the above formula is to transform the "smaller the better" indicator into a "larger the better" performance score;

[0071] The aforementioned green supply chain comprehensive performance X 32 Taking into account multiple dimensions such as financial, environmental, technological, managerial, and social factors, the calculation adopts a multi-level weighted linear model, as shown in the following formula:

[0072] ;

[0073] In the formula, This represents the score of the i-th performance indicator. This represents the weight corresponding to the i-th performance indicator, where i=1, This indicates the weight corresponding to the financial performance indicators; when i=2, This indicates the weight corresponding to the environmental performance indicators; when i=3, This indicates the weights corresponding to the technology and innovation performance indicators; when i=4, This indicates the weight corresponding to the management performance indicators; when i=5, This indicates the weights corresponding to social and benefit performance indicators;

[0074] The scores for each performance indicator are calculated as follows:

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] in, , , , , These are the scores for financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators, respectively. This indicates that the score of the j-th performance indicator is normalized (e.g., Min-Max standardization) to eliminate dimensions; The weight representing the score of the j-th performance indicator needs to be determined using the Analytic Hierarchy Process (AHP), entropy weight method, or expert scoring, and must satisfy the following conditions: .

[0081] The normalization method (Min-Max) is as follows:

[0082] For metrics where higher is always better (such as recovery rate, revenue):

[0083] ;

[0084] For indicators where smaller is better (such as carbon emissions and costs):

[0085] ;

[0086] In the formula, This represents the j-th indicator. This represents the maximum value of the j-th indicator. This represents the minimum value of the j-th indicator.

[0087] The details are shown in Table 7 below:

[0088] Table 7. Calculation methods for each indicator

[0089] Through the above indicator system, enterprises can comprehensively and systematically evaluate the performance of their green supply chain for end-of-life vehicle recycling, thereby formulating scientific and reasonable improvement measures to enhance their overall competitiveness and sustainable development capabilities.

[0090] Step S5: For the end-of-life vehicle recycling supply chain, generate an optimization scheme based on a multi-objective optimization algorithm and an evaluation index system;

[0091] In step S5, decision variables are established for the layout of recycling sites, dismantling process, and parts reuse path in the end-of-life vehicle recycling supply chain. An optimization objective function is established based on the carbon emissions and performance indicators of end-of-life vehicle recycling and dismantling. Constraints on the optimization objective function are introduced based on the decision variables. A multi-objective optimization algorithm is used to solve the optimization objective function and generate an optimization scheme.

[0092] Step S6: Based on the generated optimization plan, provide the participants in each link of the end-of-life vehicle recycling supply chain with corresponding decisions on the optimization plan, including resource allocation suggestions and cooperation model suggestions;

[0093] Step S7: Based on the actual operating data and user feedback of the optimization plan and corresponding decisions, dynamically adjust the optimization plan to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain.

[0094] The following is an application example of the method designed in this invention:

[0095] Application Background:

[0096] Vehicle type: A scrapped car with a total weight of 1255 kg;

[0097] Original process: Manual disassembly of line L1;

[0098] Target: Carbon emissions ≤ 85 / vehicle, dismantling costs decreased by 12%, and cycle time decreased by 25%;

[0099] The data collected is shown in Table 8 below:

[0100] Table 8. Data Collection Form for End-of-Life Vehicles

[0101] The steps for generating an optimal solution using a multi-objective optimization algorithm are as follows:

[0102] Step A. Establish decision variables, including , , , The details are as follows:

[0103] Indicate whether to upgrade to the semi-automatic disassembly line L2. , This indicates an upgrade to a semi-automatic disassembly line L2. This indicates that manual disassembly line L1 should be maintained;

[0104] This indicates whether to upgrade to the fully automated disassembly line L3. , This indicates an upgrade to a fully automated disassembly line, L3. This indicates that manual disassembly line L1 should be maintained;

[0105] Indicate whether to replace diesel forklifts with electric forklifts. , This indicates the replacement of diesel forklifts with electric forklifts. This indicates that electric forklifts will not be used to replace diesel forklifts;

[0106] Indicate whether to add a visual tightening gun. , This indicates the addition of a visual tightening gun. This indicates that a visual tightening gun will not be added;

[0107] Step B. Based on each decision variable, establish the objective function using a single-objective weighted approach. The specific formula is as follows:

[0108] ;

[0109] in, Represents minimizing the objective function , Indicates overall carbon emission performance. Indicates the overall performance of the green supply chain;

[0110] For the objective function, the following constraints are introduced:

[0111] Tempo constraints: ; Reduce the beat rate by ≥25%;

[0112] Carbon emission constraints: ;

[0113] Cost constraints: Total cost ≤ 1.2 million yuan; Payback period ≤ 18 months;

[0114] Step C. Using Python PuLP 0.1 s, and with the overall carbon emission performance not exceeding a preset threshold as a hard constraint, solve the objective function and generate the optimization scheme as shown in Table 9 below:

[0115] Table 9. Optimization Scheme

[0116] The results (average of 30 vehicles tested) are shown in Table 10 below:

[0117] Table 10. Results Comparison

[0118] Step D. Convert the generated optimization plan into implementation instructions and send them to on-site workers. Implementation instructions can take the form of MES dashboards, forklift driver apps, and real-time carbon credit messages, as detailed below:

[0119] MES dashboard: "The process route for this batch of vehicles has been switched to L2-semi-automatic, with a cycle time target of 27 minutes. Please execute according to SOP-20-Rev.C."

[0120] Forklift Driver App: "Starting today, diesel forklifts F3 and F5 are no longer in use. Please scan the code to receive electric forklifts E1 and E2."

[0121] Instant carbon credit message: "Your vehicle batch has reduced emissions by 19 kgCO2e per vehicle, and you have earned 19 carbon credits (=15.2 yuan)."

[0122] This embodiment upgrades the "manual line" to a "semi-automatic + electric forklift + error-proof gun" to optimize the dismantling process and further improve the carbon emission management function. By introducing a carbon emission monitoring, reporting and verification system, it achieves comprehensive monitoring and management of carbon emissions in the supply chain.

[0123] This invention also provides a carbon emission-based end-of-life vehicle recycling supply chain optimization system, referring to... Figure 3 To realize the carbon emission-based end-of-life vehicle recycling supply chain optimization method, the method specifically includes the following modules:

[0124] Data acquisition module: Collects data from each link of the end-of-life vehicle recycling supply chain, as well as cost and revenue data for vehicle recycling and dismantling;

[0125] In this embodiment, the target area contains multiple scrapped vehicle recycling sites, dismantling companies, and parts recycling companies. Through the data acquisition module, the recycling volume of each recycling site, the dismantling efficiency of each dismantling company, the reuse rate of each parts recycling company, and the transportation data of logistics companies are collected.

[0126] Carbon footprint monitoring module: Monitors carbon emission data at each stage of the end-of-life vehicle recycling supply chain;

[0127] Performance Evaluation Module: Based on data from each link of the end-of-life vehicle recycling supply chain, cost and revenue data for end-of-life vehicle recycling and dismantling, and carbon emission data from each link of the end-of-life vehicle recycling supply chain, an evaluation indicator system is established. Dimensions include financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators to characterize the cost, efficiency, and revenue of each end-of-life vehicle recycling and dismantling in each dimension. A comprehensive evaluation indicator is also constructed, including comprehensive carbon emission performance and comprehensive green supply chain performance, to characterize the overall performance of each end-of-life vehicle recycling and dismantling.

[0128] In this embodiment, the performance evaluation module quantifies the resource recycling efficiency, carbon emission level, and economic benefits of the supply chain based on a preset evaluation index system. For example, the target value for resource recycling efficiency is set at 80%, the target value for carbon emissions is no more than 100 kg of CO2 equivalent per vehicle, and the target value for economic benefits is no less than 5,000 yuan per vehicle for recycling revenue.

[0129] Optimization Algorithm Module: For the end-of-life vehicle recycling supply chain, based on a multi-objective optimization algorithm and combined with an evaluation index system, optimization solutions are generated;

[0130] In this embodiment, the optimization algorithm module employs a multi-objective optimization algorithm, combining collected data and carbon footprint monitoring data to generate optimization solutions. For example, it optimizes the layout of recycling sites by concentrating them in areas with convenient transportation and high recycling volumes; it optimizes the dismantling process by introducing automated dismantling equipment to improve efficiency; and it optimizes the reuse pathways for components by prioritizing the allocation of high-value components to reuse companies.

[0131] Tolerance levels are set for both comprehensive carbon emission performance and comprehensive green supply chain performance. If the difference between the comprehensive carbon emission performance or comprehensive green supply chain performance and the corresponding tolerance level is greater than the preset threshold, the optimization algorithm module will be automatically triggered to run again and generate a new optimization scheme.

[0132] Decision Support Module: Based on the optimization plan, this module provides corresponding decision-making support to all participants in the supply chain. For example, it offers resource allocation suggestions to recycling sites, equipment upgrade suggestions to dismantling companies, and cooperation model suggestions to parts recycling companies.

[0133] Feedback and Adjustment Module: Dynamically adjusts the optimization plan based on actual operating data and user feedback. For example, if actual operating data shows that the recycling volume of a certain recycling station is lower than expected, the layout of the recycling stations can be adjusted to increase the number of recycling stations in that area.

[0134] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A supply chain optimization method for end-of-life vehicle recycling based on carbon emissions, characterized in that, Perform the following steps S1-S7 to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain: Step S1: Collect data from each link in the end-of-life vehicle recycling supply chain; Step S2: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, calculate the cost and revenue data for end-of-life vehicle recycling and dismantling; Step S3: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, monitor the carbon emission data of each link in the end-of-life vehicle recycling supply chain; Step S4: Based on data from each link of the end-of-life vehicle recycling supply chain, cost and revenue data for end-of-life vehicle recycling and dismantling, and carbon emission data from each link of the end-of-life vehicle recycling supply chain, establish an evaluation index system. Dimensions include financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators to characterize the cost, efficiency, and revenue of each end-of-life vehicle recycling and dismantling in each dimension. Also, construct comprehensive evaluation indicators, including comprehensive carbon emission performance and comprehensive green supply chain performance, to characterize the comprehensive performance of each end-of-life vehicle recycling and dismantling. Step S5: For the end-of-life vehicle recycling supply chain, generate an optimization scheme based on a multi-objective optimization algorithm and an evaluation index system; The steps are as follows: Step A. Establish decision variables, including whether to adjust the layout of recycling sites, whether to upgrade the dismantling line, whether to adjust the reuse path of parts, whether to replace diesel forklifts with electric forklifts, and whether to add visualization equipment; Each decision variable takes the value 0 or 1. A value of 1 indicates that the corresponding decision is yes, and a value of 0 indicates that the corresponding decision is no. Step B. Based on each decision variable, establish the objective function using a single-objective weighted approach. The specific formula is as follows: ； In the formula, Represents minimizing the objective function , Indicates overall carbon emission performance. Indicates the overall performance of the green supply chain; , They are respectively , Corresponding objective function weights; For the objective function, the following constraints are introduced: Tempo Constraints: ; in, Represents the beat coefficient. Indicates the time constraint threshold. and Based on the specific end-of-life vehicle recycling supply chain environment; Indicate whether to upgrade to a semi-automatic disassembly line. , This indicates an upgrade to a semi-automatic disassembly line. This indicates that the manual disassembly line should be maintained; This indicates whether to upgrade to a fully automated disassembly line. , This indicates an upgrade to a fully automated disassembly line. This indicates that the manual disassembly line should be maintained; , They represent , The beat constraint weight; Carbon emission constraints: ; in, Indicates the carbon emission coefficient. This represents the carbon emission constraint threshold. and Based on the specific end-of-life vehicle recycling supply chain environment; Indicate whether to replace diesel forklifts with electric forklifts. , This indicates the replacement of diesel forklifts with electric forklifts. This indicates that electric forklifts will not be used to replace diesel forklifts; , , They represent , , Carbon emission constraint weights; Cost constraint: Total cost ≤ C; where C represents the cost constraint threshold. Payback period constraint: Payback period ≤ D; where D represents the payback period constraint threshold. Step C. Based on a multi-objective optimization algorithm, and with the comprehensive carbon emission performance not exceeding a preset threshold as a hard constraint, solve the objective function to obtain the decision variables that satisfy the constraints; Step S6: Based on the generated optimization scheme, provide the corresponding decision-making for each participant in the end-of-life vehicle recycling supply chain. Step S7: Based on the actual operating data and user feedback of the optimization plan and corresponding decisions, dynamically adjust the optimization plan to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain.

2. The method for optimizing the supply chain of end-of-life vehicle recycling based on carbon emissions according to claim 1, characterized in that, The financial performance indicators mentioned in step S4 include: Scrap vehicle recycling cost X1, transportation cost X2, warehousing cost X3, environmental protection equipment usage cost X4, dismantling and processing cost X5, waste disposal cost X6, carbon emission cost X7, material sales revenue X8, dismantled parts sales revenue X9, and carbon emission revenue X1. 10 .

3. The carbon emission-based end-of-life vehicle recycling supply chain optimization method according to claim 2, characterized in that, The environmental performance indicators mentioned in step S4 include: end-of-life vehicle recycling rate X 11 Material reuse rate X 12 Degree of impact on the surrounding environment X 13 Waste disposal compliance X 14 Total carbon emissions X 15 Carbon emissions per unit product X 16 Carbon emission intensity X 17 .

4. The carbon emission-based end-of-life vehicle recycling supply chain optimization method according to claim 3, characterized in that, The technology and innovation performance indicators mentioned in step S4 include: Deconstruction of technology level X 18 The degree of application of low-carbon technologies X 19 Investment in carbon emission technology innovation X 20 Carbon emission reduction technology improvement effect X 21 .

5. The carbon emission-based end-of-life vehicle recycling supply chain optimization method according to claim 4, characterized in that, The management performance indicators mentioned in step S4 include: the status of carbon emission management system construction X 22 Carbon emission target setting and achievement status X 23 Carbon emission data monitoring and reporting quality X 24 Supplier carbon emission management requirements X 25 Customer carbon emission requirement response capability X 26 .

6. The carbon emission-based end-of-life vehicle recycling supply chain optimization method according to claim 5, characterized in that, The social and benefit performance indicators mentioned in step S4 include: customer satisfaction X 27 Employee learning rate X 28 Social impact of carbon emissions X 29 Reverse logistics management level X 30 .

7. The carbon emission-based end-of-life vehicle recycling supply chain optimization method according to claim 6, characterized in that, The comprehensive evaluation indicators mentioned in step S4 include: comprehensive carbon emission performance X 31 Green supply chain comprehensive performance X 32 .

8. The carbon emission-based end-of-life vehicle recycling supply chain optimization method according to claim 7, characterized in that, The aforementioned comprehensive carbon emission performance X 31 The calculation is as follows: ； In the formula, Indicates total carbon emissions. This indicates the carbon emissions per unit of product. Indicates carbon emission intensity; , , They represent the total carbon emissions. Carbon emissions per unit product Carbon emission intensity Corresponding weights, and satisfying ; The aforementioned green supply chain comprehensive performance X 32 The calculation is as follows: ； In the formula, This represents the score of the i-th performance indicator. This represents the weight corresponding to the i-th performance indicator, where i=1, This indicates the weight corresponding to the financial performance indicators; when i=2, This indicates the weight corresponding to the environmental performance indicators; when i=3, This indicates the weights corresponding to the technology and innovation performance indicators; when i=4, This indicates the weight corresponding to the management performance indicators; when i=5, This indicates the weights corresponding to social and benefit performance indicators; and satisfies the following conditions: ; The scores for each performance indicator are calculated as follows: ； ； ； ； ； in, , , , , These are the scores for financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators, respectively. This indicates that the score of the j-th performance indicator is normalized; This represents the weight of the score for the j-th performance indicator.

9. A carbon emission-based end-of-life vehicle recycling supply chain performance optimization system, characterized in that, The carbon emission-based end-of-life vehicle recycling supply chain optimization method according to any one of claims 1-8 is implemented based on the following modules: Data acquisition module: For the end-of-life vehicle recycling supply chain, it collects data from each link of the end-of-life vehicle recycling supply chain and calculates the cost and revenue data of end-of-life vehicle recycling and dismantling; Carbon footprint monitoring module: Based on the collected data from each link of the end-of-life vehicle recycling supply chain, monitor the carbon emission data of each link in the end-of-life vehicle recycling supply chain; Performance Evaluation Module: Based on data from each link of the end-of-life vehicle recycling supply chain, cost and revenue data for end-of-life vehicle recycling and dismantling, and carbon emission data from each link of the end-of-life vehicle recycling supply chain, an evaluation indicator system is established. Dimensions include financial performance indicators, environmental performance indicators, technological and innovation performance indicators, management performance indicators, and social and benefit performance indicators to characterize the cost, efficiency, and revenue of each end-of-life vehicle recycling and dismantling in each dimension. A comprehensive evaluation indicator is also constructed, including comprehensive carbon emission performance and comprehensive green supply chain performance, to characterize the overall performance of each end-of-life vehicle recycling and dismantling. Optimization Algorithm Module: For the end-of-life vehicle recycling supply chain, based on a multi-objective optimization algorithm and combined with an evaluation index system, optimization solutions are generated; Decision support module: Based on the generated optimization scheme, it provides corresponding decision-making support for all participants in the end-of-life vehicle recycling supply chain. Feedback and Adjustment Module: Based on actual operational data and user feedback regarding optimization plans and decisions, the module dynamically adjusts the optimization plans to complete the performance evaluation and optimization of the end-of-life vehicle recycling supply chain.

10. The carbon emission-based end-of-life vehicle recycling supply chain optimization system according to claim 9, characterized in that, Tolerance levels are set for both comprehensive carbon emission performance and comprehensive green supply chain performance. If the difference between the comprehensive carbon emission performance or comprehensive green supply chain performance and the corresponding tolerance level is greater than the preset threshold, the optimization algorithm module will be automatically triggered to run again and generate a new optimization scheme.

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