Hybrid flow shop scheduling optimization method and system based on equipment green degree ranking

Abstract

The present application relates to mixed flow shop scheduling optimization technical field, particularly to a mixed flow shop scheduling optimization method and system based on equipment green degree sequencing, wherein the method comprises: constructing a comprehensive green degree evaluation matrix of each equipment based on the comprehensive green degree evaluation index system; calculating the relative progress and grey correlation degree of each equipment respectively according to the comprehensive green degree evaluation matrix of each equipment; determining the comprehensive green degree evaluation value of each equipment according to the relative progress and grey correlation degree; sequencing according to the comprehensive green degree evaluation value of each equipment from large to small, and selecting the initial population from the sequenced equipment set according to the set selection strategy; taking the initial population as the input value of the differential evolution algorithm, and obtaining the optimal solution after executing the differential evolution algorithm, which is taken as the mixed flow shop scheduling optimization result. The present application efficiently and reasonably generates the mixed flow shop scheduling scheme.

Description

Technical Field

[0001] This invention relates to the field of hybrid flow workshop scheduling optimization technology, and in particular to a method and system for hybrid flow workshop scheduling optimization based on equipment greenness ranking. Background Technology

[0002] Hybrid flow workshop production comprises multiple production stages, each with numerous parallel processing devices. Equipment selection directly impacts production efficiency, product quality, and production costs, significantly affecting a company's development. Currently, companies often rely on worker experience or the availability of idle equipment, making it difficult to guarantee optimal production efficiency. This type of workshop production is influenced and constrained by environmental emissions, delivery deadlines, and production costs, requiring a comprehensive consideration of various factors and indicators to guide the generation of a reasonable and optimized production plan. Existing hybrid flow workshop scheduling optimization methods, which utilize quantitative models of the workshop's production process, neglect the characteristics of parallel equipment, resulting in largely passive equipment selection and a pressing need to improve optimization efficiency. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing the scheduling of hybrid flow workshops based on equipment greenness ranking. This invention transforms passive equipment scheduling into active scheduling, proposing a method for optimizing the scheduling of hybrid flow workshops based on comprehensive equipment greenness ranking. It evaluates and ranks parallel equipment based on comprehensive greenness and generates a hybrid flow workshop scheduling scheme efficiently and reasonably through directional operations of differential evolution operators.

[0004] On the one hand, a method for optimizing the scheduling of hybrid production lines based on equipment greenness ranking is provided, including:

[0005] For mixed-flow workshops, a comprehensive greenness evaluation index system should be constructed.

[0006] Based on the comprehensive greenness evaluation index system, a comprehensive greenness evaluation matrix for each device is constructed.

[0007] Based on the comprehensive greenness evaluation matrix of each device, the relative progress and grey relational degree are calculated respectively; based on the relative progress and grey relational degree, the comprehensive greenness evaluation value of each device is determined.

[0008] The devices are sorted in descending order of their comprehensive greenness assessment value, and an initial population is selected from the sorted device set according to the set selection strategy.

[0009] The initial population is used as the input value of the differential evolution algorithm. After executing the differential evolution algorithm, the optimal solution is obtained, and the optimal solution is used as the scheduling optimization result of the hybrid flow workshop.

[0010] On the other hand, a hybrid production line scheduling optimization system based on equipment greenness ranking is provided, including:

[0011] The system construction module is configured to: construct a comprehensive greenness evaluation index system for the mixed flow workshop to be scheduled;

[0012] The matrix module is configured to: construct a comprehensive greenness evaluation matrix for each device based on the comprehensive greenness evaluation index system;

[0013] The evaluation degree determination module is configured to: calculate the relative progress and gray relational degree based on the comprehensive greenness evaluation matrix of each device; and determine the comprehensive greenness evaluation value of each device based on the relative progress and gray relational degree.

[0014] The initial population screening module is configured to sort the devices in descending order of their comprehensive greenness assessment values ​​and select the initial population from the sorted device set according to a set selection strategy.

[0015] The output module is configured to take the initial population as the input value of the differential evolution algorithm, execute the differential evolution algorithm to obtain the optimal solution, and use the optimal solution as the result of the hybrid flow shop scheduling optimization.

[0016] The above technical solution has the following advantages or beneficial effects:

[0017] Based on the characteristics of hybrid flow workshop production, this paper focuses on the selection of parallel equipment, considering carbon emissions, efficiency, cost, and basic performance indicators during equipment processing. A multi-attribute evaluation system for the comprehensive greenness of equipment is constructed to comprehensively assess the processing status of each piece of equipment and rank them by their overall greenness. Based on this ranking, the differential evolution solution process is improved by using directed operations on the initial population and mutation operators to actively select iterative variables, efficiently and reasonably generating scheduling optimization schemes. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0019] Figure 1 This is a flowchart of the method in Example 1. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] Hybrid production line manufacturing is a comprehensive and complex process that requires consideration of both the economic benefits of enterprise development and ensuring that product performance meets diverse customer needs. In recent years, with the establishment of carbon emissions trading markets and the introduction of related policies, the requirements and restrictions on carbon emissions have become more stringent, necessitating the inclusion of carbon emissions during the production process in overall considerations. Developing a production plan that integrates environmental and economic factors can ensure stable production and enhance the enterprise's competitiveness and development potential.

[0022] In hybrid flow workshops, the selection of parallel equipment plays a crucial role. A hybrid flow workshop is characterized by its ability to process multiple different products or parts simultaneously, with these products or parts flowing along the production line in a specific order and rhythm. In such a production environment, the selection of parallel equipment directly impacts multiple aspects, including production efficiency and costs. A comprehensive consideration of various factors and indicators is necessary to guide the workshop in generating a reasonable and optimized production plan.

[0023] Existing scheduling methods in hybrid assembly line workshops primarily rely on basic indicators such as production efficiency, lacking a comprehensive multi-attribute consideration mechanism and evaluation system. This results in scheduling schemes that are rather one-sided and lack rationality. Furthermore, companies initially rely heavily on worker experience or the current idle status of equipment to select equipment and determine the schedule, a method that struggles to guarantee the rationality of production.

[0024] With the development of intelligent algorithms, when solving problems in hybrid flow workshops, it is common practice to combine industry-specific constraints (such as delivery time constraints) or directly construct a multi-objective quantitative model of the production process, obtaining a workshop production scheduling solution through iterative optimization of the algorithm. However, this scheduling optimization process ignores the characteristics of parallel equipment, and equipment selection is often passive, resulting in a pressing need to improve solution efficiency.

[0025] In contrast, this invention constructs a comprehensive greenness index system for equipment and proposes a hybrid flow workshop scheduling optimization method based on equipment greenness ranking. This transforms passive equipment scheduling into active scheduling. Based on the ranking of comprehensive greenness of equipment and through the directional operation of differential evolution operators, the convergence speed of the algorithm is improved, and a hybrid flow workshop scheduling scheme is generated efficiently and reasonably.

[0026] Example 1, as Figure 1 As shown, this embodiment provides a method for optimizing the scheduling of a hybrid production line based on the greenness ranking of equipment, including:

[0027] S101: For mixed-flow workshops requiring scheduling, construct a comprehensive greenness evaluation index system;

[0028] S102: Based on the comprehensive greenness evaluation index system, construct a comprehensive greenness evaluation matrix for each device;

[0029] S103: Calculate the relative progress and grey relational degree based on the comprehensive greenness evaluation matrix of each device; determine the comprehensive greenness evaluation value of each device based on the relative progress and grey relational degree.

[0030] S104: Sort the devices according to their comprehensive greenness assessment values ​​from largest to smallest, and select the initial population from the sorted device set according to the set selection strategy;

[0031] S105: Use the initial population as the input value of the differential evolution algorithm. After executing the differential evolution algorithm, the optimal solution is obtained. The optimal solution is used as the scheduling optimization result of the hybrid flow workshop.

[0032] Further, in S101: For the mixed-flow workshop to be scheduled, construct a comprehensive greenness evaluation index system; the comprehensive greenness evaluation index system includes:

[0033] Primary indicator: Overall greenness of equipment;

[0034] The primary indicators include four secondary indicators, which are: processing carbon emissions, processing time, processing cost, and basic equipment performance.

[0035] The carbon emissions from the processing include three tertiary indicators: resource consumption, energy consumption, and direct carbon emissions.

[0036] The processing time includes four tertiary indicators: clamping time, disassembly time, cutting time, and tool change time;

[0037] The processing cost includes three levels of indicators: material cost, labor cost, electricity consumption cost, equipment depreciation cost, and maintenance and repair cost.

[0038] The basic performance of the equipment includes three levels of indicators: processing accuracy, stability, and degree of automation.

[0039] Furthermore, the carbon emissions from the processing Greenhouse gas emissions refer to the amount of energy, resource consumption, and chemical reactions generated during the process of transforming a workpiece from a raw material to the completed product, resulting in a specified manufacturing process. (Processing carbon emissions) This mainly refers to carbon dioxide emissions.

[0040] ;

[0041] in, For auxiliary materials consumed in the manufacturing process. The carbon intensity coefficient of energy consumption for auxiliary materials. Energy consumption in the manufacturing process. Energy carbon emission intensity coefficient, Direct carbon emissions generated by manufacturing processes.

[0042] Furthermore, the processing time It refers to the total time consumed in the product manufacturing or processing process, from processing raw materials or semi-finished products to completing the specified processing technology requirements or processing tasks, including direct processing time and indirect processing time.

[0043] ;

[0044] Direct processing time refers to the time required for raw materials to be directly processed on the equipment, i.e., the cutting time of the parts. Indirect machining time refers to the time related to direct machining but not directly involved in the machining process, including clamping time. Tool change time Disassembly time wait.

[0045] Furthermore, the processing cost This refers to the total cost incurred during the manufacturing process of electromechanical product parts:

[0046] ;

[0047] in, Indicates material cost, Indicates labor costs, This indicates equipment depreciation costs. Indicates the cost of electricity consumption. This indicates maintenance and repair costs.

[0048] Furthermore, the basic performance of the equipment refers to the core capabilities and characteristics exhibited by the equipment when fulfilling its designed functions. These capabilities and characteristics directly determine the equipment's processing quality and adaptability. The basic performance of the equipment is measured by processing accuracy. ,stability and degree of automation Characterize it.

[0049] The three basic performance indicators of the equipment are mostly qualitative. Triangular fuzzy numbers are used to quantify these qualitative indicators by constructing membership functions for the fuzzy numbers. This transforms qualitative indicators into quantitative indicators with fuzziness.

[0050] Furthermore, the quantitative transformation of qualitative indicators using triangular fuzzy numbers includes:

[0051] (1) For each indicator, construct a triangular fuzzy number; the triangular fuzzy number Represented as ,in, As the lower limit, Centered on (most likely value) The upper limit;

[0052] (2) Determine the membership function:

[0053] For triangular fuzzy numbers Its membership function Represented as a piecewise function:

[0054] when hour, ;

[0055] when hour, ;

[0056] when hour, ;

[0057] (3) Calculate the quantitative value: based on the membership function and specific Value (i.e., actual indicator value), calculate quantitative value For example, the triangular fuzzy number corresponding to a certain equipment's basic performance index is... Actual indicator value ,because, Then the membership degree ,but .

[0058] It should be understood that when confirming the scheduling plan, a mixed-flow workshop needs to select a reasonable processing equipment for the workpiece from multiple parallel machines. It needs to comprehensively consider the environmental impact, cost, processing efficiency, and basic performance of the equipment when the workpiece is processed on that equipment.

[0059] Further, in step S102: based on the comprehensive greenness evaluation index system, a comprehensive greenness evaluation matrix for each device is constructed, including:

[0060] Identify the parallel equipment and evaluation indicators in the mixed-flow workshop, and construct a comprehensive greenness evaluation matrix for each piece of equipment. As shown in formula (1).

[0061] , formula (1);

[0062] in, Indicates the number of parallel devices. m Indicates the number of evaluation indicators in this invention. m =4; Indicates the first The first of the equipment The four indicators are: the first is the carbon emission rate during processing, the second is the processing time, the third is the processing cost, and the fourth is the basic performance of the equipment. Indicates the first The carbon emission index value of the parallel processing equipment. Indicates the first Processing time of multiple parallel machines; Indicates the first The processing cost of parallel equipment Indicates the first Basic performance of the parallel equipment.

[0063] The obtained comprehensive greenness evaluation matrix is ​​standardized to eliminate the influence of different dimensions. The standardization process is shown in formula (2), and the obtained standardized matrix is ​​shown in formula (3).

[0064] , formula (2);

[0065] , formula (3);

[0066] in, Indicates the first The first of the equipment The standardized value of each indicator.

[0067] Further, in step S103: based on the comprehensive greenness evaluation matrix of each device, the relative alignment progress and gray relational degree are calculated respectively; wherein, the calculation process of the relative alignment progress includes:

[0068] S103-a1: Based on the standardized matrix Calculate the first The first indicator The proportion of features of each evaluation object ,

[0069] , formula (4);

[0070] And based on the characteristic proportion , obtain the Entropy value of each indicator :

[0071] , formula (5);

[0072] Further calculate the coefficient of difference Then the first Weight of each indicator As shown in equations (4), (5), and (6) respectively.

[0073] , formula (6);

[0074] S103-a2: Based on the standardized matrix Get the dataset

[0075] Positive Ideal Solution ;

[0076] Negative ideal solution ;

[0077] Calculate the distance between the evaluated index and the positive and negative ideal solutions:

[0078] , formula (7);

[0079] , formula (8);

[0080] S103-a3: Obtain the first The score of each evaluation object , As shown in equation (9), where, , The larger the value, the closer it is to the optimal value;

[0081] , formula (9);

[0082] Further, in step S103: based on the comprehensive greenness evaluation matrix of each device, the relative alignment progress and gray relational degree are calculated respectively, wherein the calculation process of the gray relational degree includes:

[0083] The default parent sequence is obtained based on formula (3). ,in, Find the maximum value of each column in equation (3); construct the difference matrix. As shown in formula (10);

[0084] , formula (10);

[0085] Calculate the grey relational coefficient of the index As shown in formula (11).

[0086] , formula (11);

[0087] in, The resolution coefficient is denoted as . This represents the minimum value among all values ​​in the difference matrix K. This represents the maximum value among all values ​​in the difference matrix K.

[0088] Furthermore, the grey relational degree is obtained, which is the evaluation result. For example, in formula (12):

[0089] , formula (12).

[0090] Furthermore, the determination of the comprehensive greenness assessment value for each device based on relative application progress and grey relational degree includes:

[0091] Based on the product method, the grey relational degree and the relative proximity degree are combined to obtain a comprehensive evaluation value. As shown in equation (13), the overall greenness of parallel devices is then ranked, and the ranking number is... .

[0092] , formula (13).

[0093] Further, in step S104: the devices are sorted in descending order of their comprehensive greenness evaluation values, and an initial population is selected from the sorted device set according to a set selection strategy;

[0094] Differential evolution often uses random initialization of the population, which may suffer from insufficient representativeness, susceptibility to local optima, and slower convergence. This invention proposes a directed population initialization method. Based on the overall greenness ranking of parallel devices, the initial population is selected using a logistic function. Devices ranked higher have a higher selection probability, while those ranked lower have a lower selection probability. The device selection probabilities are as follows:

[0095] , formula (14).

[0096] in, Indicates the first The probability of a device being selected; It is an adjustable parameter used to control the steepness of the selection probability distribution. The larger the value, the greater the difference in selection probability among the top-ranked devices. The smaller the value, the smaller the difference in selection probability among the top-ranked devices; Indicates the first The overall greenness value of each device; and These represent the minimum and maximum values ​​of overall greenness among all devices, respectively.

[0097] Further, in step S105: the initial population is used as the input value for the differential evolution algorithm. After executing the differential evolution algorithm, the optimal solution is obtained, and the optimal solution is used as the result of the hybrid flow shop scheduling optimization, including:

[0098] S105-1: Initialize the population;

[0099] S105-2: Fitness Function: Based on the constructed comprehensive greenness evaluation system, the fitness function is optimized by minimizing the comprehensive greenness.

[0100] Objective function:

[0101] ;

[0102] Constraints:

[0103] ;

[0104] in, Indicates the total carbon emission weight of the workshop. This indicates the weight of the total completion time of the workshop. Indicates the weight of total workshop cost. Indicates the overall performance weight of the workshop; This indicates the total carbon emissions from workshop production; Indicates the total completion time of production in the workshop; This represents the total production cost in the workshop. Indicates the overall production performance of the workshop; Indicates the total carbon emission constraints of the workshop; To constrain the delivery time of workshop production; Constraints on total production costs in the workshop; Constraints on overall production performance in the workshop; This represents a normalization function, the goal of which is to solve the problem of indicators with different dimensions. This indicates the maximum value of the indicator. This represents the minimum value of the indicator. The value is related to the workshop production plan and is generally provided by the production workshop.

[0105] S105-3: Variation: When performing variation operations on equipment in a mixed-flow workshop, based on the overall greenness ranking results of each parallel device, select the device before ranking. Bit-sized devices as mutation candidates And assign a preference factor to each device according to the ranking. ,in, The value increases as the device is ranked higher (i.e., devices ranked higher have a larger value). (Value). For the device variable to be mutated. ,from Select a device variable As the target of mutation, calculate the mutation vector. ;

[0106] ;

[0107] ;

[0108] in, Ranking based on the overall greenness of parallel devices The results are dynamically adjusted, with those ranked higher being sorted. , The larger the value, the closer the mutated value is to the original value. The later the sorting... , The smaller the value; This indicates the total number of parallel devices.

[0109] S105-4: Crossover: A crossover operation is performed between the differential variant population and the parent individuals to generate offspring individuals;

[0110]

[0111] in, This represents the crossover probability in the range [0,1]. It is a random function.

[0112] S105-5: Selection Operation: A greedy algorithm is used for the selection operation. Based on the comparison of the individual comprehensive greenness values, individuals with better performance are selected to enter the next iteration process.

[0113] S105-6: Through the above operations, the population evolves to the next generation. Steps S105-2 to S105-4 are repeated until the iteration termination condition is met.

[0114] S105-7: Output the optimal scheduling optimization results to generate the optimal solution.

[0115] By combining the above methods, we can efficiently obtain the optimal scheduling scheme for the overall greenness value of the mixed flow workshop and guide workshop production.

[0116] This invention proposes an integrated method for evaluating the overall greenness of equipment based on an improved TOPSIS and grey relational analysis. Grey relational analysis is integrated into the TOPSIS framework, using grey relational degree to quantify the closeness between the overall greenness of the equipment and the optimal and worst solutions. The grey relational degree obtained from grey relational analysis is multiplied by the relative closeness obtained from TOPSIS using a product method to obtain a comprehensive evaluation value for the overall greenness of the equipment, achieving a comprehensive and reasonable assessment.

[0117] Example 2

[0118] This embodiment provides a hybrid flow workshop scheduling optimization system based on equipment greenness ranking, including:

[0119] The system construction module is configured to: construct a comprehensive greenness evaluation index system for the mixed flow workshop to be scheduled;

[0120] The matrix module is configured to: construct a comprehensive greenness evaluation matrix for each device based on the comprehensive greenness evaluation index system;

[0121] The evaluation degree determination module is configured to: calculate the relative progress and gray relational degree based on the comprehensive greenness evaluation matrix of each device; and determine the comprehensive greenness evaluation value of each device based on the relative progress and gray relational degree.

[0122] The initial population screening module is configured to sort the devices in descending order of their comprehensive greenness assessment values ​​and select the initial population from the sorted device set according to a set selection strategy.

[0123] The output module is configured to take the initial population as the input value of the differential evolution algorithm, execute the differential evolution algorithm to obtain the optimal solution, and use the optimal solution as the result of the hybrid flow shop scheduling optimization.

[0124] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the scheduling of hybrid flow workshops based on equipment greenness ranking, characterized by: include: For workshops with mixed production lines, a comprehensive greenness evaluation index system should be constructed. Based on the comprehensive greenness evaluation index system, a comprehensive greenness evaluation matrix for each device is constructed. Based on the comprehensive greenness evaluation matrix of each device, the relative progress and grey relational degree are calculated respectively; based on the relative progress and grey relational degree, the comprehensive greenness evaluation value of each device is determined. The devices are sorted in descending order of their comprehensive greenness assessment value, and an initial population is selected from the sorted device set according to the set selection strategy. The initial population is used as the input value of the differential evolution algorithm. After executing the differential evolution algorithm, the optimal solution is obtained, and the optimal solution is used as the scheduling optimization result of the hybrid flow shop. The comprehensive greenness evaluation index system includes: Primary indicator: Overall greenness of equipment; The primary indicators include four secondary indicators, which are: carbon emissions from processing, processing time, processing cost, and basic equipment performance. The carbon emissions from the processing include three tertiary indicators: resource consumption, energy consumption, and direct carbon emissions. Processing carbon emissions Greenhouse gas emissions refer to the amount of energy, resource consumption, and chemical reactions that occur during the process of a workpiece moving from a blank to the completion of a specified process; processing carbon emissions. This mainly refers to carbon dioxide emissions; ； in, These are auxiliary materials consumed in the manufacturing process. The carbon emission intensity coefficient of energy consumption for auxiliary materials. Energy consumption in the manufacturing process. Energy carbon emission intensity coefficient, Direct carbon emissions generated by manufacturing processes; Based on the aforementioned comprehensive greenness evaluation index system, a comprehensive greenness evaluation matrix for each piece of equipment is constructed, including: identifying each parallel piece of equipment in the mixed flow workshop and its evaluation index, and constructing a comprehensive greenness evaluation matrix for each piece of equipment. As shown in formula (1); , Formula (1); in, Indicates the number of parallel devices. m Indicates the number of indicators in the evaluation. Indicates the first The first of the equipment The four indicators are: the first is the carbon emission rate during processing, the second is the processing time, the third is the processing cost, and the fourth is the basic performance of the equipment. Indicates the first The carbon emission index value of the parallel processing equipment. Indicates the first Processing time of multiple parallel machines; Indicates the first The processing cost of parallel equipment Indicates the first Basic performance characteristics of parallel devices; The obtained comprehensive greenness evaluation matrix is ​​standardized to eliminate the influence of different dimensions. The standardization process is shown in formula (2), and the obtained standardized matrix is ​​shown in formula (3). , Formula (2); , Formula (3); in, Indicates the first The first of the equipment The standardized value of each indicator; Based on the comprehensive greenness evaluation matrix of each device, the relative alignment progress and grey relational degree are calculated respectively; the calculation process of the relative alignment progress includes: Based on the normalized matrix Calculate the first The first indicator The proportion of features of each evaluation object , , Formula (4); And based on the characteristic proportion , obtain the Entropy value of each indicator : , Formula (5); Calculate the coefficient of difference Then the first Weight of each indicator As shown in equations (4), (5), and (6) respectively: , Formula (6); Based on the normalized matrix Obtain the ideal solution from the dataset. Negative ideal solution ； Calculate the distance between the evaluated index and the positive and negative ideal solutions: , Formula (7); , Formula (8); Obtain the The score of each evaluation object , As shown in equation (9): , Formula (9); in, , The larger the value, the closer it is to the optimal value; Based on the comprehensive greenness evaluation matrix of each device, the relative alignment and grey relational degree are calculated respectively. The calculation process of the grey relational degree includes: The default parent sequence is obtained based on formula (3). ,in, Find the maximum value of each column in equation (3); construct the difference matrix. As shown in formula (10); , Formula (10); Calculate the grey relational coefficient of the index As shown in formula (11); , Formula (11); in, The resolution coefficient, Represents the difference matrix The minimum value among all values. Represents the difference matrix The maximum value among all values ​​is used to obtain the grey relational degree, and the evaluation result is... For example, in formula (12): , Formula (12); The process of determining the comprehensive greenness assessment value for each device based on relative proximity and gray relational degree includes: using a product method to combine gray relational degree and relative proximity to obtain a comprehensive evaluation value. As shown in equation (13), the overall greenness of parallel devices is then ranked, and the ranking number is... ; , Formula (13); The devices are sorted in descending order of their overall greenness assessment value. Following a pre-defined selection strategy, an initial population is selected from the sorted device set. The probability of a device being selected is as follows: , Formula (14); in, Indicates the first The probability of a device being selected; It is an adjustable parameter used to control the steepness of the selection probability distribution; The larger the value, the greater the difference in selection probability among the top-ranked devices. The smaller the value, the smaller the difference in selection probability among the top-ranked devices; Indicates the first The overall greenness value of each device; and These represent the minimum and maximum values ​​of overall greenness among all devices, respectively.

2. The method for optimizing the scheduling of a hybrid production line based on equipment greenness ranking as described in claim 1, characterized in that, The processing time includes four tertiary indicators: clamping time, disassembly time, cutting time, and tool change time; The processing cost includes three levels of indicators: material cost, labor cost, electricity consumption cost, equipment depreciation cost, and maintenance and repair cost. The basic performance of the equipment includes three levels of indicators: processing accuracy, stability, and degree of automation.

3. The method for optimizing the scheduling of a hybrid production line based on equipment greenness ranking as described in claim 2, characterized in that, Processing time It refers to the total time consumed in the product manufacturing or processing process, from processing raw materials or semi-finished products to completing the specified processing technology requirements or processing tasks, including direct processing time and indirect processing time; ； Direct processing time refers to the time required for raw materials to be directly processed on the equipment, i.e., the cutting time of the parts. Indirect machining time refers to the time related to direct machining but not directly involved in the machining process, including clamping time. Tool change time Disassembly time ; Processing costs This refers to the total cost incurred during the manufacturing process of electromechanical product parts: ； in, Indicates material cost, Indicates labor costs, This indicates equipment depreciation costs. Indicates the cost of electricity consumption. Indicates maintenance and repair costs; Basic equipment performance refers to the core capabilities and characteristics exhibited by the equipment in fulfilling its designed functions. This basic equipment performance is measured by machining accuracy. ,stability and degree of automation To characterize; The three basic performance indicators of the equipment are mostly qualitative. Triangular fuzzy numbers are used to quantify these qualitative indicators by constructing membership functions for the fuzzy numbers. This transforms qualitative indicators into quantitative indicators with fuzziness. The method of using triangular fuzzy numbers to convert qualitative indicators into quantitative ones includes: (1) For each indicator, construct a triangular fuzzy number; the triangular fuzzy number Represented as ,in, As the lower limit, Centered on, The upper limit; (2) Determine the membership function: For triangular fuzzy numbers Its membership function Represented as a piecewise function: when hour, ; when hour, ; when hour, ; (3) Calculate the quantitative value: based on the membership function and specific Value, calculate quantitative value .

4. The method for optimizing the scheduling of a hybrid production line based on equipment greenness ranking as described in claim 1, characterized in that, The initial population is used as input to the differential evolution algorithm. After executing the differential evolution algorithm, the optimal solution is obtained. The optimal solution is used as the result of the hybrid flow shop scheduling optimization, including: (1): Initialize the population; (2): Fitness function: Based on the constructed comprehensive greenness evaluation system, the fitness function is optimized by minimizing the comprehensive greenness. Objective function: ； Constraints: ； in, Indicates the total carbon emission weight of the workshop. This indicates the weight of the total completion time of the workshop. Indicates the weight of total workshop cost. Indicates the overall performance weight of the workshop; This indicates the total carbon emissions from workshop production; Indicates the total completion time of production in the workshop; This represents the total production cost in the workshop. Indicates the overall production performance of the workshop; Indicates the total carbon emission constraints of the workshop; To constrain the delivery time of workshop production; Constraints on total production costs in the workshop; Constraints on overall production performance in the workshop; This represents a normalization function, the goal of which is to solve the problem of indicators with different dimensions. This indicates the maximum value of the indicator. This indicates the minimum value of the indicator; (3): Variation: When performing variation operations on equipment in a mixed flow workshop, based on the comprehensive greenness ranking results of each parallel equipment, select the equipment that was ranked first. Bit-sized devices as mutation candidates And assign a preference factor to each device according to the ranking. ,in, The value increases as the device is ranked higher; for the device variable to be mutated... ,from Select a device variable As the target of mutation; calculate the mutation vector. ; ； ； in, Ranking based on the overall greenness of parallel devices The results are dynamically adjusted, with those ranked higher being sorted. , The larger the value, the closer the mutated value is to the original value. The later the sorting... , The smaller the value; Indicates the total number of parallel devices; (4): Crossover: Crossover is performed between the differentially mutated population and the parent individuals to generate offspring individuals; ； in, This represents the crossover probability in the range [0,1]. It is a random function; (5): Selection operation: A greedy algorithm is used for selection operation. Based on the comparison results of the individual comprehensive greenness values, individuals with better performance are selected to enter the next generation of iteration process; (6): Through the above operations, the population evolves to the next generation, and steps (2) to (4) are repeated until the iteration termination condition is met; (7): Output the optimal scheduling optimization result to generate the optimal solution.

5. A hybrid flow workshop scheduling optimization system based on equipment greenness ranking, characterized in that: include: The system construction module is configured to: construct a comprehensive greenness evaluation index system for the mixed flow workshop to be scheduled; The matrix module is configured to: construct a comprehensive greenness evaluation matrix for each device based on the comprehensive greenness evaluation index system; The evaluation degree determination module is configured to: calculate the relative progress and gray relational degree based on the comprehensive greenness evaluation matrix of each device; and determine the comprehensive greenness evaluation value of each device based on the relative progress and gray relational degree. The initial population screening module is configured to sort the devices in descending order of their comprehensive greenness assessment values ​​and select the initial population from the sorted device set according to a set selection strategy. The output module is configured to take the initial population as the input value of the differential evolution algorithm, execute the differential evolution algorithm to obtain the optimal solution, and use the optimal solution as the result of the hybrid flow shop scheduling optimization. The comprehensive greenness evaluation index system includes: Primary indicator: Overall greenness of equipment; The primary indicators include four secondary indicators, which are: carbon emissions from processing, processing time, processing cost, and basic equipment performance. The carbon emissions from the processing include three tertiary indicators: resource consumption, energy consumption, and direct carbon emissions. Processing carbon emissions Greenhouse gas emissions refer to the amount of energy, resource consumption, and chemical reactions that occur during the process of a workpiece moving from a blank to the completion of a specified process; processing carbon emissions. This mainly refers to carbon dioxide emissions; ； in, These are auxiliary materials consumed in the manufacturing process. The carbon emission intensity coefficient of energy consumption for auxiliary materials. Energy consumption in the manufacturing process. Energy carbon emission intensity coefficient, Direct carbon emissions generated by manufacturing processes; Based on the aforementioned comprehensive greenness evaluation index system, a comprehensive greenness evaluation matrix for each piece of equipment is constructed, including: identifying each parallel piece of equipment in the mixed flow workshop and its evaluation index, and constructing a comprehensive greenness evaluation matrix for each piece of equipment. As shown in formula (1); , Formula (1); in, Indicates the number of parallel devices. m Indicates the number of indicators in the evaluation. Indicates the first The first of the equipment The four indicators are: the first is the carbon emission rate during processing, the second is the processing time, the third is the processing cost, and the fourth is the basic performance of the equipment. Indicates the first The carbon emission index value of the parallel processing equipment. Indicates the first Processing time of multiple parallel machines; Indicates the first The processing cost of parallel equipment Indicates the first Basic performance characteristics of parallel devices; The obtained comprehensive greenness evaluation matrix is ​​standardized to eliminate the influence of different dimensions. The standardization process is shown in formula (2), and the obtained standardized matrix is ​​shown in formula (3). , Formula (2); , Formula (3); in, Indicates the first The first of the equipment The standardized value of each indicator; Based on the comprehensive greenness evaluation matrix of each device, the relative alignment progress and grey relational degree are calculated respectively; the calculation process of the relative alignment progress includes: Based on the normalized matrix Calculate the first The first indicator The proportion of features of each evaluation object , , Formula (4); And based on the characteristic proportion , obtain the Entropy value of each indicator : , Formula (5); Calculate the coefficient of difference Then the first Weight of each indicator As shown in equations (4), (5), and (6) respectively: , Formula (6); Based on the normalized matrix Obtain the ideal solution from the dataset. Negative ideal solution ； Calculate the distance between the evaluated index and the positive and negative ideal solutions: , Formula (7); , Formula (8); Obtain the The score of each evaluation object , As shown in equation (9): , Formula (9); in, , The larger the value, the closer it is to the optimal value; Based on the comprehensive greenness evaluation matrix of each device, the relative alignment and grey relational degree are calculated respectively. The calculation process of the grey relational degree includes: The default parent sequence is obtained based on formula (3). ,in, Find the maximum value of each column in equation (3); construct the difference matrix. As shown in formula (10); , Formula (10); Calculate the grey relational coefficient of the index As shown in formula (11); , Formula (11); in, The resolution coefficient, Represents the difference matrix The minimum value among all values. Represents the difference matrix The maximum value among all values ​​is used to obtain the grey relational degree, and the evaluation result is... For example, in formula (12): , Formula (12); The process of determining the comprehensive greenness assessment value for each device based on relative proximity and gray relational degree includes: using a product method to combine gray relational degree and relative proximity to obtain a comprehensive evaluation value. As shown in equation (13), the overall greenness of parallel devices is then ranked, and the ranking number is... ; , Formula (13); The devices are sorted in descending order of their overall greenness assessment value. Following a pre-defined selection strategy, an initial population is selected from the sorted device set. The probability of a device being selected is as follows: , Formula (14); in, Indicates the first The probability of a device being selected; It is an adjustable parameter used to control the steepness of the selection probability distribution; The larger the value, the greater the difference in selection probability among the top-ranked devices. The smaller the value, the smaller the difference in selection probability among the top-ranked devices; Indicates the first The overall greenness value of each device; and These represent the minimum and maximum values ​​of overall greenness among all devices, respectively.

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