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

Abstract

The invention relates to the technical field of hybrid flow shop scheduling optimization, in particular to a hybrid flow shop scheduling optimization method and system based on equipment green degree sequencing, and the method comprises the steps: constructing a comprehensive green degree evaluation matrix of each equipment based on a comprehensive green degree evaluation index system; according to the comprehensive green degree evaluation matrix of each device, respectively calculating a relative pasting degree and a grey correlation degree; according to the relative pasting progress and the grey correlation degree, determining a comprehensive green degree evaluation value of each device; sorting according to a descending order of the comprehensive green degree evaluation values of the devices, and screening out an initial population from a sorted device set according to a set selection strategy; and taking the initial population as an input value of a differential evolution algorithm, executing the differential evolution algorithm to obtain an optimal solution, and taking the optimal solution as a hybrid flow shop scheduling optimization result. According to the method, the hybrid flow shop scheduling scheme is efficiently and reasonably generated.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid flow shop scheduling optimization, and in particular to a hybrid flow shop scheduling optimization method and system based on equipment green degree sorting. Background Art

[0002] The production of a mixed flow workshop includes multiple production stages, and there are multiple parallel processing equipment in each stage. The choice of equipment directly affects production efficiency, product quality, production cost and other aspects, and has an important impact on the development of the enterprise. At present, enterprises mostly rely on workers' experience or the idleness of equipment at the time of production to make choices, which makes it difficult to ensure the rationality of production. The production of this workshop is affected and restricted by many aspects such as environmental emissions, delivery deadlines, and production costs. It is necessary to integrate multiple factors and indicators to guide the workshop to generate a reasonable and optimized production plan. The existing scheduling optimization of mixed flow workshops performs scheduling optimization by constructing a target quantitative model of the workshop production process, ignoring the characteristics of parallel equipment. The selection of equipment is mostly passive, and the optimization efficiency needs to be improved urgently. Summary of the invention

[0003] In order to address the deficiencies of the prior art, the present invention provides a hybrid flow shop scheduling optimization method and system based on equipment green degree ranking; the present invention transforms passive scheduling of equipment into active scheduling, proposes a hybrid flow shop scheduling optimization method based on equipment comprehensive green degree ranking, evaluates and ranks the comprehensive green degrees of parallel equipment, and generates a hybrid flow shop scheduling plan efficiently and reasonably through the directional operation of the differential evolution operator.

[0004] On the one hand, a hybrid flow shop scheduling optimization method based on equipment greenness ranking is provided, including: For the scheduling of mixed flow workshops, a comprehensive green evaluation index system is constructed; Based on the comprehensive green evaluation index system, a comprehensive green evaluation matrix for each device is constructed; According to the comprehensive green evaluation matrix of each device, the relative progress and grey correlation are calculated respectively; according to the relative progress and grey correlation, the comprehensive green evaluation value of each device is determined; Sort the devices in descending order according to their comprehensive green evaluation values, and select the initial population 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 hybrid flow shop scheduling optimization result.

[0005] On the other hand, a hybrid flow shop scheduling optimization system based on equipment greenness ranking is provided, including: The system building module is configured to: construct a comprehensive green evaluation index system for scheduling mixed flow workshops; A matrix module is configured to: construct a comprehensive green evaluation matrix for each device based on the comprehensive green evaluation index system; An evaluation degree determination module is configured to: calculate the relative progress and the grey correlation degree respectively according to the comprehensive green evaluation matrix of each device; determine the comprehensive green evaluation value of each device according to the relative progress and the grey correlation degree; An initial population screening module is configured to: sort the devices in descending order according to their comprehensive green evaluation values, and screen out an initial population from the sorted device set according to a set selection strategy; The output module is configured to: use the initial population as the input value of the differential evolution algorithm, execute the differential evolution algorithm, obtain the optimal solution, and use the optimal solution as the hybrid flow shop scheduling optimization result.

[0006] The above technical solution has the following advantages or beneficial effects: Based on the production characteristics of hybrid flow workshops, the paper focuses on the problem of parallel equipment selection, considers the carbon emissions, efficiency, cost and basic performance indicators of equipment processing, and constructs a multi-attribute evaluation system for the comprehensive greenness of equipment, comprehensively evaluates the equipment processing situation, and realizes the comprehensive greenness ranking of equipment. Based on the comprehensive greenness ranking of equipment, the differential evolution solution process is improved, and the iterative variables are actively selected through the directional operation of the initial population and mutation operators, so as to generate scheduling optimization solutions efficiently and reasonably. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0008] Figure 1 This is a flow chart of the method of embodiment 1. DETAILED DESCRIPTION

[0009] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0010] The production of mixed flow workshop is a comprehensive and complex process. It is necessary to consider the economic benefits of enterprise development and ensure that the performance of products can meet the diverse needs of customers. In recent years, with the establishment of carbon emission trading market and the introduction of relevant policies, the requirements and restrictions on carbon emissions have become more stringent, and it is also urgent to incorporate carbon emissions in the production process into the overall consideration. Formulating workshop production plans based on various factors such as environment and economy can ensure the steady progress of production and improve the competitiveness and development potential of enterprises.

[0011] In a hybrid flow shop, the selection of parallel equipment plays a vital role. The hybrid flow shop is characterized by being able to process multiple different products or parts at the same time, and these products or parts flow in a certain order and rhythm on the production line. In such a production environment, the selection of parallel equipment directly affects production efficiency, production cost and other aspects. It is necessary to combine multiple factors and indicators to guide the workshop to generate a reasonable and optimized production plan.

[0012] The existing scheduling methods of mixed flow workshops are mainly based on basic indicators such as production efficiency, lacking a comprehensive multi-attribute consideration mechanism and evaluation system, resulting in a one-sided and irrational scheduling plan. In addition, enterprises initially relied on workers' experience or the idleness of equipment at the time of production to select equipment and determine plans. This method is difficult to ensure the rationality of production.

[0013] With the development of intelligent algorithms, when solving the mixed flow workshop problem, the actual constraints of the industry (such as delivery time constraints, etc.) are usually combined, or a multi-objective quantitative model of the production process is directly constructed to obtain the workshop production scheduling plan through iterative optimization of the algorithm. This scheduling optimization process ignores the characteristics of parallel equipment, and the selection of equipment is mostly passive, and the solution efficiency needs to be improved urgently.

[0014] In contrast, the present invention constructs a comprehensive green index system for equipment, proposes a hybrid flow shop scheduling optimization method based on equipment green ranking, transforms the passive scheduling of equipment into active scheduling, and improves the convergence speed of the algorithm based on the ranking of the comprehensive greenness of equipment and the directional operation of the differential evolution operator, so as to efficiently and reasonably generate a hybrid flow shop scheduling plan.

[0015] Embodiment 1, as Figure 1 As shown, this embodiment provides a hybrid flow shop scheduling optimization method based on equipment greenness sorting, including: S101: For the scheduling of mixed flow workshops, a comprehensive green evaluation index system is established; S102: constructing a comprehensive green evaluation matrix for each device based on the comprehensive green evaluation index system; S103: Calculate the relative progress and the grey correlation degree according to the comprehensive green evaluation matrix of each device; determine the comprehensive green evaluation value of each device according to the relative progress and the grey correlation degree; S104: sorting the devices in descending order according to their comprehensive green evaluation values, and selecting an initial population from the sorted device set according to a set selection strategy; S105: Using the initial population as an input value of the differential evolution algorithm, executing the differential evolution algorithm, obtaining an optimal solution, and using the optimal solution as a hybrid flow shop scheduling optimization result.

[0016] Furthermore, the S101: constructing a comprehensive green evaluation index system for the mixed flow workshop to be scheduled; the comprehensive green evaluation index system includes: First-level indicator: comprehensive greenness of equipment; The first-level indicators include: four second-level indicators, the four second-level indicators include: processing carbon emissions, processing time, processing costs and basic equipment performance; The processing carbon emissions include three three-level indicators: resource consumption, energy consumption and direct carbon emissions; The processing time includes four three-level 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.

[0017] Furthermore, the processing carbon emissions , refers to the greenhouse gas emissions generated by factors such as energy consumption, resource consumption, and chemical reactions during the process of a workpiece from a blank to the completion of a specified process. Mainly refers to the emission of carbon dioxide.

[0018] ; in, It is the auxiliary material consumption of the manufacturing process. is the carbon emission intensity coefficient of auxiliary material energy consumption, Energy consumption of the manufacturing process. is the energy carbon emission intensity coefficient, Direct carbon emissions from the manufacturing process.

[0019] Furthermore, the processing time It refers to the total time consumed in processing raw materials or semi-finished products until the specified processing technology requirements or processing tasks are completed during the product manufacturing or processing process, including direct processing time and indirect processing time.

[0020] ; Direct processing time refers to the time required for raw materials to be directly processed on the equipment, that is, the part cutting time Indirect processing time refers to the time related to direct processing but not directly processed, including clamping time , Tool change time , Disassembly time wait.

[0021] Furthermore, the processing cost , refers to the sum of various expenses incurred in the process of manufacturing electromechanical product parts: ; in, represents the material cost, Represents labor cost, represents the equipment depreciation expense, represents the electricity consumption cost, Represents maintenance and repair costs.

[0022] Furthermore, the basic performance of the equipment refers to the core capabilities and characteristics of the equipment when completing its designed functions. These capabilities and characteristics directly determine the processing quality and adaptability of the equipment. The basic performance of the equipment is determined by the processing accuracy. ,stability and degree of automation To characterize.

[0023] The three indicators of basic performance of equipment are mostly qualitative indicators. Triangular fuzzy numbers are used to convert qualitative indicators into quantitative indicators. By constructing the membership function of fuzzy numbers , transforming qualitative indicators into fuzzy quantitative indicators.

[0024] Furthermore, the use of triangular fuzzy numbers to convert qualitative indicators into quantitative ones includes: (1) For each indicator, a triangular fuzzy number is constructed; triangular fuzzy number Expressed as ,in, is the lower limit, is the center (most likely value), is the upper limit; (2) Determine the membership function: For triangular fuzzy numbers , its membership function Expressed as a piecewise function: when hour, ; when hour, ; when hour, ; (3) Calculate quantitative value: According to the membership function and specific Value (i.e. actual indicator value), calculate the quantitative value For example, the triangular fuzzy number corresponding to the basic performance index of a certain device is , the actual indicator value ,because, , then the degree of membership ,but .

[0025] It should be understood that when confirming the scheduling plan, the hybrid assembly line workshop needs to select reasonable processing equipment for the workpiece from multiple parallel devices, and needs to comprehensively consider the environmental impact, consumed cost, processing efficiency and basic performance of the equipment when the workpiece is processed on the equipment.

[0026] Furthermore, the S102: constructing a comprehensive green evaluation matrix for each device based on the comprehensive green evaluation index system includes: Confirm the parallel equipment and evaluation indicators of the mixed flow workshop, and build a comprehensive green evaluation matrix for each equipment , as shown in formula (1).

[0027] , formula (1); in, Indicates the number of parallel devices, m Indicates the number of indicators to be evaluated. m =4; Indicates The first device The first indicator is the processing carbon emission index, the second indicator is the processing time, the third indicator is the processing cost, and the fourth indicator is the basic performance of the equipment; Indicates The carbon emission index value of the processing of parallel equipment, Indicates Processing time of parallel equipment; Indicates The processing cost of a parallel device, Indicates Basic device performance of 2 parallel devices.

[0028] The obtained comprehensive green 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).

[0029] , formula (2); , formula (3); in, Indicates The first device The standardized value of an indicator.

[0030] Furthermore, the S103: calculates the relative progress and the grey correlation degree respectively according to the comprehensive green evaluation matrix of each device; wherein the calculation process of the relative progress includes: S103-a1: Based on the standardized matrix , calculate the Under the indicator The characteristic proportion of the evaluation object , , formula (4); And according to the characteristic weight , get the The entropy value of the index : , formula (5); Further calculation of the coefficient of variation , then The weight of the indicator , as shown in formula (4), formula (5), and formula (6) respectively.

[0031] , formula (6); S103-a2: Based on the standardized matrix , get the Positive ideal solution ; Negative ideal solution ; Calculate the distance between the indicator to be evaluated and the positive ideal solution and the negative ideal solution: , formula (7); , formula (8); S103-a3: Get the The score of the evaluation object , , as shown in formula (9), where , The larger it is, the closer it is to the optimal value; , formula (9); Furthermore, the S103: calculates the relative progress and the grey correlation degree respectively according to the comprehensive green evaluation matrix of each device, wherein the grey correlation degree calculation process includes: Based on formula (3), the default parent sequence is obtained ,in, is the maximum value of each column in formula (3); construct the difference matrix , as shown in formula (10); , formula (10); Calculation index grey correlation coefficient , as shown in formula (11).

[0032] , formula (11); in, is the resolution coefficient. represents the minimum value of all values ​​in the difference matrix K, Represents the maximum value of all values ​​in the difference matrix K.

[0033] Further, the grey correlation degree is obtained, that is, the evaluation result is , as shown in formula (12): , formula (12).

[0034] Furthermore, the comprehensive green evaluation value of each device is determined according to the relative progress and the grey correlation, including: Based on the product method, the grey relational degree and relative closeness are combined to obtain the comprehensive evaluation value. , as shown in formula (13), the comprehensive greenness of the parallel devices is sorted, and the sorting sequence is .

[0035] , formula (13).

[0036] Furthermore, the S104: sorting the devices in descending order according to their comprehensive green evaluation values, and selecting an initial population from the sorted device set according to a set selection strategy; Differential evolution often generates the initial population randomly. The randomly generated population may have problems such as insufficient representation, easy to fall into local optimality, slow convergence speed, etc. The present invention proposes a directional population initialization method, which selects the initial population through the logistic stick function according to the comprehensive green degree ranking of each parallel device. The devices with higher ranking have a higher selection probability, while the devices with lower ranking have a lower selection probability. The probability of a device being selected is as follows: , formula (14).

[0037] in, Indicates The probability of a device being selected; is an adjustable parameter that controls the steepness of the selection probability distribution. The larger the value, the greater the difference in selection probability between the top ranked devices. The smaller the value, the smaller the difference in selection probability between the top ranked devices; Indicates The comprehensive green value of each device; and Respectively represent the minimum and maximum values ​​of the comprehensive greenness of all devices.

[0038] Furthermore, the S105: using the initial population as an input value of the differential evolution algorithm, executing the differential evolution algorithm, obtaining an optimal solution, and using the optimal solution as a hybrid flow shop scheduling optimization result, includes: S105-1: Initialize the population; S105-2: Fitness function: Based on the constructed comprehensive green evaluation system, the fitness function with the minimum comprehensive green degree is optimized and solved; Objective function: ; Constraints: ; in, represents the total carbon emission weight of the workshop, represents the total completion time weight of the workshop, represents the total cost weight of the workshop, represents the total performance weight of the workshop; represents the total carbon emissions of workshop production; Indicates the total completion time of workshop production; represents the total production cost of the workshop; Indicates the overall production performance of the workshop; represents the total carbon emission constraint of the workshop; Provide production lead time constraints for the workshop; is the total production cost constraint of the workshop; is the total performance constraint of workshop production; It represents the normalization function, and its goal is to solve the problem of different dimensions of indicators; Indicates the maximum value of the indicator. Indicates the minimum value of the indicator. The value is related to the workshop production plan and is generally provided by the production workshop. S105-3: Mutation: When performing the mutation operation on the equipment in the mixed flow workshop, select the equipment before sorting according to the comprehensive green degree sorting results of each parallel equipment. Devices with bits as mutation candidates , and assign a propensity factor to each device based on the ranking ,in, The value of increases as the device is ranked higher (that is, the higher the device is ranked, the greater the value). For the device variable to be mutated ,from Select a device variable in As the mutation target. Calculate the mutation vector ; ; ; in, Sort by the overall greenness of the parallel devices The results are adjusted dynamically. , The larger the value, the closer the mutated value is to ; The later the order , The smaller the value; Indicates the total number of parallel devices.

[0039] S105-4: Crossover: Perform a crossover operation between the differential mutation population and the parent individuals to generate offspring individuals;

[0040] in, represents the crossover probability in the range [0,1]; is a random function.

[0041] S105-5: Selection operation: A greedy algorithm is used to perform the selection operation. According to the comparison results of the comprehensive greenness values ​​of the individuals, the individuals with better performance are selected to enter the next generation of iteration process; S105-6: Through the above operations, the population evolves to the next generation, and steps S105-2 to S105-4 are re-executed until the iteration termination condition is met; S105-7: Output the optimal scheduling optimization result to generate the optimal solution.

[0042] Combining the above methods, we can efficiently obtain the scheduling plan with the optimal comprehensive green value of the mixed flow workshop to guide the workshop production.

[0043] The present invention proposes an integrated method based on improved TOPSIS and grey correlation analysis to evaluate the comprehensive greenness of equipment. Grey correlation analysis is integrated into the TOPSIS framework, and the grey correlation is used to quantify the closeness between the comprehensive greenness of the equipment and the optimal and worst solutions. The grey correlation of the grey correlation analysis is multiplied by the relative closeness obtained by TOPSIS through the product method to obtain the comprehensive evaluation value of the comprehensive greenness of the equipment, thus achieving a comprehensive and reasonable evaluation.

[0044] Embodiment 2 This embodiment provides a hybrid flow shop scheduling optimization system based on equipment greenness ranking, including: The system building module is configured to: construct a comprehensive green evaluation index system for scheduling mixed flow workshops; A matrix module is configured to: construct a comprehensive green evaluation matrix for each device based on the comprehensive green evaluation index system; An evaluation degree determination module is configured to: calculate the relative progress and the grey correlation degree respectively according to the comprehensive green evaluation matrix of each device; determine the comprehensive green evaluation value of each device according to the relative progress and the grey correlation degree; An initial population screening module is configured to: sort the devices in descending order according to their comprehensive green evaluation values, and screen out an initial population from the sorted device set according to a set selection strategy; The output module is configured to: use the initial population as the input value of the differential evolution algorithm, execute the differential evolution algorithm, obtain the optimal solution, and use the optimal solution as the hybrid flow shop scheduling optimization result.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A hybrid flow shop scheduling optimization method based on equipment greenness ranking is characterized by: include: For the scheduling of mixed flow workshops, a comprehensive green evaluation index system is constructed; Based on the comprehensive green evaluation index system, a comprehensive green evaluation matrix for each device is constructed; According to the comprehensive green evaluation matrix of each device, the relative progress and grey correlation are calculated respectively; according to the relative progress and grey correlation, the comprehensive green evaluation value of each device is determined; Sort the devices in descending order according to their comprehensive green evaluation values, and select the initial population 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 hybrid flow shop scheduling optimization result.

2. The hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 1 is characterized in that: For the scheduling of mixed flow workshops, a comprehensive green evaluation index system is constructed; The comprehensive green evaluation index system includes: First-level indicator: comprehensive greenness of equipment; The first-level indicators include: four second-level indicators, the four second-level indicators include: processing carbon emissions, processing time, processing costs and basic equipment performance; The processing carbon emissions include three three-level indicators: resource consumption, energy consumption and direct carbon emissions; The processing time includes four three-level 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 hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 2 is characterized in that: Processing Carbon Emissions , refers to the greenhouse gas emissions generated by energy consumption, resource consumption and chemical reaction factors in the process of workpiece from blank to completion of specified process; processing carbon emissions Mainly refers to the emission of carbon dioxide; ； in, It is the auxiliary material consumption of the manufacturing process. is the carbon emission intensity coefficient of auxiliary material energy consumption, Energy consumption of the manufacturing process. is the energy carbon emission intensity coefficient, Direct carbon emissions from the manufacturing process; Processing time , refers to the total time consumed in the process of product manufacturing or processing, from processing raw materials or semi-finished products to completing the specified processing 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, that is, the part cutting time Indirect processing time refers to the time related to direct processing but not directly processed, including clamping time , Tool change time , Disassembly time ; Processing cost , refers to the sum of various expenses incurred in the process of manufacturing electromechanical product parts: ； in, represents the material cost, Represents labor cost, represents the equipment depreciation expense, represents the electricity consumption cost, represents maintenance and repair costs; The basic performance of the equipment refers to the core capabilities and characteristics that the equipment exhibits when completing its designed functions. The basic performance of the equipment is determined by the processing accuracy. ,stability and degree of automation To characterize; The three indicators of basic performance of equipment are mostly qualitative indicators. Triangular fuzzy numbers are used to convert qualitative indicators into quantitative indicators. By constructing the membership function of fuzzy numbers , transforming qualitative indicators into quantitative indicators with fuzziness; The method of converting qualitative indicators into quantitative indicators by using triangular fuzzy numbers includes: (1) For each indicator, a triangular fuzzy number is constructed; triangular fuzzy number Expressed as ,in, is the lower limit, As the center, is the upper limit; (2) Determine the membership function: For triangular fuzzy numbers , its membership function Expressed as a piecewise function: when hour, ; when hour, ; when hour, ; (3) Calculate quantitative value: According to the membership function and specific Value, calculate quantitative value .

4. The hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 1 is characterized in that: Based on the comprehensive green evaluation index system, a comprehensive green evaluation matrix for each device is constructed, including: confirming each parallel device in the mixed flow workshop and the evaluation index, and constructing a comprehensive green evaluation matrix for each device , as shown in formula (1); , Formula (1); in, Indicates the number of parallel devices, m Indicates the number of evaluation indicators, Indicates The first device The first indicator is the processing carbon emission index, the second indicator is the processing time, the third indicator is the processing cost, and the fourth indicator is the basic performance of the equipment; Indicates The carbon emission index value of the processing of parallel equipment, Indicates Processing time of parallel equipment; Indicates The processing cost of a parallel device, Indicates Basic performance of equipment with 100 parallel devices; The obtained comprehensive green 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 device The standardized value of an indicator.

5. The hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 4 is characterized in that: According to the comprehensive green evaluation matrix of each device, the relative progress and grey correlation degree are calculated respectively; the calculation process of the relative progress includes: Based on the standardized matrix , calculate the Under the indicator The characteristic proportion of the evaluation object , , Formula (4); And according to the characteristic weight , get the The entropy value of the index : , Formula (5); Calculate the coefficient of variation , then The weight of the indicator , as shown in formula (4), formula (5), and formula (6) respectively: , Formula (6); Based on the standardized matrix , get the positive ideal solution in the data set ; Negative ideal solution ; Calculate the distance between the indicator to be evaluated and the positive ideal solution and the negative ideal solution: , Formula (7); , Formula (8); Get the The score of the evaluation object , , as shown in formula (9): , Formula (9); in, , The larger it is, the closer it is to the optimal value.

6. The hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 5 is characterized in that: According to the comprehensive green evaluation matrix of each device, the relative progress and grey correlation degree are calculated respectively. The calculation process of grey correlation degree includes: Based on formula (3), the default parent sequence is obtained ,in, is the maximum value of each column in formula (3); construct the difference matrix , as shown in formula (10); , Formula (10); Calculation index grey correlation coefficient , as shown in formula (11); , Formula (11); in, is the resolution coefficient, Represents the difference matrix The minimum value of all values ​​in , Represents the difference matrix The maximum value of all values ​​in .

7. The hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 6 is characterized in that: The grey correlation degree is obtained, and the evaluation result is , as shown in formula (12): , Formula (12); The method of determining the comprehensive green evaluation value of each device according to the relative closeness and the grey correlation degree includes: based on the product method, the grey correlation degree and the relative closeness are combined to obtain a comprehensive evaluation value. , as shown in formula (13), the comprehensive greenness of the parallel devices is sorted, and the sorting sequence is ; , formula (13).

8. The hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 7 is characterized in that: Sort the devices in descending order according to their comprehensive green evaluation values, and select the initial population from the sorted device set according to the set selection strategy; the probability of a device being selected is as follows: , Formula (14); in, Indicates The probability of a device being selected; is an adjustable parameter that controls the steepness of the selection probability distribution; The larger the value, the greater the difference in selection probability between the top ranked devices. The smaller the value, the smaller the difference in selection probability between the top ranked devices; Indicates The comprehensive green value of each device; and Respectively represent the minimum and maximum values ​​of the comprehensive greenness of all devices.

9. The hybrid flow shop scheduling optimization method based on equipment greenness ranking as claimed in claim 8 is characterized in that: 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 hybrid flow shop scheduling optimization result, including: (1): Initialize the population; (2): Fitness function: Based on the constructed comprehensive green evaluation system, the fitness function is optimized with the minimum comprehensive greenness as the solution; Objective function: ； Constraints: ； in, represents the total carbon emission weight of the workshop, represents the total completion time weight of the workshop, represents the total cost weight of the workshop, represents the total performance weight of the workshop; represents the total carbon emissions of workshop production; Indicates the total completion time of workshop production; It represents the total production cost of the workshop; Indicates the overall production performance of the workshop; represents the total carbon emission constraint of the workshop; Provide production delivery constraints for the workshop; is the total production cost constraint of the workshop; is the total performance constraint of workshop production; It represents the normalization function, and its goal is to solve the problem of different dimensions of indicators; Indicates the maximum value of the indicator. Indicates the minimum value of the indicator; (3): Mutation: When performing the mutation operation on the equipment in the mixed flow workshop, select the equipment before sorting according to the comprehensive greenness ranking results of each parallel equipment. Devices with the same bit as mutation candidates , and assign a propensity factor to each device based on the ranking ,in, The value of increases as the device is ranked higher; for the device variable to be mutated ,from Select a device variable in As mutation target; calculate mutation vector ; ； ； in, Sort by the overall greenness of the parallel devices The results are adjusted dynamically. , The larger the value, the closer the mutated value is to ; The later the order , The smaller the value; Indicates the total number of parallel devices; (4): Crossover: Perform a crossover operation between the differential mutation population and the parent individuals to generate offspring individuals; ； in, represents the crossover probability in the range [0,1]; is a random function; (5): Selection operation: A greedy algorithm is used to perform the selection operation. According to the comparison results of the individual comprehensive greenness values, the 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 re-executes steps (2) to (4) until the iteration termination condition is met; (7): Output the optimal scheduling optimization results to generate the optimal solution.

10. A hybrid flow shop scheduling optimization system based on equipment greenness ranking is characterized by: include: The system building module is configured to: construct a comprehensive green evaluation index system for scheduling mixed flow workshops; A matrix module is configured to: construct a comprehensive green evaluation matrix for each device based on the comprehensive green evaluation index system; An evaluation degree determination module is configured to: calculate the relative progress and the grey correlation degree respectively according to the comprehensive green evaluation matrix of each device; determine the comprehensive green evaluation value of each device according to the relative progress and the grey correlation degree; An initial population screening module is configured to: sort the devices in descending order according to their comprehensive green evaluation values, and screen out an initial population from the sorted device set according to a set selection strategy; The output module is configured to: use the initial population as the input value of the differential evolution algorithm, execute the differential evolution algorithm, obtain the optimal solution, and use the optimal solution as the hybrid flow shop scheduling optimization result.

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