Master-slave joint optimization method for assembly line balance and assembly sequence planning

Abstract

The invention provides a master-slave joint optimization method for assembly line balance and assembly sequence planning, and the method comprises the steps: constructing a master-slave joint optimization (LFJO) model from the perspective of game theory optimization, and the model comprises an upper-layer optimization problem of assembly sequence planning (ASP) and a lower-layer optimization problem of assembly line balance (ALB). According to the method, an LFJO mechanism of dynamic interactive optimization decision making is provided, and the LFJO mechanism comprises an LFJO model construction mechanism containing different decision maker requirements; and a layered interaction dynamic evaluation mechanism, namely, an objective function, engineering constraints and associated logic constraints constructed according to the requirements of different decision makers. Through the implementation of the improved genetic algorithm, the LFJO model is applied to some situations. The method has feasibility and potential for truly representing the coupling relation between the ASP and the ALB, and a satisfactory comprehensive optimization scheme is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of assembly process planning, and in particular to a master-slave joint optimization method for assembly line balancing and assembly sequence planning. Background Art

[0002] Assembly process planning plays an important role in production planning and control. It involves the determination of ASP and ALB problems required for product production to ensure the effective utilization of equipment and meet personalized needs. Due to the complexity of mixed assembly lines and the fact that ASP and ALB problems usually have several conflicting criteria, choosing an appropriate assembly process planning requires the expertise and experience of decision makers. Therefore, a joint optimization framework is needed to reflect the conflicting connotations of these decision problems and provide a balanced solution.

[0003] Among them, the ASP and ALB joint problem not only has a hierarchical decision structure, but also adopts different decision variables and optimization objectives. The optimal solution of this problem is determined by the dynamic decision process of two independent subjects, which are problems across two different fields. Coordination between multiple engineering decision problems means balanced decisions between multiple objectives, in which different stakeholders work for different interests and must compromise with other stakeholders to achieve a common solution. Therefore, it is impossible and unreasonable to find the optimal objectives of all optimization problems. In addition, this aggregation has computational and organizational complexity in many cases because they are optimization problems in different fields of assembly systems.

[0004] In addition, another problem is that the current research on the ASP and ALB integration problem focuses on a single product, without involving the ASP and ALB integration optimization of mixed products. With the rapid growth of personalized demand, more and more companies are adopting the order-based production model, which requires the production system to be able to produce a variety of different types of products according to customer specifications. The output of these products is different, and the order completion time or production time can be improved through reasonable settings. The mixed model assembly line is popular among companies because it can adapt to the production characteristics of multiple varieties and small batches. Considering the ASP and ALB integration problem of mixed products is more difficult than the ALB problem of mixed products or the integration problem of ASP and ALB of a single product. Therefore, it is of practical significance to integrate the ASP and ALB problem into the assembly of mixed products.

[0005] In view of this, this application is filed. Summary of the invention

[0006] The present invention provides a master-slave joint optimization method for assembly line balancing and assembly sequence planning, which can at least partially improve the above problems.

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

[0008] A master-slave joint optimization method for assembly line balancing and assembly sequence planning, comprising:

[0009] Obtain the assembly priority relationship of different products and obtain the comprehensive task priority diagram of the products;

[0010] Get the connector information of different products and get the connector information of the product family;

[0011] Obtaining a product family connector comprehensive priority map according to the product task comprehensive priority map and the connector information of the product family;

[0012] Based on the preset comprehensive optimization objectives and constraint information, establish the ASP and ALB joint optimization model;

[0013] The comprehensive task priority map of the product, the connector information of the product family and the comprehensive priority map of the connectors of the product family are input into the ASP and ALB joint optimization model, and based on the hierarchical interactive dynamic evaluation mechanism, a comprehensive optimization plan for connector ASP and connector configuration to a fixed number of workstations ALB is output.

[0014] In summary, the master-slave joint optimization method for assembly line balancing and assembly sequence planning constructs a master-slave joint optimization (LFJO) model from the perspective of game theory optimization, including the upper-level optimization problem of assembly sequence planning (ASP) and the lower-level optimization problem of assembly line balancing (ALB). This method proposes a LFJO mechanism for dynamic interactive optimization decision-making, including a LFJO model construction mechanism that includes the needs of different decision makers; and a hierarchical interactive dynamic evaluation mechanism, that is, the objective function, engineering constraints and associated logical constraints constructed according to the needs of different decision makers. Through the implementation of an improved genetic algorithm, the LFJO model is applied to certain situations. This method has the feasibility and potential to truly characterize the coupling relationship between ASP and ALB, and obtains a satisfactory comprehensive optimization solution. Thus, the master-slave game joint optimization problem of ASP and ALB in the assembly process planning process of the hybrid model assembly line is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a master-slave joint optimization method for assembly line balancing and assembly sequence planning provided by an embodiment of the present invention;

[0016] Figure 2 It is a schematic diagram of a connector of a master-slave joint optimization method for assembly line balancing and assembly sequence planning provided by an embodiment of the present invention;

[0017] Figure 3 is an operation priority diagram of a mixed product provided by an embodiment of the present invention;

[0018] Figure 4 is the product family connector information provided by the embodiment of the present invention;

[0019] Figure 5 It is a connector integrated operation priority diagram of mixed products A, B & C provided by an embodiment of the present invention;

[0020] Figure 6 It is an example of a joint scenario of assembly line balancing and assembly sequence planning provided by an embodiment of the present invention;

[0021] Figure 7 It is the LFJO mechanism of dynamic interactive optimization decision-making provided by the embodiment of the present invention;

[0022] Figure 8 is a flow chart of an improved genetic algorithm provided by an embodiment of the present invention;

[0023] Fig. 9 is a diagram comparing the connector sequence and workstation configuration provided by the electric fan provided by an embodiment of the present invention;

[0024] Fig.10 is a comparison diagram of the working time of each workstation obtained by using three algorithms in the electric fan provided by the embodiment of the present invention;

[0025] Fig.11 is a comprehensive job priority map provided by an embodiment of the present invention;

[0026] Fig.12 is a connector integrated operation priority diagram provided by an embodiment of the present invention;

[0027] Fig.13 It is a comparison diagram of job allocation solutions obtained by using three methods provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] refer to Figure 1 , Figure 2 As shown, the first embodiment of the present invention discloses a master-slave joint optimization method for assembly line balancing and assembly sequence planning, which can be executed by a master-slave joint optimization device for assembly line balancing and assembly sequence planning (hereinafter referred to as the optimization device), and in particular, executed by one or more processors in the optimization device to implement the following method:

[0030] S1, obtain the assembly priority relationship of different products and obtain the comprehensive task priority diagram of the products;

[0031] Specifically, step S1 includes: the comprehensive task priority diagram of the product includes product assembly operation numbers and the immediate precedence relationship between assembly operation numbers.

[0032] In this embodiment, a comprehensive task priority diagram of products is determined according to the assembly priority relationship of different products; the comprehensive task priority diagram of products includes product assembly operation numbers and the immediate precedence relationship between assembly numbers.

[0033] S2, obtaining connector information of different products to obtain connector information of the product family;

[0034] Specifically, step S2 includes: the connector information of the product family includes connector assembly direction, connector assembly tools, connector comprehensive operation time, and connector immediately preceding process.

[0035] In this embodiment, the connector information of the product family is determined based on the connector information of different products; the connector information of the product series includes the connector assembly direction, connector assembly tools, connector comprehensive operation time, and connector immediate preceding process.

[0036] S3, obtaining a product family connector comprehensive priority map according to the product task comprehensive priority map and the connector information of the product family;

[0037] Specifically, step S3 includes: the product family connector comprehensive priority diagram includes connector assembly operation serial numbers, connector information connectors of the product series, and the immediate precedence relationship between assembly serial numbers.

[0038] In this embodiment, a comprehensive priority graph of product family connectors is constructed based on the task joint priority graph and the connector information of the product family; the connector joint priority graph of the product family includes the connector assembly operation serial number, the connector information connector of the product series, and the immediate precedence relationship between the assembly serial number.

[0039] S4, establishing a joint optimization model of ASP and ALB based on the preset comprehensive optimization objectives and constraint information;

[0040] Specifically, step S4 includes: the comprehensive optimization target includes the efficiency of the production line, the smoothness index, the assembly direction and the number of tool switching. The constraint information includes the assembly sequence constraint, the connector allocation constraint and the total workstation operation time constraint.

[0041] The formula of the ASP and ALB joint optimization model specifically includes:

[0042] The expression for the additional assembly time of product m in workstation k is: Where tD1 is the time taken for each 180° direction change, and tD2 is the time taken for each 90° direction change. is the number of times the assembly direction of product m changes by 180° in workstation k, is the number of 90° assembly direction changes of product m in workstation k, In the process from workstation k-1 to workstation k, whether the assembly direction of product m changes by 180° is 1 if yes and 0 if no. In the process from workstation k-1 to workstation k, whether the assembly direction of product m changes by 90°, the value is 1 if yes, and 0 if no;

[0043] The expression for the additional assembly time of product m caused by replacing the assembly tool in workstation k is: Among them, t L The time required to make changes to assembly tools, is the number of replacements of assembly tools for product m;

[0044] The expression of the total assembly operation time of workstation k is: in, is the assembly time of the connector of product m assigned to workstation k;

[0045] The upper fitness function is: Max J1 = LE / [SI*(L k +D 2k +D 1k )], Among them, LE is the efficiency of the production line, SI is the smoothness index, L k is the number of changes in the assembly tool for product m in workstation k, D 2k is the average number of times the product in workstation k changes 90° in assembly direction, D 1k is the average number of 180° changes in the assembly direction of the products in workstation k, and M is the number of products;

[0046] The expression of the precedence constraint of the integrated operation precedence graph of the product family connector is:

[0047]

[0048] Where K is the number of workstations, x ik is the decision variable for assigning the ith connector to workstation k, x jk is the decision variable for assigning the jth connector to workstation k, n is the total number of connectors, PRE(j) is the set of tasks that take precedence over connector j tasks based on the connector priority graph;

[0049] Taking the efficiency LE and smoothness index SI of the production line as the optimization objectives, the ALB problem is optimized to obtain the lower fitness function: Where LE is the efficiency of the production line, is the weight of the smoothness index SI, C is the maximum working time of the workstation;

[0050] Calculate the theoretical beat T c , the formula is: Where ρ is the theoretical workstation assembly time wide coefficient.

[0051] In this embodiment, based on the different needs of hierarchical decision makers, the efficiency, smoothness index, assembly direction and tool switching times of the production line are taken as comprehensive optimization goals, and the assembly sequence constraints, connector allocation constraints and total workstation operation time constraints are considered to establish an ASP and ALB joint optimization model.

[0052] The hierarchical mathematical model, that is, the formula of the ASP and ALB joint optimization model is described as follows:

[0053] Due to the change in assembly direction, the additional assembly time of product m in workstation k is as follows: The additional assembly time of product m caused by changing the assembly tool in workstation k is calculated as Calculate the total assembly time of workstation k:

[0054] The upper model generates feasible ASPs based on the connector integrated job priority graph and selects the optimal ASP. The upper fitness function for solution evaluation is Max J1 = LE / [SI*(L k +D 2k +D 1k )]; Used to record the number of times the assembly tool of product m is replaced. Whenever the assembly tool is changed, The number of times that product m changes its assembly direction by 90° in workstation k is recorded as When the assembly direction changes to 90°, The number of times that product m changes its assembly direction by 180° in workstation k is recorded as When the assembly direction changes 180°, The distribution of the assembly operation sequence of the product connector must satisfy the priority order constraints of the comprehensive operation priority graph of the product family connector. The constraint expression is:

[0055] Taking the efficiency LE and smoothness index SI of the production line as the optimization objectives, the ALB problem is optimized and the lower fitness function of the solution is:

[0056]

[0057] , where LE is the ratio of the working time of each workstation to the maximum working time of each workstation, that is, the efficiency of the production line, which can be calculated according to the formula Calculate. C is the maximum working time of the workstation, which can be calculated by SI is the smoothness index, which measures the working time of each workstation relative to the theoretical beat T c The lower the function value, the higher the smoothness. SI can be expressed as Calculation. Theoretical beat T c Pressable calculate. Among them, the equation Make sure that each connector can only be assigned to one workstation. The decision variable representing the assignment of each connector to a workstation is of type 0 / 1. Make sure that the assembly time at any station does not exceed the set assembly time. If any maximum cycle time limit is violated, connectors can no longer be assigned to the station.

[0058] See also Figure 6 and Figure 7 The model mechanism and model process of the ASP and ALB joint optimization model are as follows: Figure 6 and Figure 7 As shown in the figure, the ASP and ALB joint optimization model can be regarded as two layers, where the upper layer starts to work, firstly setting parameters, initializing the upper layer population, and judging the feasibility; when it is judged that the feasibility meets the standard, it enters the lower layer, and when it is judged that the feasibility does not meet the standard, the fitness value is set to 0. It is judged whether the end condition is met. If so, the global optimization solution is obtained and the process ends. If not, selection, crossover and mutation are performed, and the feasibility judgment is repeated.

[0059] To further describe the processing of the lower layer, when it is judged that the feasibility reaches the preset standard, the lower layer mixed loading line balance decision model is called to initialize the mixed loading line balance population and make a feasibility judgment; when it is judged that the feasibility reaches the standard, the fitness value is calculated, and when it is judged that the feasibility does not reach the standard, the fitness value is set to 0. Continue to calculate the preset conditions, and when the calculation result reaches the preset value, the lower layer optimization plan is obtained, the fitness value calculation of the upper layer is returned, and the end condition is continued to be judged; when the calculation result does not reach the preset value, selection, crossover and mutation are performed, and the feasibility judgment and other steps are continued in the lower layer.

[0060] S5, takes the task comprehensive priority map of the product, the connector information of the product family and the comprehensive priority map of the connector of the product family as input into the ASP and ALB joint optimization model, and based on the hierarchical interactive dynamic evaluation mechanism, outputs the comprehensive optimization plan of the connector ASP and the connector configuration to a fixed number of workstations ALB.

[0061] Specifically, step S5 includes: the hierarchical interactive dynamic evaluation mechanism includes an objective function, engineering constraints and associated logic constraints constructed according to the needs of different decision makers.

[0062] In this embodiment, based on the hierarchical interactive dynamic evaluation mechanism, that is, the objective function, engineering constraints and associated logical constraints constructed according to the needs of different decision makers, a master-slave joint optimization method for assembly line balancing and assembly sequence planning is proposed; to obtain a comprehensive optimization solution for connector ASP and connector configuration to a fixed number of workstations (ALB) under the dynamic hierarchical interactive dynamic evaluation mechanism.

[0063] Preferably, the method further comprises: verifying the comprehensive optimization scheme according to a preset example scenario to obtain a rationality value and an effectiveness value.

[0064] In this embodiment, a calculation example scenario is set to verify the rationality and effectiveness of the proposed master-slave joint optimization model and strategy for assembly line balancing and assembly sequence planning.

[0065] Specifically, in this embodiment, taking a mixed product A, B & C as an example, Figure 3 Display the job priority diagram for mixed products, Figure 4 Displays connector information for mixed products. Figure 5 This is a comprehensive operation priority diagram for connectors of mixed products A, B & C. Figure 6 It is a solution for the joint scenario of assembly line balancing and assembly sequence planning under this embodiment. The results show that the ASP solution of the hybrid product based on the connector is C1-C2-C4-C3-C5-C7-C8-C6-C9, which is the input of the ALB based on the connector. There are three workstations, and the results of ALB under this solution are 11, 10.83, and 11.

[0066] To further verify the practicality of the proposed master-slave joint optimization method, an electric fan is used as another example. Table 1 is the electric fan connector information table. The product includes 24 connectors. The assembly direction, assembly process, preceding process, and operation time are all reflected in the table. The product is jointly optimized for ASP and ALB at 5 workstations.

[0067] Table 1 Electric fan connector information

[0068] Connector sequence Assembly Assembler Comprehensive work time Preceding process C0 Z T1 178 C1 X T1 55 C2 Z T1 46 C3 -X T2 35 C4 X T1 59 C5\C6 C5 X T1 416 C6 X T1 556 C9 C7 Z T1 214 C8 -X T2 788 C9 X T2 661 C11\C12\C13\C16 C10 -X T1 336 C11 -Z T2 39 C12 Y T2 538 C13 -Z T4 352 C14 Z T1 244 C15 -Z T1 321 C14 C16 Y T2 476 C17 Z T1 294 C14 C18 -X T3 822 C17\C24 C19 Y T2 535 C20 -X T2 529 C21 -X T1 34 C22 -X T2 448 C23 -X T2 699 C0\C20\C21\C22 C24 Z T4 34

[0069] The optimization results obtained by the proposed LFJO method, ACA and GA are compared. In view of the large size of the feasible solution space of the case problem and the unpredictability of the optimal solution, the initial population size is set to 200, the crossover probability is set to 0.8, the mutation probability is set to 0.05, and the number of iterations is set to 100. The comparison results are shown in Fig. 9 , showing the fan connector sequence diagram and the workstation configuration scheme comparison diagram. Fig. 9 It can be seen that the time change of the 90° assembly direction of the solution obtained by the LFJO method is 5 times, which is significantly less than the best results of the other two methods. The number of tool changes in the station is 5 times, ACA is 4 times, and GA is 5 times. The number of changes in the 180° assembly direction of the solutions obtained by the three methods is 3 times. In addition, Fig.10 Comparison of the working time of each workstation of various methods shows that the working time of the LFJO method is more balanced. In general, the solution obtained by the LFJO method is better.

[0070] Fig.11 Shown is a comprehensive task priority diagram for another classic problem. Fig.12 The connector synthesis job priority graph for typical problems. ASP and ALB optimization were performed on the above classics using LFJO. The results were compared with the optimization schemes of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective genetic algorithm (MOGA). The operating environment is Windows 10, equipped with a 2.0GHz Pentium CPU, 1G ram, and programmed using Matlab 2018b. For Thom1-3, the population size is 50 and the number of iterations is 100; for other problems, the population size is 100 and the number of iterations is 200. The relevant parameters of NSGA-II are: crossover probability 0.8, mutation probability 0.05. Fig.13 The distribution results obtained by the three algorithms are shown. For example, the distribution scheme obtained by MOGA assigns connectors C2, C8, C5, C4, C11, C13, and C17 to workstation 1. Due to the different number of connectors contained in products A, B, and C, the actual distribution is as follows Fig.13 As shown. The connectors of product A assigned to workstation 1 include C2, C5, C4, C11, C13 and C17. The assembly time is 2.4, the assembly direction is switched once, and the assembly tool is switched 4 times. The connectors of product B assigned to workstation 1 include C2, C8, C5, C11 and C17. The assembly time is 2.7, the assembly direction is switched once, and the assembly tool is switched 3 times. The connectors of product C assigned to workstation 1 include C2, C8, C5, C11 and C13. The assembly time is 2.7, the assembly direction is switched once, and the assembly tool is switched once. Fig.13The data comparison shows that the LFJO method has obvious advantages in the joint optimization of ASP and ALB of mixed products. In addition, the LFJO method has considerable advantages in shortening the change time and improving the assembly efficiency of mixed products.

[0071] In summary, compared with the existing schemes, the master-slave joint optimization method for assembly line balancing and assembly sequence planning has the following beneficial effects: 1. Obtain feedback data of ALB decisions during ASP planning. This model can make the information obtained by leaders and followers more transparent, and finally obtain a more reasonable equilibrium solution. 2. According to the assembly priority diagram of the connector, the influence of the number of assembly direction changes and the number of assembly tool changes on the assembly time is analyzed, and they are included in the total assembly time of each workstation. Based on the analysis of the coupling relationship between ASP and ALB, the LJFO model of ASP and ALB is established. This model can truly express the relationship between the two in the assembly process planning process, making the optimization results more practical. 3. For multi-objective optimization problems, a nested genetic algorithm integrating ASP and ALB is used. Through case studies, the algorithm results based on this method are compared with those of NSGA-II and MOGA. According to the comparison results, this method can obtain more satisfactory joint configuration results of product ASP and ALB.

[0072] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A master-slave joint optimization method for assembly line balancing and assembly sequence planning, characterized in that: include: Obtain the assembly priority relationship of different products and obtain the comprehensive task priority diagram of the products; Get the connector information of different products and get the connector information of the product family; Obtaining a product family connector comprehensive priority map according to the product task comprehensive priority map and the connector information of the product family; Based on the preset comprehensive optimization objectives and constraint information, establish the ASP and ALB joint optimization model; The comprehensive task priority map of the product, the connector information of the product family and the comprehensive priority map of the connectors of the product family are input into the ASP and ALB joint optimization model, and based on the hierarchical interactive dynamic evaluation mechanism, a comprehensive optimization plan for connector ASP and connector configuration to a fixed number of workstations ALB is output.

2. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 1 is characterized in that: The comprehensive task priority diagram of the product includes product assembly operation serial numbers and the immediate precedence relationship between the assembly serial numbers.

3. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 2 is characterized in that: The connector information of the product family includes connector assembly direction, connector assembly tools, connector comprehensive operation time, and connector immediate preceding process.

4. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 3 is characterized in that: The product family connector comprehensive priority diagram includes connector assembly operation serial numbers, connector information connectors of the product series, and the immediate precedence relationship between assembly serial numbers.

5. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 4 is characterized in that: The comprehensive optimization objectives include the efficiency of the production line, the smoothness index, and the assembly direction and tool switching times.

6. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 5, characterized in that: The constraint information includes assembly sequence constraint, connector allocation constraint, and workstation operation total time constraint.

7. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 6 is characterized in that: The formula of the ASP and ALB joint optimization model specifically includes: The expression for the additional assembly time of product m in workstation k is: Where tD1 is the time taken for each 180° direction change, and tD2 is the time taken for each 90° direction change. is the number of times the assembly direction of product m changes by 180° in workstation k, is the number of 90° assembly direction changes of product m in workstation k, In the process from workstation k-1 to workstation k, whether the assembly direction of product m changes by 180° is 1 if yes and 0 if no. In the process from workstation k-1 to workstation k, whether the assembly direction of product m changes by 90°, the value is 1 if yes, and 0 if no; The expression for the additional assembly time of product m caused by replacing the assembly tool in workstation k is: Among them, t L The time required to make changes to assembly tools, is the number of replacements of assembly tools for product m; The expression of the total assembly operation time of workstation k is: in, is the assembly time of the connector of product m assigned to workstation k; The upper fitness function is: Max J1 = LE / [SI*(L k +D 2k +D 1k )], Among them, LE is the efficiency of the production line, SI is the smoothness index, L k is the number of changes in the assembly tool for product m in workstation k, D 2k is the average number of times the product in workstation k changes 90° in assembly direction, D 1k is the average number of 180° changes in the assembly direction of the products in workstation k, and M is the number of products; The expression of the precedence constraint of the integrated operation precedence graph of the product family connector is: Where K is the number of workstations, x ik is the decision variable for assigning the ith connector to workstation k, x jk is the decision variable for assigning the jth connector to workstation k, n is the total number of connectors, PRE(j) is the set of tasks that take precedence over connector j tasks based on the connector priority graph; Taking the efficiency LE and smoothness index SI of the production line as the optimization objectives, the ALB problem is optimized to obtain the lower fitness function: Where LE is the efficiency of the production line, is the weight of the smoothness index SI, C is the maximum working time of the workstation; Calculate the theoretical beat T c , the formula is: Where ρ is the theoretical workstation assembly time wide coefficient.

8. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 1, characterized in that: The hierarchical interactive dynamic evaluation mechanism includes objective functions, engineering constraints and associated logic constraints constructed according to the needs of different decision makers.

9. The master-slave joint optimization method for assembly line balancing and assembly sequence planning according to claim 1, characterized in that: Also includes: According to the preset calculation example scenario, the comprehensive optimization scheme is verified to obtain the rationality value and effectiveness value.

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