Automobile engine assembly line design method, system and device and storage medium

Abstract

The invention relates to an automobile engine assembly line design method, system and device and a storage medium, and relates to the technical field of assembly line design planning. According to the space physical structure, assembly tasks and the priority relation between the assembly tasks are determined; measuring and calculating the assembly time of each assembly task in three assembly modes of workers, robots and man-machine cooperation; based on the assembling time, the priority relation, the preset number of workers and the preset number of robots, a mixed integer linear programming model of the man-machine cooperation assembling line balance problem with the takt as the guide is established; optimizing according to the mixed integer linear programming model to obtain an assembly line balancing scheme; and forming an assembly scheme Gantt chart and annular assembly line station arrangement information according to analysis of the assembly line balance scheme, and sending the assembly scheme Gantt chart and the annular assembly line station arrangement information to a terminal held by an operator. The method has the effect of improving the comprehensive performance of the final assembly scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of assembly line design and planning, and in particular to a method, system, device and storage medium for designing an automobile engine assembly line. Background Art

[0002] Assembly line design planning refers to the process of comprehensively planning and designing the layout, process, equipment configuration, and personnel arrangement of the assembly line in the manufacturing industry based on the production requirements and process flow of the product. This process needs to consider multiple factors such as production efficiency, cost, quality, and safety to ensure that the assembly line can operate efficiently and stably to meet production goals.

[0003] As the number of cars in my country increases year by year, the rate of car scrapping also increases year by year. Scrapped cars not only occupy a large amount of renewable resources, but also contain parts or materials that are harmful to the environment. If these products cannot be handled in a timely and standardized manner, it will not only cause a waste of resources, but also pollute the environment. Therefore, scrapped cars need to be recycled and remanufactured to meet the requirements of sustainable development and green manufacturing. When remanufacturing automobile engines, it is generally necessary to build a human-machine collaborative assembly line to assemble the engine, and the assembly line of automobile engines is generally planned through production experience or simple heuristic rules.

[0004] When planning the assembly line of automobile engines through production experience or simple heuristic rules, it is easy to cause problems such as unbalanced operations, low efficiency, and production congestion on the assembly line, which in turn leads to poor overall performance of the final assembly plan. Summary of the invention

[0005] In order to improve the comprehensive performance of the final assembly solution, the present invention provides a method, system, device and storage medium for designing an automobile engine assembly line.

[0006] In a first aspect, the present invention provides a method for designing an automobile engine assembly line, which adopts the following technical solution:

[0007] A method for designing an automobile engine assembly line, comprising:

[0008] Obtain the spatial physical structure of a car engine;

[0009] Determine the assembly tasks and the priority relationship between each assembly task according to the physical structure of the space;

[0010] Calculate the assembly time of each assembly task under three assembly modes: human, robot, and human-machine collaborative;

[0011] A mixed integer linear programming model for the human-machine collaborative assembly line balancing problem oriented by beat is established based on assembly time, priority relationship and the preset number of workers and robots.

[0012] Optimize according to the mixed integer linear programming model to obtain the assembly line balancing solution;

[0013] The assembly plan Gantt chart and the circular assembly line station layout information are formed according to the assembly line balancing plan analysis, and the assembly plan Gantt chart and the circular assembly line station layout information are sent to the terminal held by the operator.

[0014] Optionally, the specific steps of establishing a mixed integer linear programming model include:

[0015] Define parameter symbols and decision variables, including:

[0016] I, j: assembly task number, the assembly task set is I, i∈I, and the maximum number of assembly tasks is |I|;

[0017] w: workstation number, the workstation set is W, w∈W, the maximum number of workstations is |W|, where |W|≤|I|;

[0018] s: assembly station mode, the set of which is S, s=1 represents a station operated by a person, s=2 represents a station operated by a robot, and s=3 represents a station operated by a human and a robot;

[0019] m: Task assembly mode, whose set is M, m∈{1,2,3} represents the three assembly modes of human, robot, and human-machine collaboration respectively;

[0020] t im : Assembly time of task i in mode m;

[0021] p ij : Task priority relationship, if task i is the immediate predecessor of task j, then p ij =1, otherwise p ij =0;

[0022] |W|: number of workers;

[0023] |R|: number of robots;

[0024] ψ1, ψ2: maximum values;

[0025] T C : The beat of the assembly line (non-negative variable);

[0026] x iws : Task allocation variable. If task i is assigned to workstation w in mode s, then x iws =1, otherwise x iws =0;

[0027] y iw : Task allocation variable. If task i is assigned to workstation w, then y iw=1, otherwise y iw =0;

[0028] z im : Task assembly mode variable. If task i is assembled using mode m, then z im =1, otherwise z im =0;

[0029] π ijws : Task assembly order variable. If task i is assembled before task j in station w, then π ijws =1, otherwise π ijws =0;

[0030] Station open state, if there is s station mode in station w, then otherwise

[0031] The completion time of task i (non-negative variable);

[0032] Set the objective function to minimize the cycle time of the assembly line, expressed as min f = T C ;

[0033] Set constraints, including the following:

[0034] Constraint 1: Relationship between assembly patterns for any task

[0035] Constraint 2: Relationship between task assignment variables

[0036] Constraint 3: Relationship between task allocation variables and assembly patterns

[0037]

[0038] Constraint 4: Priority constraint, the workstation of the previous task is not greater than the workstation of the next task

[0039]

[0040] Constraint 5: Priority constraint, the start time of the next task should not be less than the completion time of the previous task

[0041]

[0042] Constraint 6: Constraint on the order of tasks in the workstation

[0043]

[0044]

[0045] Constraint 7: Any task needs to be assigned to a workstation

[0046] Constraint 8: Workstation opening constraint

[0047] Constraint 9: Workstations are opened in sequence

[0048] Constraint 10: Relationship between task completion time and takt time

[0049] Constraint 11: Relationship between task completion time and task assembly time

[0050] Constraint 12: Worker number constraint, robot number constraint

[0051] Optionally, the method for determining the assembly line balancing solution includes:

[0052] Input assembly task and mixed integer linear programming model;

[0053] The coding sequence was determined based on the assembly task analysis;

[0054] Determine the maximum completion time of a single workstation based on the coding sequence and mixed integer linear programming model analysis;

[0055] Start iteration to randomly select individuals and form random values;

[0056] If and only if the random value is less than the preset crossover probability value, the preset crossover operation is performed;

[0057] If and only if the random value is less than the preset mutation probability value, the preset mutation operation is performed, and the crossover probability value is greater than the mutation probability value;

[0058] Execute the preset neighborhood search operation;

[0059] According to the number of iterations, the maximum completion time of a single workstation is selected and used as the optimal completion time of the single workstation;

[0060] The assembly line plan is determined based on the optimal completion time analysis of a single workstation, and is used as the assembly line balancing plan.

[0061] Optionally, the method for determining the coding sequence includes:

[0062] Numbering the assembly tasks based on the precedence relationship to form a task number value;

[0063] Construct a priority matrix based on the priority relationship;

[0064] Selecting unassigned and unpredetermined assembly tasks based on the priority matrix and using the corresponding task number value as the number selection value, and updating the priority matrix;

[0065] When all assembly tasks are selected, the number selection values ​​are combined in sequence to form an assembly task sequence;

[0066] The assembly task based on the assembly task sequence randomly selects reference values ​​corresponding to the assembly mode to form an assembly mode sequence;

[0067] The assembly task sequence and the assembly pattern sequence are combined to form a coding sequence.

[0068] Optionally, the crossover operation includes:

[0069] Based on the assembly task sequence, two exchange points are randomly selected to form a cross-task parent sequence between the two exchange points, and the assembly task sequence except the cross-task parent sequence is used as the cross-task remaining sequence, the assembly mode sequence corresponding to the cross-task parent sequence is used as the cross-mode parent sequence, and the assembly mode sequence except the cross-mode parent sequence is used as the cross-mode remaining sequence;

[0070] Based on the correspondence between the cross-task parent sequence and the preset cross-task neighborhood sequence, a cross-task neighborhood sequence corresponding to the cross-task parent sequence is determined;

[0071] Performing random transformation based on the cross-mode parent sequence to form a cross-mode child sequence;

[0072] The cross-task neighborhood sequence is combined with the cross-task residual sequence to form a cross-task adjustment sequence, the cross-mode subsequence is combined with the cross-mode residual sequence to form a cross-mode adjustment sequence, and the cross-task adjustment sequence and the cross-mode adjustment sequence are combined as a cross-comprehensive adjustment sequence to replace and form a new coding sequence.

[0073] Optionally, mutation operations include:

[0074] Based on the assembly task sequence, two exchange points are randomly selected as mutation selection position points;

[0075] Retrieve insertable position points based on assembly task sequence;

[0076] According to the consistency between the mutation selection position points and the insertable position points, the mutation task selection sequence is determined, and the assembly mode sequence corresponding to the mutation task selection sequence is used as the mutation mode selection sequence;

[0077] Based on the variant task selection sequence, the assembly task sequence is mutually exchanged and adjusted to form a variant task adjustment sequence; based on the variant mode selection sequence, the assembly mode sequence is mutually exchanged and adjusted to form a variant mode adjustment sequence;

[0078] The variation task adjustment sequence and the variation mode adjustment sequence are combined as a variation comprehensive adjustment sequence to replace and form a new coding sequence.

[0079] Optionally, the neighborhood search operation includes:

[0080] Based on the assembly task sequence, a position point is randomly selected as a neighborhood selection point, and the assembly task sequence corresponding to the neighborhood selection point is used as a neighborhood task selection sequence, and the assembly mode sequence corresponding to the neighborhood selection point is used as a neighborhood mode selection sequence;

[0081] Selecting a sequence according to the neighboring tasks to query the preceding and following tasks, inserting the assembly task sequence adjacently to form a neighboring task adjustment sequence, and inserting the assembly mode sequence adjacently to form a neighboring mode adjustment sequence;

[0082] The neighborhood task adjustment sequence and the neighborhood pattern adjustment sequence are combined as the neighborhood comprehensive adjustment sequence to replace and form a new coding sequence.

[0083] In a second aspect, the present invention provides an automobile engine assembly line design system, which adopts the following technical solution:

[0084] An automobile engine assembly line design system, comprising:

[0085] The acquisition module is used to obtain the spatial physical structure and the number of iterations;

[0086] A memory for storing a program of the automobile engine assembly line design method according to any one of the first aspects;

[0087] The program in the memory can be loaded and executed by the processor to implement the automobile engine assembly line design method as described in any one of the first aspects.

[0088] In a third aspect, the present invention provides a design device for an automobile engine assembly line, which adopts the following technical solution:

[0089] A device for designing an automobile engine assembly line comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes any one of the methods for designing an automobile engine assembly line in the first aspect.

[0090] In a fourth aspect, the present invention provides a computer storage medium capable of storing a corresponding program, which has the characteristic of facilitating the improvement of the comprehensive performance of the final assembly solution, and adopts the following technical solution:

[0091] A computer-readable storage medium stores a computer program that can be loaded by a processor and executes the automobile engine assembly line design method as described in any one of the first aspects.

[0092] In summary, the present invention includes at least one of the following beneficial technical effects:

[0093] 1. By acquiring the physical structure of the space and determining the assembly tasks and their priority relationships, and calculating the assembly time of each assembly task under the three assembly modes of workers, robots, and human-machine collaboration, a mixed integer linear programming model is established and optimized to obtain an assembly line balance plan. Then, through the assembly line balance plan analysis, an assembly plan Gantt chart and circular assembly line station layout information are formed and sent to the terminal held by the operator. When designing the engine assembly line, the assembly time of the assembly task under different assembly modes is optimized using a mixed integer linear programming model to obtain the design result of the assembly line, thereby reducing problems such as unbalanced operations, low efficiency, and production congestion on the assembly line, thereby improving the comprehensive performance of the final assembly plan;

[0094] 2. By defining parameter symbols and decision variables, and setting objective functions and constraints, a mixed integer linear programming model is established, which facilitates the subsequent use of the mixed integer linear programming model to design the assembly line, thereby improving the comprehensive performance of the final assembly solution;

[0095] 3. By inputting the assembly task and the mixed integer linear programming model, analyzing and determining the coding sequence and the maximum completion time of a single workstation, randomly selecting individuals and forming random values ​​to start iteration, selecting the optimal completion time of a single workstation and analyzing and determining the assembly line plan, and then using the assembly line plan as the assembly line balancing plan, the accuracy of the obtained assembly line balancing plan is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 It is a flow chart of the method for designing an automobile engine assembly line according to an embodiment of the present application.

[0097] Figure 2 It is a method flow chart of a method for determining an assembly line balancing solution according to an embodiment of the present application.

[0098] Figure 3 It is a schematic diagram of the crossover operation of an embodiment of the present application.

[0099] Figure 4 It is a schematic diagram of the variation operation of the embodiment of the present application.

[0100] Figure 5 It is a schematic diagram of a neighborhood search according to an embodiment of the present application.

[0101] Figure 6 It is an assembly task priority relationship diagram of an embodiment of the present application.

[0102] Figure 7 It is a Gantt chart of the assembly line balancing solution of the embodiment of the present application.

[0103] Figure 8 It is a schematic diagram of a circular assembly line according to an embodiment of the present application.

[0104] Fig. 9 It is a system flow chart of the automobile engine assembly line design according to an embodiment of the present application.

[0105] Explanation of the accompanying drawings: 1. Acquisition module; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0106] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0107] The embodiment of the invention discloses a method for designing an automobile engine assembly line.

[0108] Reference Figure 1 , a method for designing an automobile engine assembly line includes:

[0109] Step S100: Acquire the spatial physical structure of the automobile engine.

[0110] The spatial physical structure refers to a structure used to indicate the size specifications of various parts in a car engine in physical space and the connection relationship between them. The spatial physical structure can be obtained by the operator inputting a three-dimensional model in advance.

[0111] Step S200: Determine assembly tasks and priority relationships between assembly tasks according to the spatial physical structure.

[0112] Among them, the assembly task refers to the task of combining and installing the various parts in the automobile engine according to the actual required assembly process. The priority relationship refers to the sequence of combining and installing the various parts. The assembly tasks are divided according to the correlation between the various parts by decomposing the indivisible attributes between the various parts in the spatial physical structure, and the priority relationship is determined according to the order of the assembly processes corresponding to the parts that need to be combined and installed in each assembly task, so that they can be used later.

[0113] Step S300: Calculate the assembly time of each assembly task in three assembly modes: worker, robot, and human-machine collaborative assembly.

[0114] Among them, assembly time refers to the time required to assemble the parts corresponding to each assembly task under different assembly modes. The assembly time can be obtained by retrieving the historical time spent on each assembly task under different assembly modes. The assembly time can also be obtained by testing the time spent on each assembly task under different assembly modes using the stopwatch method and then inputting it.

[0115] Step S400: A mixed integer linear programming model for the human-machine collaborative assembly line balancing problem oriented by beat is established based on the assembly time, priority relationship, and the preset number of workers and robots.

[0116] The number of workers refers to the number of all workers on the entire assembly line when assembling automobile engines, and the number of robots refers to the number of all robots on the entire assembly line when assembling automobile engines. The number of workers and the number of robots are obtained after pre-input by the operator. A mixed integer linear programming model for the beat-oriented human-machine collaborative assembly line balance problem is established by considering the assembly time, priority relationship, and the preset number of workers and robots, so as to facilitate subsequent use.

[0117] The mixed integer linear programming model for the balancing problem of the human-machine collaborative assembly line has the following assumptions: the types of products to be assembled are unique, the quantity is sufficient, the parts are complete, and unexpected interruptions of the production line are ignored; the relevant information of the human-machine collaborative assembly line is determined, including the number of workers and machines, the daily assembly quantity, and the daily effective assembly time; the information of parts and assembly tasks is determined, including the priority relationship and the task assembly time under different assembly modes.

[0118] exist Figure 1 In step S400 shown, in order to further ensure the rationality of the mixed integer linear programming model, it is necessary to perform further separate analysis and calculation on the establishment of the mixed integer linear programming model, which is specifically described in detail through the following steps.

[0119] The specific steps of encryption processing include the following steps:

[0120] Define parameter symbols and decision variables, including:

[0121] I, j: assembly task number, the assembly task set is I, i∈I, and the maximum number of assembly tasks is |I|;

[0122] w: workstation number, the workstation set is W, w∈W, the maximum number of workstations is |W|, where |W|≤|I|;

[0123] s: assembly station mode, the set of which is S, s=1 represents a station operated by a person, s=2 represents a station operated by a robot, and s=3 represents a station operated by a human and a robot;

[0124] m: Task assembly mode, whose set is M, m∈{1,2,3} represents the three assembly modes of human, robot, and human-machine collaboration respectively;

[0125] t im : Assembly time of task i in mode m;

[0126] p ij : Task priority relationship, if task i is the immediate predecessor of task j, then p ij =1, otherwise p ij =0;

[0127] |W|: number of workers;

[0128] |R|: number of robots;

[0129] ψ1, ψ2: maximum values;

[0130] T C : The beat of the assembly line (non-negative variable);

[0131] x iws : Task allocation variable. If task i is assigned to workstation w in mode s, then x iws =1, otherwise x iws =0;

[0132] y iw : Task allocation variable. If task i is assigned to workstation w, then y iw =1, otherwise y iw =0;

[0133] z im : Task assembly mode variable. If task i is assembled using mode m, then z im =1, otherwise z im =0;

[0134] π ijws : Task assembly order variable. If task i is assembled before task j in station w, then π ijws =1, otherwise π ijws =0;

[0135] Station open state, if there is s station mode in station w, then otherwise

[0136] The completion time of task i (non-negative variable);

[0137] Set the objective function to minimize the cycle time of the assembly line, expressed as min f = T C ;

[0138] Set constraints, including the following:

[0139] Constraint 1: Relationship between assembly patterns for any task

[0140] Constraint 2: Relationship between task assignment variables

[0141] Constraint 3: Relationship between task allocation variables and assembly patterns

[0142]

[0143] Constraint 4: Priority constraint, the workstation of the previous task is not greater than the workstation of the next task

[0144]

[0145] Constraint 5: Priority constraint, the start time of the next task should not be less than the completion time of the previous task

[0146]

[0147] Constraint 6: Constraint on the order of tasks in the workstation

[0148]

[0149]

[0150] Constraint 7: Any task needs to be assigned to a workstation

[0151] Constraint 8: Workstation opening constraint

[0152] Constraint 9: Workstations are opened in sequence

[0153] Constraint 10: Relationship between task completion time and takt time

[0154] Constraint 11: Relationship between task completion time and task assembly time

[0155] Constraint 12: Worker number constraint, robot number constraint

[0156] Step S500: Optimizing according to a mixed integer linear programming model to obtain an assembly line balancing solution.

[0157] Among them, the assembly line balancing plan refers to a plan that enables the entire assembly line to operate in a balanced manner when assembling automobile engines. The assembly line balancing plan is obtained by optimizing the mixed integer linear programming model, which is convenient for subsequent use.

[0158] Step S600: forming an assembly plan Gantt chart and circular assembly line workstation layout information according to the assembly line balancing plan analysis, and sending the assembly plan Gantt chart and circular assembly line workstation layout information to the terminal held by the operator.

[0159] Among them, the assembly plan Gantt chart refers to the Gantt chart that shows that the entire assembly line can operate in a balanced manner when assembling automobile engines, and the circular assembly line station layout information refers to the layout information of the entire assembly line when the automobile engine is assembled in a circular manner. The assembly plan Gantt chart and the circular assembly line station layout information are formed through assembly line balance plan analysis and sent to the terminal held by the operator, so that the operator can directly use the assembly plan Gantt chart and the circular assembly line station layout information to layout the assembly line, reduce problems such as unbalanced operations, low efficiency, and production congestion on the assembly line, and thus improve the overall performance of the final assembly plan.

[0160] exist Figure 1 In step S500, in order to further ensure the rationality of the assembly line balancing solution, it is necessary to further analyze and calculate the assembly line balancing solution. Figure 2 The steps shown are explained in detail.

[0161] Reference Figure 2 , the method for determining the assembly line balancing plan includes the following steps:

[0162] Step S510: Input the assembly task and the mixed integer linear programming model.

[0163] Step S520: Determine the coding sequence according to the assembly task analysis.

[0164] The coding sequence refers to the sequence formed after encoding each assembly task. The coding sequence is determined by analyzing the assembly tasks to facilitate subsequent use.

[0165] exist Figure 2 In step S520 shown, in order to further ensure the rationality of the coding sequence, it is necessary to perform further separate analysis and calculation on the coding sequence, which is specifically described in detail through the following steps.

[0166] The method for determining the coding sequence comprises the following steps:

[0167] Step S521: Numbering the assembly tasks based on the priority relationship to form task number values.

[0168] Among them, the task number value refers to the numerical value corresponding to each assembly task numbered according to the assembly priority. By numbering the assembly tasks according to the priority relationship and forming the task number value, it is convenient for subsequent use.

[0169] Step S522: construct a priority matrix according to the priority relationship.

[0170] Among them, the priority matrix refers to a matrix diagram formed according to the priority and allocation of assembly tasks. Each assigned assembly task is analyzed through the priority relationship, and a priority matrix is ​​constructed to facilitate subsequent use.

[0171] Step S523: Selecting unassigned and non-predecessor assembly tasks based on the priority matrix and using the corresponding task number value as the number selection value, and updating the priority matrix.

[0172] Among them, by selecting the unassigned and non-predecessor assembly tasks in the priority matrix, and using the task number value corresponding to the selected assembly task as the number selection value, it is convenient for subsequent use, and the priority matrix is ​​updated to facilitate subsequent selection of the priority matrix.

[0173] Step S524: After all assembly tasks are selected, the numbered selected values ​​are combined in sequence to form an assembly task sequence.

[0174] Among them, the assembly task sequence refers to a series of number selection values ​​corresponding to each assembly task arranged in order. After all assembly tasks are selected, it means that all assembly tasks have corresponding number selection values. At this time, all number selection values ​​are combined to form an assembly task sequence for subsequent use.

[0175] Step S525: randomly selecting reference values ​​corresponding to assembly modes based on the assembly tasks in the assembly task sequence to form an assembly mode sequence.

[0176] Among them, the assembly mode sequence refers to the reference values ​​corresponding to the modes used in each assembly task, which are arranged according to the assembly task sequence. The reference values ​​corresponding to the assembly modes are randomly selected for the assembly tasks in the assembly task sequence, and the assembly mode sequence is formed after being arranged according to the assembly task sequence, so as to facilitate subsequent use. The reference value corresponding to the assembly mode of a worker is 1, the reference value corresponding to the assembly mode of a robot is 2, and the reference value corresponding to the assembly mode of a human-machine collaboration is 3.

[0177] Step S526: Combine the assembly task sequence and the assembly mode sequence to form a coding sequence.

[0178] The accuracy of the obtained coding sequence is improved by combining the assembly task sequence and the assembly mode sequence and using the combined sequence as the coding sequence.

[0179] Step S530: Determine the maximum completion time of a single workstation based on the coding sequence and the mixed integer linear programming model analysis.

[0180] Among them, the maximum completion time of a single workstation refers to the maximum time required for a single workstation to complete the corresponding assembly task. By analyzing it based on the coding sequence and using a mixed integer linear programming model, the maximum completion time of a single workstation can be determined for subsequent use.

[0181] Step S540: Start iteration to randomly select individuals and form random values.

[0182] Among them, the random value refers to a corresponding numerical value randomly selected from the coding sequence. The random value is formed by random selection to facilitate subsequent use.

[0183] Step S550: If and only if the random value is less than a preset crossover probability value, perform a preset crossover operation.

[0184] The crossover probability value refers to the maximum probability value that can be tolerated when a crossover operation is required, and the crossover probability value is obtained after being pre-input by the operator. The crossover operation refers to an operation of cross-changing a section of the coding sequence, and the crossover operation is obtained after being pre-input by the operator.

[0185] The default crossover operation includes the following steps:

[0186] Step S551: Randomly select two exchange points based on the assembly task sequence to form a cross-task parent sequence between the two exchange points, and use the assembly task sequence except the cross-task parent sequence as the cross-task remaining sequence, the assembly mode sequence corresponding to the cross-task parent sequence as the cross-mode parent sequence, and the assembly mode sequence except the cross-mode parent sequence as the cross-mode remaining sequence.

[0187] Among them, the cross task parent sequence refers to the sequence corresponding to when no cross operation is performed between two exchange points in the assembly task sequence, the cross task remaining sequence refers to the sequence outside the two exchange points in the assembly task sequence, the cross mode parent sequence refers to the sequence corresponding to when no cross operation is performed between two exchange points in the assembly mode sequence, and the cross mode remaining sequence refers to the sequence outside the two exchange points in the assembly mode sequence. By defining the cross task parent sequence, the cross task remaining sequence, the cross mode parent sequence and the cross mode remaining sequence respectively, it is convenient for subsequent use.

[0188] Step S552: based on the correspondence between the cross-task parent sequence and the preset cross-task neighborhood sequence, determine the cross-task neighborhood sequence corresponding to the cross-task parent sequence.

[0189] The cross-task neighborhood sequence refers to the sequence corresponding to the cross-task parent sequence for reference, and the cross-task neighborhood sequence is obtained by querying from a database storing the cross-task neighborhood sequence and the cross-task parent sequence. The cross-task neighborhood sequence is determined by querying the cross-task parent sequence, so as to facilitate subsequent use.

[0190] Step S553: ​​Perform random transformation based on the cross-mode parent sequence to form a cross-mode child sequence.

[0191] Among them, the cross-mode subsequence refers to a sequence formed by randomly transforming the cross-mode parent sequence. The cross-mode subsequence is formed by randomly transforming the cross-mode parent sequence, which is convenient for subsequent use.

[0192] Step S554: Combine the cross-task neighborhood sequence with the cross-task residual sequence to form a cross-task adjustment sequence, combine the cross-mode subsequence with the cross-mode residual sequence to form a cross-mode adjustment sequence, and combine the cross-task adjustment sequence and the cross-mode adjustment sequence as a cross-comprehensive adjustment sequence to replace and form a new coding sequence.

[0193] Among them, the cross-task adjustment sequence refers to the sequence after cross-adjustment of the assembly task sequence, the cross-mode adjustment sequence refers to the sequence after cross-adjustment of the assembly mode sequence, and the cross-integrated adjustment sequence refers to the sequence after cross-task adjustment sequence and cross-mode adjustment sequence are integrated.

[0194] The accuracy of the obtained coding sequence is improved by combining the cross-task neighborhood sequence with the cross-task residual sequence as a cross-task adjustment sequence, combining the cross-mode subsequence with the cross-mode residual sequence as a cross-mode adjustment sequence, and combining the cross-task adjustment sequence and the cross-mode adjustment sequence as a cross-comprehensive adjustment sequence to replace and form a new coding sequence.

[0195] Reference Figure 3 In this embodiment, the cross-mode subsequence is also partially adjusted for disassembly variables based on the cross-mode subsequence. When the assembly task sequence is 124563, the assembly mode sequence is 231213, and the cross-task parent sequence is 2456, the cross-mode parent sequence is 3131. At this time, the query determines that the cross-task neighborhood sequence is 4652, and the cross-mode subsequence is randomly selected to be 2313. After the disassembly variables of the cross-mode subsequence are adjusted, the final cross-task adjustment sequence is 146523, and the cross-mode adjustment sequence is 213123.

[0196] Step S560: If and only if the random value is less than the preset mutation probability value, execute the preset mutation operation.

[0197] Among them, the crossover probability value is greater than the mutation probability value. The mutation probability value refers to the maximum probability value that can be tolerated when a mutation operation is required. The mutation probability value is obtained after the operator pre-enters. The mutation operation refers to the operation of performing a mutation change on the coding sequence by replacing two of them. The mutation operation is obtained after the operator pre-enters.

[0198] The preset mutation operation includes the following steps:

[0199] Step S561: Randomly select two exchange points as variation selection position points based on the assembly task sequence.

[0200] Among them, the mutation selection position point refers to the position point selected when performing the mutation operation. By randomly selecting two positions from the assembly task sequence and using them as the mutation selection position points, it is convenient for subsequent use.

[0201] Step S562: Retrieve insertable position points based on the assembly task sequence.

[0202] The insertable position point refers to the position point corresponding to the assembly task sequence that can be inserted into the sequence. The insertable position point is retrieved by querying the assembly tasks corresponding to each position in the assembly task sequence, so as to facilitate subsequent use.

[0203] Step S563: Determine the mutation task selection sequence according to the consistency between the mutation selection position points and the insertable position points, and use the assembly mode sequence corresponding to the mutation task selection sequence as the mutation mode selection sequence.

[0204] Among them, the mutation task selection sequence refers to the sequence of selected positions in the assembly task sequence when performing the mutation operation. The consistency between the mutation selection position points and the insertable position points is analyzed. When there is consistency, the sequence corresponding to the consistent position points is used as the mutation task selection sequence. When there is inconsistency, the mutation selection position points are reselected.

[0205] The mutation mode selection sequence refers to the sequence of positions selected in the assembly mode sequence when performing a mutation operation. By using the assembly mode sequence corresponding to the mutation task selection sequence as the mutation mode selection sequence, subsequent use is facilitated.

[0206] Step S564: selecting a sequence based on the variant task to exchange and adjust the assembly task sequence to form a variant task adjustment sequence, and selecting a sequence based on the variant mode to exchange and adjust the assembly mode sequence to form a variant mode adjustment sequence.

[0207] The variation task adjustment sequence refers to the sequence after the assembly task sequence is mutated and adjusted, and the variation mode adjustment sequence refers to the sequence after the assembly mode sequence is mutated and adjusted.

[0208] By exchanging and adjusting the variant task selection sequences in the assembly task sequence to form a variant task adjustment sequence, and exchanging and adjusting the variant mode selection sequences in the assembly mode sequence to form a variant mode adjustment sequence, subsequent use is facilitated.

[0209] Step S565: combining the mutated task adjustment sequence and the mutated mode adjustment sequence as a mutated comprehensive adjustment sequence to replace and form a new coding sequence.

[0210] Among them, the variation task adjustment sequence and the variation mode adjustment sequence are combined as a variation comprehensive adjustment sequence to replace and form a new coding sequence, thereby improving the accuracy of the obtained coding sequence.

[0211] Reference Figure 4 In this embodiment, the variation mode adjustment sequence is also adjusted for partial disassembly variables. When the assembly task sequence is 146523, the assembly mode sequence is 231231, and the variation selection position points are the third and fifth positions, the variation task selection sequence is 6 and 2, and the variation mode selection sequence is 1 and 3. After the variation operation, the variation task adjustment sequence is 142563, and the variation mode adjustment sequence is 233211. After the partial disassembly variables are adjusted, the variation mode adjustment sequence is 223211.

[0212] Step S570: Execute a preset neighborhood search operation.

[0213] The neighborhood search operation refers to an operation of performing adjacent insertion changes on a coding sequence, and the neighborhood search operation is obtained through pre-input by an operator.

[0214] The default neighborhood search operation includes the following steps:

[0215] Step S571: randomly selecting a position point as a neighborhood selection point based on the assembly task sequence, and taking the assembly task sequence corresponding to the neighborhood selection point as the neighborhood task selection sequence, and taking the assembly mode sequence corresponding to the neighborhood selection point as the neighborhood mode selection sequence.

[0216] Among them, the neighborhood selection point refers to the position point randomly selected from the assembly task sequence when performing a domain search operation, the neighborhood task selection sequence refers to the sequence of positions corresponding to the neighborhood selection points in the assembly task sequence, and the neighborhood pattern selection sequence refers to the sequence of positions corresponding to the neighborhood selection points in the assembly pattern sequence.

[0217] By randomly selecting a position point in the assembly task sequence as a neighborhood selection point, and defining the neighborhood task selection sequence and the neighborhood mode selection sequence according to the neighborhood selection point, it is convenient for subsequent use.

[0218] Step S572: Select a sequence according to the neighboring tasks to query the preceding and following tasks, so as to insert the assembly task sequence adjacently and form a neighboring task adjustment sequence, and to insert the assembly mode sequence adjacently and form a neighboring mode adjustment sequence.

[0219] The neighborhood task adjustment sequence refers to the sequence after the neighborhood search adjustment is performed on the assembly task sequence, and the neighborhood mode adjustment sequence refers to the sequence after the neighborhood search adjustment is performed on the assembly mode sequence.

[0220] By querying the preceding and following tasks of the neighborhood task selection sequence, when the query is found, the neighborhood task selection sequence is inserted after the adjacent sequence to form a neighborhood task adjustment sequence, and at the same time, the neighborhood mode selection sequence is inserted after the adjacent sequence to form a neighborhood mode adjustment sequence, and when not found, the neighborhood selection point is reselected.

[0221] Step S573: combining the neighborhood task adjustment sequence and the neighborhood mode adjustment sequence as a neighborhood comprehensive adjustment sequence to replace and form a new coding sequence.

[0222] Among them, the accuracy of the obtained coding sequence is improved by combining the neighborhood task adjustment sequence with the neighborhood mode adjustment sequence as the neighborhood comprehensive adjustment sequence to replace and form a new coding sequence.

[0223] Reference Figure 5 In this embodiment, the neighborhood pattern adjustment sequence is also adjusted for partial disassembly variables. When the assembly task sequence is 146523, the assembly pattern sequence is 231213, and the neighborhood selection point is the fourth position, the neighborhood task selection sequence is 5, and the neighborhood pattern selection sequence is 2. After the neighborhood search operation, the neighborhood task adjustment sequence is 146253, and the neighborhood pattern adjustment sequence is 231121. After the partial disassembly variables are adjusted, the neighborhood pattern adjustment sequence is 231321.

[0224] Step S580: Select the maximum completion time of a single workstation according to the number of iterations and use it as the optimal completion time of the single workstation.

[0225] Among them, the optimal completion time of a single workstation refers to the minimum value of the maximum completion time of a single workstation. After performing crossover operation, mutation operation or neighborhood search operation, the maximum completion time of a single workstation is selected according to the number of iterations, and the selected maximum completion time of a single workstation is used as the optimal completion time of a single workstation for subsequent use.

[0226] In step S580, in order to further ensure the rationality of the optimal completion time of a single workstation, it is necessary to further analyze and calculate the optimal completion time of a single workstation separately, which is explained in detail through the following steps.

[0227] The steps after forming the optimal completion time for a single workstation include the following steps:

[0228] Step S581: Get the iteration count value of the current time.

[0229] The iteration count value refers to the number of times corresponding to the current iteration, and the iteration count value is obtained by counting the iterations at the current time.

[0230] Step S582: Determine whether the number of iterations is equal to the preset maximum number of iterations. If yes, execute step S583; if no, execute step S584

[0231] The maximum iteration number value refers to the maximum number of iterations that can be achieved, and the maximum iteration number value is obtained by pre-input by the operator. By judging whether the iteration number value is equal to the preset maximum iteration number value, it is judged whether to continue the iteration.

[0232] Step S583: re-analyze the maximum completion time of a single workstation based on the new coding sequence and select a smaller value as the optimal completion time of a single workstation.

[0233] Among them, when the number of iterations is equal to the preset maximum number of iterations, it means that there is no need to continue iterating at this time. Therefore, the maximum completion time of a single workstation is re-analyzed through a new coding sequence, and a smaller value of the obtained maximum completion time of a single workstation is selected as the optimal completion time of a single workstation, thereby improving the accuracy of the obtained optimal completion time of a single workstation.

[0234] Step S584: Jump to execute step S540.

[0235] When the iteration number value is not equal to the preset maximum iteration number value, it means that the iteration needs to be continued, so the process jumps to step S540.

[0236] Step S590: Determine the assembly line plan based on the optimal completion time analysis of a single workstation, and use the assembly line plan as the assembly line balancing plan.

[0237] Among them, the assembly line plan refers to a plan that determines the rhythm of the assembly line based on the optimal completion time of a single workstation. The rhythm of the assembly line is determined by the optimal completion time of a single workstation, and then the assembly line plan is determined based on the rhythm of the assembly line and used as the assembly line balance plan, thereby improving the accuracy of the obtained assembly line balance plan.

[0238] Reference Figure 6 , Figure 7 and Figure 8 Taking a certain model of automobile engine remanufacturing assembly line as an example, a human-machine collaborative ring assembly line balance problem is constructed to analyze the application performance of the method of the present invention in actual engineering cases. The automobile engine contains 34 assembly tasks, and the priority relationship between the assembly tasks is as follows Figure 6 As shown. Set a total of 7 workstations to be opened, the number of workers is 6, and the number of robots is 8. Set the maximum number of iterations Imax = 200, the crossover probability value Pc = 0.8, and the mutation probability value Pm = 0.2.

[0239] The time required for the installation of various parts of a certain model of automobile engine by workers, robots and human-machine collaborative assembly is 38, 33 and 17 respectively for the installation of the alternator bracket, 23, 21 and 12 respectively for the installation of the alternator, 30, 35 and 15 respectively for the installation of the drive belt, 180, 165 and 86 respectively for the installation of the intake pipe, 144, 159 and 65 respectively for the installation of the exhaust pipe, and 730, 805 and 85 respectively for the installation of the cylinder head, distributor, camshaft and valve. 455, water pump pulleys are 56, 51, 29, oil filters are 60, 58, 25, special washers are 161, 182, 78, oil pans are 126, 130, 72, crankshaft pulleys are 10, 9, 6, oil filters are 38, 32, 21, shock absorber pulleys are 12, 15, 8, oil plugs are 10, 9, 6, oil level indicators are 12, 12, 7, front housing The upper covers of synchronous belts are 70, 62 and 33 respectively; the oil pumps are 54, 61 and 28 respectively; the lower covers of synchronous belts are 140, 127 and 56 respectively; the pistons and connecting rods are 24, 29 and 13 respectively; the synchronous belts are 45, 48 and 22 respectively; the connecting rod cups are 24, 21 and 14 respectively; the tensioner springs are 34, 34 and 20 respectively; the flywheels are 675, 694 and 334 respectively; the tensioners are 34, 32, 15; the rear plate is 44, 44, 26; the crankshaft sprocket flange is 70, 80, 43; the flywheel cover is 36, 29, 15; the cam sprocket is 10, 11, 7; the oil cap is 63, 59, 29; the water pump is 56, 58, 35; the rear oil seal is 30, 26, 13; the rocker cover and gasket are 11, 13, 5; the crankshaft is 530, 593, 241.

[0240] After optimization, the minimum beat value corresponding to the optimal completion time of a single station is 455, which is the optimal value. The beat is mainly limited by the bottleneck assembly task 20. The operation time of this task in three different assembly modes is 730, 805, and 455 respectively, that is, the task assembly time in the human-machine collaborative assembly mode is the smallest, which is the minimum and maximum assembly time of all tasks in different assembly modes. The beat value cannot be less than the task operation time, so the beat value obtained by the improved genetic algorithm is the optimal value. Among them, there are workers in 6 stations, and there are no workers in the 5th station. There are also robots in 6 stations, and there is no robot in the 7th station. Human-machine collaborative assembly can shorten the offline time of automobile engine remanufacturing and improve assembly efficiency. The circular assembly line can reduce the occupied site area.

[0241] Reference Fig. 9 Based on the same inventive concept, an embodiment of the present invention provides an automobile engine assembly line design system, including:

[0242] Acquisition module 1, used to obtain the spatial physical structure and the number of iterations;

[0243] Memory 2, used to store Figures 1 to 2 Any of the procedures for the design method of an automobile engine assembly line;

[0244] Processor 3, the program in the memory can be loaded and executed by the processor and realize the following Figures 1 to 2 Any one of the automobile engine assembly line design methods.

[0245] Based on the same inventive concept, an embodiment of the present invention provides an automobile engine assembly line design device, including a memory and a processor, the memory stores a program that can be loaded and executed by the processor. Figures 1 to 2 A computer program for the automobile engine assembly line design method according to any one of the above.

[0246] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0247] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor. Figures 1 to 2 A computer program for the automobile engine assembly line design method according to any one of the above.

[0248] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0249] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for designing an automobile engine assembly line, characterized in that: include: Obtain the spatial physical structure of a car engine; Determine the assembly tasks and the priority relationship between each assembly task according to the physical structure of the space; Calculate the assembly time of each assembly task under three assembly modes: human, robot, and human-machine collaborative; A mixed integer linear programming model for the human-machine collaborative assembly line balancing problem oriented by beat is established based on assembly time, priority relationship and the preset number of workers and robots. Optimize according to the mixed integer linear programming model to obtain the assembly line balancing solution; The assembly plan Gantt chart and the circular assembly line station layout information are formed according to the assembly line balancing plan analysis, and the assembly plan Gantt chart and the circular assembly line station layout information are sent to the terminal held by the operator.

2. The automobile engine assembly line design method according to claim 1, characterized in that: The specific steps to establish a mixed integer linear programming model include: Define parameter symbols and decision variables, including: I, j: assembly task number, the assembly task set is I, i∈I, and the maximum number of assembly tasks is |I|; w: workstation number, the workstation set is W, w∈W, the maximum number of workstations is |W|, where |W|≤|I|; s: assembly station mode, the set of which is S, s=1 represents a station operated by a person, s=2 represents a station operated by a robot, and s=3 represents a station operated by a human and a robot; m: Task assembly mode, whose set is M, m∈{1,2,3} represents the three assembly modes of human, robot, and human-machine collaboration respectively; t im : Assembly time of task i in mode m; p ij : Task priority relationship, if task i is the immediate predecessor of task j, then p ij =1, otherwise p ij =0; |W|: number of workers; |R|: number of robots; ψ1, ψ2: maximum values; T C : The beat of the assembly line (non-negative variable); x iws : Task allocation variable. If task i is assigned to workstation w in mode s, then x iws =1, otherwise x iws =0; y iw : Task allocation variable. If task i is assigned to workstation w, then y iw =1, otherwise y iw =0; z im : Task assembly mode variable. If task i is assembled using mode m, then z im =1, otherwise z im =0; π ijws : Task assembly order variable. If task i is assembled before task j in station w, then π ijws =1, otherwise π ijws =0; Station open state, if there is s station mode in station w, then otherwise The completion time of task i (non-negative variable); Set the objective function to minimize the cycle time of the assembly line, expressed as min f = T C ; Set constraints, including the following: Constraint 1: Relationship between assembly patterns for any task Constraint 2: Relationship between task assignment variables Constraint 3: Relationship between task allocation variables and assembly patterns Constraint 4: Priority constraint, the workstation of the previous task is not greater than the workstation of the next task Constraint 5: Priority constraint, the start time of the next task should not be less than the completion time of the previous task Constraint 6: Task order constraints in workstations Constraint 7: Any task needs to be assigned to a workstation Constraint 8: Workstation opening constraint Constraint 9: Workstations are opened in sequence Constraint 10: Relationship between task completion time and takt time Constraint 11: Relationship between task completion time and task assembly time Constraint 12: Worker number constraint, robot number constraint 3. The automobile engine assembly line design method according to claim 2, characterized in that: The methods for determining the assembly line balancing solution include: Input assembly task and mixed integer linear programming model; The coding sequence was determined based on the assembly task analysis; Determine the maximum completion time of a single workstation based on the coding sequence and mixed integer linear programming model analysis; Start iteration to randomly select individuals and form random values; If and only if the random value is less than the preset crossover probability value, the preset crossover operation is performed; If and only if the random value is less than the preset mutation probability value, the preset mutation operation is performed, and the crossover probability value is greater than the mutation probability value; Execute the preset neighborhood search operation; According to the number of iterations, the maximum completion time of a single workstation is selected and used as the optimal completion time of the single workstation; The assembly line plan is determined based on the optimal completion time analysis of a single workstation, and is used as the assembly line balancing plan.

4. The automobile engine assembly line design method according to claim 3, characterized in that: Methods for determining coding sequences include: Numbering the assembly tasks based on the precedence relationship to form a task number value; Construct a priority matrix based on the priority relationship; Selecting unassigned and unpredetermined assembly tasks based on the priority matrix and using the corresponding task number value as the number selection value, and updating the priority matrix; When all assembly tasks are selected, the number selection values ​​are combined in sequence to form an assembly task sequence; The assembly task based on the assembly task sequence randomly selects reference values ​​corresponding to the assembly mode to form an assembly mode sequence; The assembly task sequence and the assembly pattern sequence are combined to form a coding sequence.

5. The automobile engine assembly line design method according to claim 4, characterized in that: Crossover operations include: Based on the assembly task sequence, two exchange points are randomly selected to form a cross-task parent sequence between the two exchange points, and the assembly task sequence except the cross-task parent sequence is used as the cross-task remaining sequence, the assembly mode sequence corresponding to the cross-task parent sequence is used as the cross-mode parent sequence, and the assembly mode sequence except the cross-mode parent sequence is used as the cross-mode remaining sequence; Based on the correspondence between the cross-task parent sequence and the preset cross-task neighborhood sequence, a cross-task neighborhood sequence corresponding to the cross-task parent sequence is determined; Performing random transformation based on the cross-mode parent sequence to form a cross-mode child sequence; The cross-task neighborhood sequence is combined with the cross-task residual sequence to form a cross-task adjustment sequence, the cross-mode subsequence is combined with the cross-mode residual sequence to form a cross-mode adjustment sequence, and the cross-task adjustment sequence and the cross-mode adjustment sequence are combined as a cross-comprehensive adjustment sequence to replace and form a new coding sequence.

6. The automobile engine assembly line design method according to claim 5, characterized in that: Mutation operations include: Based on the assembly task sequence, two exchange points are randomly selected as mutation selection position points; Retrieve insertable position points based on assembly task sequence; According to the consistency between the mutation selection position points and the insertable position points, the mutation task selection sequence is determined, and the assembly mode sequence corresponding to the mutation task selection sequence is used as the mutation mode selection sequence; Based on the variant task selection sequence, the assembly task sequence is mutually exchanged and adjusted to form a variant task adjustment sequence; based on the variant mode selection sequence, the assembly mode sequence is mutually exchanged and adjusted to form a variant mode adjustment sequence; The variation task adjustment sequence and the variation mode adjustment sequence are combined as a variation comprehensive adjustment sequence to replace and form a new coding sequence.

7. The automobile engine assembly line design method according to claim 6, characterized in that: Neighborhood search operations include: Based on the assembly task sequence, a position point is randomly selected as a neighborhood selection point, and the assembly task sequence corresponding to the neighborhood selection point is used as a neighborhood task selection sequence, and the assembly mode sequence corresponding to the neighborhood selection point is used as a neighborhood mode selection sequence; Selecting a sequence according to the neighboring tasks to query the preceding and following tasks, inserting the assembly task sequence adjacently to form a neighboring task adjustment sequence, and inserting the assembly mode sequence adjacently to form a neighboring mode adjustment sequence; The neighborhood task adjustment sequence and the neighborhood pattern adjustment sequence are combined as the neighborhood comprehensive adjustment sequence to replace and form a new coding sequence.

8. An automobile engine assembly line design system, characterized in that: include: An acquisition module (1) is used to acquire the spatial physical structure and the number of iterations; A memory (2) for storing a program of a method for designing an automobile engine assembly line according to any one of claims 1 to 7; A processor (3), wherein the program in the memory can be loaded and executed by the processor and implements the automobile engine assembly line design method as claimed in any one of claims 1 to 7.

9. An automobile engine assembly line design device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and executes the method for designing an automobile engine assembly line according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the automobile engine assembly line design method according to any one of claims 1 to 7.

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