Modeling and solving method, system and device of new intelligent production line and medium

Abstract

The application discloses a kind of novel intelligent production line's modeling and solving method, system, device and medium, belong to intelligent manufacturing, production line modeling and solving technical field.The method includes: determining the solving target of production line model and hypothesis constraint condition;Based on the solving target and hypothesis constraint condition, establish production line mathematical model;According to production line mathematical model, construct directed graph model, to solve topological sorting sequence;According to the order recorded in topological sorting result set, substitute into production line mathematical model, solve and obtain the required performance index.The application establishes mathematical model and directed graph model, accurately describes the constraint condition and solving target of intelligent production line;Topological sorting method is introduced to solve traversal order, improve solving efficiency, and calculate the required performance index accordingly.The application comprehensively considers multiple resources in intelligent production line, can output target result in a short time, can effectively improve the overall benefit of enterprise.

Description

Technical Field

[0001] This invention relates to the fields of intelligent manufacturing, production line modeling and solving technology, and in particular to a novel intelligent production line modeling and solving method, system, device and medium. Background Technology

[0002] With the rise and rapid development of the intelligent manufacturing concept, and facing the increasing personalized demands of consumers and the market's need for mass production of high-performance products, the development of flexible intelligent production lines has become a key strategy for many enterprises to maintain competitiveness across various industries. Against this backdrop, in response to market trends such as product diversification and rapid delivery, new intelligent production lines have attracted significant attention and importance both within and outside the industry due to their unique advantages. However, significant challenges remain in system modeling and solution methods for these production lines. Currently, there is a lack of technical solutions to accurately and quickly calculate the required performance indicators based on specific production order inputs, providing guidance for actual production and maximizing production efficiency and capacity utilization. Summary of the Invention

[0003] In order to at least partially solve one of the technical problems existing in the prior art, the purpose of this invention is to provide a new modeling and solving method, system, device and medium for intelligent production lines.

[0004] The technical solution adopted in this invention is:

[0005] A modeling and solution method for a novel intelligent production line, the novel intelligent production line including a circular transport track, an electrical and control system, several movers and multiple processing machines, the movers can run at high speed along a fixed direction on the circular transport track to transport finished products and semi-finished products in the production process, and cooperate with the processing machines to complete the assembly line operation;

[0006] The modeling and solution method includes the following steps:

[0007] Determine the solution objective and assumed constraints for the production line model;

[0008] Based on the aforementioned solution objective and assumed constraints, a mathematical model of the production line is established;

[0009] A directed graph model is constructed based on the mathematical model of the production line to solve the topological sorting sequence, and the mathematical model is solved accordingly.

[0010] Substitute the records in the topological sorting result set into the mathematical model of the production line to solve for the required performance indicators.

[0011] Furthermore, the novel intelligent production line has three characteristics:

[0012] First, the production line is circular;

[0013] Secondly, it uses moving parts for transportation. There is a fixed number of moving parts on the track for transporting workpieces, which makes the processing of workpieces on the production line constrained by transportation resources.

[0014] Third, the buffer capacity is limited, meaning the buffer size between machines is limited, allowing only a few workpieces to remain at the same time.

[0015] Furthermore, determining the solution objective and constraints of the production line model includes:

[0016] The maximum completion time is taken as the solution objective;

[0017] The following assumptions and constraints are established:

[0018] (1) Each workpiece is available at time zero, and the processing start time of each workpiece is independent of each other;

[0019] (2) The process route for each workpiece is the same, and the processing sequence on each machine is also the same;

[0020] (3) At any given time, a workpiece can be processed by a single machine. The workpiece must complete the preceding process before it can be processed. Once the processing task has started, it cannot be interrupted.

[0021] (4) There is a capacity of b between the machines. j The buffer is used for processing, and the processing time on the machine in the buffer is 0. When the buffer is full, the workpiece will be blocked on the current processing machine.

[0022] (5) The transport of workpieces between machines must be carried out by a moving part, and the movement of the moving part is constrained by a circular transport track.

[0023] (6) The moving part only engages and separates from the workpiece during loading and unloading. The moving part and the workpiece will not separate during the processing. The transportation time of the moving part between machines is negligible.

[0024] (7) The number of movers k is a fixed value, determined before processing begins. The number of movers remains constant during processing, and to ensure smooth system operation, the number of movers must be less than the number of machines m and the buffer capacity b. j The sum

[0025] Furthermore, the established mathematical model for the production line is as follows:

[0026] C π(1),1 =P π(1),1

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] n i,m =i+kb m -1

[0033] C max =C π(n),m

[0034] In the formula, n is the total number of workpieces to be processed; m is the total number of processing machines; k is the total number of moving parts; π = {π(1), π(2), ..., π(n)} is the feasible workpiece processing sequence, and π(i) represents the i-th workpiece to be processed with the number π(i); b j P is the buffer capacity between machine j and j+1; π(i),j C represents the processing time of workpiece π(i) on machine j; π(i),j Let n be the completion time of workpiece π(i) on machine j; 1,j The processing of workpiece 1 on the j-th machine is constrained by the circular production line; n i,m The processing of workpiece i on the m-th machine is constrained by the circular production line; C max The maximum completion time.

[0035] Furthermore, the directed graph model is as follows:

[0036] G = (N, Q)

[0037] N = M × J

[0038] Q = Q V ∪Q H ∪Q S ∪Q K ∪Q N1 ∪Q N2

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, G is a directed graph model; N is a vertex set representing the machining operation O(π(i),j) of workpiece π(i) on machine j; Q is a set of connected arcs representing machining constraints; Q V Indicates workpiece machining sequence constraints; Q H Indicates machining machine constraints; Q S Indicates buffer constraints; Q K Indicates the mover quantity constraint; Q N1 Q N2 This indicates a circular constraint.

[0046] Furthermore, the process of solving the topological sort sequence includes:

[0047] A1. Establish a directed graph model of the production line and record the in-degree of each vertex; the in-degree is the sum of the number of times the current vertex is the endpoint of an edge in the directed graph model.

[0048] A2. Select a vertex O(π(i),j) with an in-degree of 0 from the directed graph model and output it to the result set R;

[0049] A3. Delete the vertex mentioned in step A2 and all connecting arcs originating from that vertex, and reduce the in-degree of the vertex pointed to by each connecting arc by one accordingly;

[0050] A4. Repeat steps A2-A3 until there are no vertices left in the directed graph model;

[0051] A5. Use the obtained result set R as a topological sorting sequence.

[0052] Furthermore, the process of substituting the records in the topological sorting result set into the production line mathematical model to solve for the required performance indicators includes:

[0053] Following the order of the processing operations O(π(i),j) recorded in the topological sorting result set R, they are sequentially substituted into the mathematical model of the production line for solution, and the maximum completion time C is obtained. max As the solution objective of the model.

[0054] Another technical solution adopted in this invention is:

[0055] A modeling and solution system for a novel intelligent production line, the novel intelligent production line includes a circular transport track, an electrical and control system, several movers and multiple processing machines. The movers can run at high speed along a fixed direction on the circular transport track to transport finished products and semi-finished products in the production process, and cooperate with the processing machines to complete the assembly line operation.

[0056] The modeling and solving system includes:

[0057] The objective determination module is used to determine the solution objective and the assumed constraints of the production line model;

[0058] The model building module is used to establish a mathematical model of the production line based on the solution objective and the assumed constraints.

[0059] The topology solving module is used to construct a directed graph model based on the production line mathematical model in order to solve the topological sorting sequence and thus solve the mathematical model.

[0060] The model solving module is used to input the production line mathematical model according to the order of the records in the topological sorting result set, and solve for the required performance indicators.

[0061] Another technical solution adopted in this invention is:

[0062] A novel modeling and solving device for intelligent production lines includes:

[0063] At least one processor;

[0064] At least one memory for storing at least one program;

[0065] When the at least one program is executed by the at least one processor, the at least one processor performs the method as described above.

[0066] Another technical solution adopted in this invention is:

[0067] A computer-readable storage medium storing a processor-executable program, which, when executed by a processor, performs the method described above.

[0068] The beneficial effects of this invention are as follows: By establishing a mathematical model and a directed graph model, this invention accurately describes the constraints and solution objectives of the intelligent production line; it introduces a topological sorting method to solve the traversal order, improving solution efficiency, and thereby calculating the required performance indicators. This invention comprehensively considers various resources in the intelligent production line, enabling it to output target results in a short time. It can effectively help enterprises predict key performance indicators such as production cycle time and completion time, thereby reducing production costs and improving overall efficiency. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic diagram illustrating the steps of the modeling and solving method for the novel intelligent production line in an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of the novel intelligent production line in an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of an example of a directed graph model of a novel intelligent production line in an embodiment of the present invention. Detailed Implementation

[0073] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0074] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0075] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0076] Furthermore, in the description of this invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0077] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0078] To address the lack of specific modeling guidance in existing new intelligent production lines, this invention provides a modeling and solution method for a new type of intelligent production line. It considers possible circular constraints, transportation resource constraints, and buffer constraints in the production line, making the model more realistic and the prediction of performance indicators more accurate. At the same time, it introduces a directed graph model and topology sorting algorithm to ensure that the model can be correctly traversed and solved, thus providing a guarantee for enterprises to make full use of limited production resources and improve production efficiency.

[0079] See Figure 2 This invention provides a novel intelligent production line, comprising a circular transport track composed of multiple straight and arc modules, a number of moving parts, and corresponding electrical and control systems, processing machines, etc. The moving parts can run at high speed along a fixed direction on the transport track. Positioning and clamping devices can be installed on the moving parts to transport finished and semi-finished products during the production process, cooperating with robotic arms and production equipment to complete assembly line operations.

[0080] The new intelligent production line has three main characteristics that distinguish it from ordinary production lines: First, the production line is circular. Unlike ordinary assembly lines, this production line has transport tracks that connect the beginning and end of the line, resulting in a very compact layout. Second, it uses movers for transportation. There are a fixed number of movers on the tracks for transporting workpieces, which makes the processing of workpieces on the production line constrained by transportation resources. Third, the buffer capacity is limited. That is, the buffer between machines is limited in size, allowing only a few workpieces to stay at the same time.

[0081] like Figure 1 As shown, based on the aforementioned novel intelligent production line, this embodiment provides a method for modeling and solving a production line, including the following steps:

[0082] S1. Determine the solution objective and constraints.

[0083] To establish a mathematical model of the production line, it is necessary to first clarify the specific performance indicators for model solving and consider the guiding significance for the actual production process. Here, the most commonly used maximum completion time is selected as the solution objective.

[0084] To make the model more general, concise, and accurate, it is necessary to analyze actual production scenarios and establish appropriate constraints. The constraints and assumptions established in this invention for a circular intelligent production line with a transport device and an intermediate buffer zone are as follows:

[0085] (1) Each workpiece is available at time zero, and the processing start time of each workpiece is independent of each other.

[0086] (2) The process route for each workpiece is the same, and the processing sequence on each machine is also the same.

[0087] (3) At any given time, a workpiece can be processed by at most one machine. The workpiece must complete the preceding process before it can be processed. Once the processing task has started, it is not allowed to be interrupted.

[0088] (4) There is a capacity of b between the machines. j The buffer is used for processing. The processing time of the machine on the buffer is 0. When the buffer is full, the workpiece will be blocked on the current processing machine.

[0089] (5) The transport of workpieces between machines must be carried out by a moving part, and the movement of the moving part is constrained by a circular track.

[0090] (6) The moving part only engages and separates from the workpiece during loading and unloading. The moving part and the workpiece will not separate during the processing. The transportation time of the moving part between machines is negligible.

[0091] (7) The number of movers k is a fixed value, determined before processing begins. The number of movers remains constant during processing, and to ensure smooth system operation, the number of movers must be less than the number of machines m and the buffer capacity b. j The sum

[0092] S2. Establish a mathematical model for the production line.

[0093] Based on the above solution objectives and constraints, the production line mathematical model is established as follows:

[0094] C π(1),1 =P π(1),1 (2-1)

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] n i,m =i+kb m -1,1 <n i,m ≤n (2-7)

[0101] C max =C π(n),m (2-8)

[0102] In equations (2-2) to (2-5), when a < 1 or b < 1, C π(a),b =0.

[0103] In the formula, n is the total number of workpieces to be processed; m is the total number of processing machines; k is the total number of moving parts; π = {π(1), π(2), ..., π(b)} is the feasible workpiece processing sequence, π(i) represents the i-th workpiece to be processed with the number π(i), 1 ≤ i ≤ n; b j Let b be the buffer capacity between machines j and j+1, 1≤j≤m, where b m P is the buffer size between machine m and machine 1; π(i),j C represents the processing time of workpiece π(i) on machine j; π(i),j Let n be the completion time of workpiece π(i) on machine j; 1,j The processing of workpiece 1 on the j-th machine is constrained by the circular production line; n i,m The processing of workpiece i on the m-th machine is constrained by the circular production line; C max The maximum completion time.

[0104] Specifically, Equation (2-1) indicates that the completion time of the first workpiece on the first machine is only affected by its own processing time. Equation (2-2) indicates that the processing time of the first workpiece on the j-th machine needs to consider its own completion time on the previous machine, as well as the n-th time under the influence of the cyclic constraint. 1,j The completion time of the i-th workpiece on the first machine. Equation (2-3) represents the completion time of the i-th workpiece on the m-th machine, i.e., the last machine. It is necessary to consider the completion time of the i-th workpiece on the previous machine, the completion time of the previous workpiece on the current machine, and the completion time of the n-th workpiece under the influence of the cyclic constraint. i,m Equation (2-4) indicates that to solve for the completion time of the i-th workpiece on the first machine, it is necessary to simultaneously consider the completion time of the previous workpiece on the current machine, the completion time of the (ik)-th workpiece on the m-th machine under the influence of the mover number constraint, and the completion time of the (i-b1-1)-th workpiece on the next machine under the influence of the current machine buffer size constraint. Equation (2-5) indicates that when i and j are not within the range of values ​​of equations (2-1) to (2-4), solving for the completion time of the i-th workpiece on the j-th machine only requires considering its own completion time on the previous machine, the completion time of the previous workpiece on the current machine, and the completion time of the (ib)-th workpiece under the influence of the buffer constraint. j -1) The completion time of each workpiece on the next machine. Equations (2-6) and (2-7) are used to solve for the above n. 1,j n i,m The value of . Equation (2-8) represents the maximum completion time C of the objective solved by the model. max That is, the completion time C of the nth workpiece on the mth machine. π(n),m .

[0105] S3. Construct a directed graph model.

[0106] Because of the circular constraints in the production line, calculating the completion time of the current workpiece on the last or several machines requires first calculating the completion time of the subsequent workpiece on the first machine. Conversely, the mover constraint requires first calculating the completion time of the preceding workpiece on the last machine before calculating the completion time of the current workpiece on the first machine. Furthermore, the completion time calculation regions affected by these two constraints overlap to some extent, making it impossible to solve the problem in the normal order of traversing machines first and then workpieces. Therefore, a Directed Acyclic Graph (DAG) model of the production line mathematical model was established to determine the correct traversal order.

[0107] Based on the above requirements, the DAG model is established as follows:

[0108] G = (N, Q) (3-1)

[0109] N = M × J (3-2)

[0110] Q = Q V ∪Q H ∪Q S ∪Q K ∪Q N1 ∪Q N2 (3-3)

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] In the formula, G is a directed graph model; N is a vertex set representing the machining operation O(π(i),j) of workpiece π(i) on machine j; Q is a set of connected arcs representing machining constraints; Q V Indicates workpiece machining sequence constraints; Q H Indicates machining machine constraints; Q S Indicates buffer constraints; Q K Indicates the mover quantity constraint; Q N1 Q N2 This indicates a circular constraint.

[0118] In the DAG model, the meanings of the various equations are the same as those in the production line mathematical model described above, and will not be repeated here.

[0119] S4. Solve for the topological sort sequence.

[0120] Based on the properties of the DAG model, since its graphical model does not contain cycles, topological sorting can be performed. The specific steps are as follows:

[0121] S41. Establish a DAG model of the production line and record the in-degree of each vertex. The in-degree is the sum of the number of times the current vertex is the endpoint of an edge in the DAG.

[0122] S42. Select a vertex O(π(i),j) with an in-degree of 0 from the DAG and output it to the result set R.

[0123] S43. Delete the vertex mentioned in step S42 and all connecting arcs originating from that vertex, and reduce the in-degree of the vertex pointed to by each connecting arc by one accordingly.

[0124] S44. Repeat steps S42-S43 until there are no vertices left in the DAG.

[0125] S45. The resulting set R is the topological sort sequence.

[0126] S5. Substitute the mathematical model to solve the problem.

[0127] Following the order of the processing operations O(π(i),j) recorded in the topological sorting result set R, substitute them sequentially into equations (2-1) to (2-8) to solve for the maximum completion time C. max This is the objective of solving the model.

[0128] The above method will be explained in detail below with reference to specific embodiments.

[0129] This embodiment provides a novel modeling and solution method for intelligent production lines. The specific operation steps are as follows:

[0130] Step 1: Determine the solution objective and constraints.

[0131] Select the maximum completion time C max The constraints and assumptions established as the solution objective are as follows:

[0132] (1) Each workpiece is available at time zero, and the processing start time of each workpiece is independent of each other.

[0133] (2) The process route for each workpiece is the same, and the processing sequence on each machine is also the same.

[0134] (3) At any given time, a workpiece can be processed by at most one machine. The workpiece must complete the preceding process before it can be processed. Once the processing task has started, it is not allowed to be interrupted.

[0135] (4) There is a capacity of b between the machines. j The buffer is used for processing. The processing time of the machine on the buffer is 0. When the buffer is full, the workpiece will be blocked on the current processing machine.

[0136] (5) The transport of workpieces between machines must be carried out by a moving part, and the movement of the moving part is constrained by a circular track.

[0137] (6) The moving part only engages and separates from the workpiece during loading and unloading. The moving part and the workpiece will not separate during the processing. The transportation time of the moving part between machines is negligible.

[0138] (7) The number of movers k is a fixed value, determined before processing begins. The number of movers remains constant during processing, and to ensure smooth system operation, the number of movers must be less than the number of machines m and the buffer capacity b. j The sum

[0139] To illustrate the solution steps more clearly, a specific example is used here:

[0140] There are n = 10 workpieces being processed on m = 5 machines. K = 6 movers are used to transport the workpieces. The workpieces are numbered from 1 to 10, and the processing order is π = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}. The processing time P for each workpiece on each machine is... π(i),j As shown in Table 1, due to the circular layout of the production line and limited space, there is a fixed buffer capacity between machines: b1=3, b2=2, b3=1, b4=0, b5=1. The relationship between the number of moving parts and machines, and the buffer capacity, satisfies...

[0141] Table 1. Workpiece Machining Time Schedule (Unit: seconds)

[0142] 1 54 83 15 71 77 36 53 38 27 87 2 79 3 11 99 56 70 99 60 5 56 3 16 89 49 15 89 45 60 23 57 64 4 66 58 31 68 78 91 13 59 49 85 5 58 56 20 85 53 35 53 41 69 13

[0143] Step 2: Establish a mathematical model for the production line.

[0144] Based on the above solution objectives and constraints, the production line mathematical model is established as follows:

[0145] C π(1),1 =P π(1),1 (2-1)

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] n i,m =i+kb m -1,1 <n i,m ≤n (2-7)

[0152] C max =C π(n),m (2-8)

[0153] Step 3: Construct a directed graph model.

[0154] The DAG model is established as follows:

[0155] G = (N, Q) (3-1)

[0156] N = M × J (3-2)

[0157] Q = Q V ∪Q H ∪Q S ∪Q K ∪Q N1 ∪Q N2 (3-3)

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] The DAG model established in this example is as follows: Figure 3 As shown.

[0165] Step 4: Solve for the topological sort sequence.

[0166] The DAG model established for this example is topologically sorted, and the specific steps are as follows:

[0167] (1) Establish an adjacency list based on the DAG model to represent the connection relationship between vertices, as shown in Table 2, and record the in-degree of each vertex, as shown in Table 3.

[0168] (2) Select a vertex O(π(i),j) with an in-degree of 0 from the DAG and output it to the result set R.

[0169] (3) Delete the vertex mentioned in step (2) and all connecting arcs originating from that vertex, and reduce the in-degree of each vertex pointed to by each connecting arc by one.

[0170] (4) Repeat steps (2)-(3) above until there are no vertices in the DAG.

[0171] (5) The resulting set R is the topological sort sequence. The topological sort sequence obtained in this example is:

[0172] R={(1,1),(1,2),(2,1),(2,2),(3,1),(1,3),(3,2),(4,1),(2,3),(4,2),(5,1),(1,4),(5 ,2),(6,1),(1,5),(3,3),(2,4),(7,1),(6,2),(2,5),(4,3),(3,4),(8,1),(7,2),(3,5),( 5,3),(4,4),(9,1),(8,2),(4,5),(6,3),(5,4),(10,1),(9,2),(5,5),(7,3),(6,4),(10,2),(6,5),(8,3),(7,4),(7,5),(9,3),(8,4),(8,5),(10,3),(9,4),(9,5),(10,4),(10,5)}

[0173] Table 2 Adjacency List

[0174]

[0175] Note: In Table 2, (i,j) represents the connecting arc that starts at the current vertex and ends at (i,j).

[0176] Table 3 In-degree table

[0177]

[0178]

[0179] Note: The data in Table 3 represents the in-degree value of the current vertex.

[0180] Step 5: Substitute the mathematical model to solve.

[0181] Following the order of the processing operations O(π(i),j) recorded in the topological sorting result set R, substitute them sequentially into equations (2-1) to (2-8) to solve for the maximum completion time C. max This is the objective of solving the model.

[0182] The completion times of each workpiece calculated in this example are shown in Table 4.

[0183] Table 4. Workpiece Completion Time Schedule (Unit: seconds)

[0184] 1 54 137 152 223 300 336 389 427 454 596 2 133 140 163 322 378 448 547 607 612 668 3 149 238 287 337 467 512 607 636 693 757 4 223 300 356 424 545 636 671 730 779 864 5 300 356 389 509 598 671 724 771 848 877

[0185] Based on the above steps, the completion time of each workpiece on each machine can be calculated, as well as the target maximum completion time C. max =C π(10),5 =877s.

[0186] In summary, this invention proposes a novel modeling and solution method for intelligent production lines with transportation and buffer resources. A mathematical model and a directed graph model are established, accurately describing the constraints and solution objectives of the intelligent production line. Due to the complexity of model traversal, a topological sorting method is introduced to solve for the traversal order, improving solution efficiency and calculating the required performance indicators accordingly. This method comprehensively considers various resources in the intelligent production line, enabling the output of target results in a short time. It can effectively help enterprises predict key performance indicators such as production cycle time and completion time, thereby reducing production costs and improving overall efficiency.

[0187] This embodiment also provides a modeling and solution system for a new type of intelligent production line. The new intelligent production line includes a circular transport track, an electrical and control system, several movers and multiple processing machines. The movers can run at high speed along a fixed direction on the circular transport track to transport finished products and semi-finished products in the production process, and cooperate with the processing machines to complete the assembly line operation.

[0188] The modeling and solving system includes:

[0189] The objective determination module is used to determine the solution objective and the assumed constraints of the production line model;

[0190] The model building module is used to establish a mathematical model of the production line based on the solution objective and the assumed constraints.

[0191] The topology solving module is used to construct a directed graph model based on the production line mathematical model in order to solve the topological sorting sequence and thus solve the mathematical model.

[0192] The model solving module is used to input the production line mathematical model according to the order of the records in the topological sorting result set, and solve for the required performance indicators.

[0193] This embodiment of the novel intelligent production line modeling and solving system can execute the novel intelligent production line modeling and solving method provided in the method embodiment of the present invention, and can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0194] This embodiment also provides a novel modeling and solving device for intelligent production lines, including:

[0195] At least one processor;

[0196] At least one memory for storing at least one program;

[0197] When the at least one program is executed by the at least one processor, the at least one processor performs the following: Figure 1 The method shown.

[0198] This embodiment provides a modeling and solving device for a novel intelligent production line. It can execute the modeling and solving method for a novel intelligent production line provided in the method embodiment of the present invention, and can execute any combination of implementation steps of the method embodiment. It has the corresponding functions and beneficial effects of the method.

[0199] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform... Figure 1 The method shown.

[0200] This embodiment also provides a storage medium storing instructions or programs that can execute the modeling and solving method for a novel intelligent production line provided by the method embodiment of the present invention. When the instructions or programs are run, any combination of implementation steps of the method embodiment can be executed, and the method has the corresponding functions and beneficial effects.

[0201] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0202] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0203] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0204] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0205] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0206] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0207] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0208] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0209] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A novel modeling and solution method for intelligent production lines, characterized in that, The new intelligent production line includes a circular transport track, an electrical and control system, several movers, and multiple processing machines. The movers can run at high speed along a fixed direction on the circular transport track to transport finished and semi-finished products in the production process, and cooperate with the processing machines to complete the assembly line operation. The modeling and solution method includes the following steps: Determine the solution objective and assumed constraints for the production line model; Based on the aforementioned solution objective and assumed constraints, a mathematical model of the production line is established; A directed graph model is constructed based on the mathematical model of the production line to solve the topological sorting sequence; Substitute the records in the topological sorting result set into the mathematical model of the production line to solve for the required performance indicators. The established mathematical model for the production line is as follows: In the formula, The total number of workpieces to be processed; This represents the total number of processing machines; The total number of movers; A feasible workpiece processing sequence, Representative number is The One workpiece to be processed; For machines and Buffer capacity between; For workpiece In the machine Processing time; For workpiece In the machine The completion time on the document; For workpiece 1 in the first The processing on the machine is constrained by the circular production line; For workpiece In the The processing on the machine is constrained by the circular production line; The maximum completion time.

2. The modeling and solution method for a novel intelligent production line according to claim 1, characterized in that, The new intelligent production line has three characteristics: First, the production line is circular; Secondly, it uses moving parts for transportation. There is a fixed number of moving parts on the track for transporting workpieces, which makes the processing of workpieces on the production line constrained by transportation resources. Third, the buffer capacity is limited, meaning the buffer size between machines is limited, allowing only a few workpieces to remain at the same time.

3. The modeling and solution method for a novel intelligent production line according to claim 1, characterized in that, The determination of the solution objective and constraints for the production line model includes: The maximum completion time is taken as the solution objective; The following assumptions and constraints are established: (1) Each workpiece is available at time zero, and the processing start time of each workpiece is independent of each other; (2) The process route for each workpiece is the same, and the processing sequence on each machine is also the same; (3) At any given time, a workpiece can be processed by a single machine. The workpiece must complete the preceding process before it can be processed. Once the processing task has started, it cannot be interrupted. (4) The capacity between machines is The buffer is used for processing, and the processing time on the machine in the buffer is 0. When the buffer is full, the workpiece will be blocked on the current processing machine. (5) The transport of workpieces between machines must be carried out by a moving part, and the movement of the moving part is constrained by a circular transport track; (6) The moving part only engages and separates from the workpiece during loading and unloading. The moving part and the workpiece will not separate during the processing. The transportation time of the moving part between machines is negligible. (7) Number of movers The number of moving parts is a fixed value, determined before processing begins. During processing, the number of moving parts remains constant, and to ensure smooth system operation, the number of moving parts must be less than the number of machines. and buffer capacity The sum of .

4. The modeling and solution method for a novel intelligent production line according to claim 1, characterized in that, The directed graph model is as follows: In the formula, It is a directed graph model; For vertex set, vertex Indicates workpiece In the machine On the processing operations; Connecting arc sets represents processing constraints; Indicates workpiece processing sequence constraints; Indicates constraints on the processing machine; Indicates buffer constraints; Indicates the constraint on the number of movers; , This indicates a circular constraint.

5. The modeling and solution method for a novel intelligent production line according to claim 4, characterized in that, The process of solving the topological sort sequence includes: A1. Establish a directed graph model of the production line and record the in-degree of each vertex; the in-degree is the sum of the number of times the current vertex is the endpoint of an edge in the directed graph model. A2. Select a vertex with an in-degree of 0 from the directed graph model. Output to the result set ; A3. Delete the vertex mentioned in step A2 and all connecting arcs originating from that vertex, and reduce the in-degree of the vertex pointed to by each connecting arc by one accordingly; A4. Repeat steps A2-A3 until there are no vertices left in the directed graph model; A5. Obtain the result set As a topologically sorted sequence.

6. The modeling and solution method for a novel intelligent production line according to claim 5, characterized in that, The process involves substituting the records from the topological sorting result set into the mathematical model of the production line to solve for the required performance indicators, including: Sort the result set by topology Vertex of the processing operation recorded in The order of substitutions is used to solve the mathematical model of the production line, and the maximum completion time is obtained. As the solution objective of the model.

7. A novel modeling and solution system for intelligent production lines, characterized in that, The new intelligent production line includes a circular transport track, an electrical and control system, several movers, and multiple processing machines. The movers can run at high speed along a fixed direction on the circular transport track to transport finished and semi-finished products in the production process, and cooperate with the processing machines to complete the assembly line operation. The modeling and solving system includes: The objective determination module is used to determine the solution objective and the assumed constraints of the production line model; The model building module is used to establish a mathematical model of the production line based on the solution objective and the assumed constraints. The topology solver module is used to construct a directed graph model based on the mathematical model of the production line in order to solve the topological sorting sequence. The model solving module is used to substitute the records in the topological sorting result set into the production line mathematical model to solve for the required performance indicators. The established mathematical model for the production line is as follows: In the formula, The total number of workpieces to be processed; This represents the total number of processing machines; The total number of movers; A feasible workpiece processing sequence, Representative number is The One workpiece to be processed; For machines and Buffer capacity between; For workpiece In the machine Processing time; For workpiece In the machine The completion time on the document; For workpiece 1 in the first The processing on the machine is constrained by the circular production line; For workpiece In the The processing on the machine is constrained by the circular production line; The maximum completion time.

8. A novel modeling and solving device for intelligent production lines, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1-6.

9. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform the method as described in any one of claims 1-6.

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