CN116520787A - Production line optimization method and system and electronic equipment - Google Patents
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Abstract
The invention relates to the technical field of production management and discloses a production line optimization method, a system and electronic equipment.
Description
Technical Field
The present invention relates to the field of production management technologies, and in particular, to a method and a system for optimizing a production line, and an electronic device.
Background
With the increasing degree of automation of the manufacturing industry, an automatic production line has become a core technology of the manufacturing industry, and technicians have summarized a set of complete linear theory for designing, integrating and debugging the automatic production line according to related experience, so as to solve the distribution planning of the capacity path of the automatic production line and summarize the advantages and disadvantages of the automatic production line, thereby providing an improvement scheme and capacity optimization for the production line.
However, because the manufacturing technology of the industries such as automobiles, airplanes and the like is complex, the automatic production line in the industry is complex, the existing topological graph of the production line is not good at expressing the event relationship among the processes, linear reference and linear constraint cannot be provided for the linear theory of the automatic production line, the automatic production line is analyzed by the linear theory to be complex, and the obtained analysis result is inaccurate, so that the optimization effect of the automatic production line cannot reach the industry requirement.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
In view of the above-mentioned shortcomings of the prior art, the invention discloses a production line optimization method, a system and electronic equipment, so as to improve the optimization effect on an automatic production line.
The invention provides a production line optimization method, which comprises the following steps: acquiring an original production line, wherein the original production line comprises a plurality of process nodes and a production sequence among the process nodes; establishing a node relation matrix according to the production sequence among the process nodes, wherein the node relation matrix is used for representing the node adjacent relation among the process nodes; acquiring a plurality of mark distribution schemes meeting preset limiting conditions according to the node relation matrix, and determining a target scheme from the mark distribution schemes according to the number of mark adoption, wherein the mark distribution schemes are obtained by respectively distributing a node mark to each process node from a plurality of preset marks, the preset limiting conditions comprise that adjacent process nodes do not have the same node mark, and the number of mark adoption is the number of preset marks adopted by the mark distribution schemes; and respectively determining the set labels of all the production line nodes according to the node identifiers of the target schemes, and establishing a linear model according to the set labels so as to optimize all the process nodes in the original production line according to the linear model.
Optionally, establishing a node relation matrix according to the production sequence among the process nodes, including: generating edge data for representing a current node to a child node according to the production sequence, wherein the current node and the child node are both process nodes; and establishing a node relation matrix according to the edge data, wherein matrix elements in the node relation matrix are determined according to whether edge data exist among the process nodes.
Optionally, a node relation matrix is established according to the edge data by the following formula:wherein A is a node relation matrix, a ij E, matrix elements of the ith row and the jth column in the node relation matrix ij For the edge data from the ith process node to the jth process node, N is the number of process nodes.
Optionally, acquiring, according to the node relation matrix, multiple identifier allocation schemes meeting preset constraint conditions, including: the preset marks are of different colors; dyeing any process node by taking one color in the preset mark as a node mark, and determining the process node as a current node; responding to a current node, determining adjacent nodes of the current node from the process nodes according to the node relation matrix, and determining node identifiers of the adjacent nodes from the preset identifiers according to the preset limiting conditions so as to dye the adjacent nodes; if all adjacent nodes of the current node are dyed, determining a new current node from the adjacent nodes; and if all the process nodes are dyed, generating an identification allocation scheme according to the node identification of each process node.
Optionally, the preset constraints are characterized by the following formula:wherein M is the number of preset marks, x i Node identification, x, for the ith process node p Node identification, x, for the p-th process node q Node identification, a, for the q-th process node pi A is the matrix element of the ith row and the ith column in the node relation matrix iq For the matrix elements of the ith row and the qth column in the node relation matrix, N is the number of process nodes and Z + Representing a positive integer.
Optionally, determining a target scheme from the identifier allocation schemes according to the number of identifier adoption includes: if the number of the identifiers of the identifier allocation scheme is equal to 2, determining the identifier allocation scheme as a target scheme; and if the number of the identifiers of the identifier allocation scheme is not equal to 2, discarding the identifier allocation scheme.
Optionally, building a linear model according to the set of labels to optimize each process node in the original production line according to the linear model, including: acquiring an original capacity threshold corresponding to each process node, and acquiring a capacity plan of the original production line, wherein the capacity plan comprises raw material data, a capacity target and capacity constraint conditions; establishing a linear model according to at least one of the original capacity threshold, the set label, the capacity target and the capacity constraint condition to obtain a capacity model corresponding to the original production line; inputting the raw material data into the productivity model to simulate the original production line based on the linear constraint through the productivity model to obtain a simulation result, and adjusting the original productivity threshold and/or the production sequence according to the simulation result until the simulation result meets a preset first productivity requirement.
Optionally, building a linear model according to the set of labels to optimize each process node in the original production line according to the linear model, including: the process node comprises a production line input node and a production line output node; acquiring raw material data and a capacity model of the original production line; layering all process nodes in the original production line according to the set labels and the production sequence to obtain a plurality of production layers arranged according to the production sequence, and establishing linear constraint based on the process nodes in the production layers; inputting the raw material data into a line input node of the capacity model to simulate the original line based on the linear constraints by the capacity model and to determine capacity data for each of the process nodes; and adjusting the production sequence among the associated nodes according to the capacity data until the capacity data of the output nodes of the production line meet the preset second capacity requirement, wherein the associated nodes are two different process nodes and are respectively existing in adjacent node layers.
The invention provides a production line optimization system, which comprises: the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an original production line, and the original production line comprises a plurality of process nodes and production sequences among the process nodes; the building module is used for building a node relation matrix according to the production sequence among the process nodes, wherein the node relation matrix is used for representing the node adjacent relation among the process nodes; the determining module is used for obtaining a plurality of mark distribution schemes meeting preset limiting conditions according to the node relation matrix, and determining a target scheme from the mark distribution schemes according to the number of mark adoption, wherein the mark distribution schemes are obtained by respectively distributing a node mark to each process node from a plurality of preset marks, the preset limiting conditions comprise that adjacent process nodes do not have the same node mark, and the number of mark adoption is the number of preset marks adopted by the mark distribution schemes; and the optimization module is used for respectively determining the set labels of all the production line nodes according to the node identifiers of the target scheme, and establishing a linear model according to the set labels so as to optimize all the process nodes in the original production line according to the linear model.
The invention provides an electronic device, comprising: a processor and a memory; the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory so as to enable the electronic equipment to execute the method.
The invention has the beneficial effects that:
the method comprises the steps of establishing a node relation matrix through a production sequence among process nodes in an original production line, respectively distributing node identifiers to the process nodes from a plurality of preset identifiers based on the node relation matrix, enabling adjacent process nodes not to have the same node identifiers, obtaining a plurality of identifier distribution schemes, determining a target scheme from the identifier distribution schemes according to the number of the identifiers, respectively determining an aggregate label of the process nodes in the original production line according to the node identifiers of the target scheme, and establishing a linear model according to the aggregate label so as to optimize the process nodes in the original production line according to the linear model. Therefore, the production sequence among all process nodes in the original production line is converted from the mixed programming solving problem to the linear programming problem for solving, so that linear reference and linear constraint are provided for the linear theory of the automatic production line, the analysis accuracy of the linear theory on the automatic production line is improved, and the optimization effect of the automatic production line is improved.
Drawings
FIG. 1 is a schematic flow chart of a process line optimization method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a topology of an original production line in accordance with an embodiment of the present invention;
FIG. 3 is a flowchart of a method for obtaining an identifier assignment scheme according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a line optimization system in accordance with an embodiment of the present invention;
fig. 5 is a schematic diagram of an electronic device in an embodiment of the invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that, without conflict, the following embodiments and sub-samples in the embodiments may be combined with each other.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, it will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise indicated.
In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
Referring to fig. 1, an embodiment of the present disclosure provides a method for optimizing a production line, including:
step S101, an original production line is obtained;
the original production line comprises a plurality of process nodes and a production sequence among the process nodes;
step S102, a node relation matrix is established according to the production sequence among process nodes;
the node relation matrix is used for representing node adjacent relations among process nodes;
step S103, obtaining a plurality of mark distribution schemes meeting preset limiting conditions according to the node relation matrix, and determining a target scheme from the mark distribution schemes according to the number of the adopted marks;
the identifier allocation scheme is obtained by respectively allocating a node identifier to each process node from a plurality of preset identifiers;
the preset limiting conditions comprise that adjacent process nodes do not have the same node identification;
the number of the marks is the number of preset marks adopted by a mark distribution scheme;
step S104, respectively determining the set labels of all the production line nodes according to the node identifiers of the target schemes, and establishing a linear model according to the set labels so as to optimize all the process nodes in the original production line according to the linear model.
By adopting the production line optimization method provided by the embodiment of the disclosure, the node relation matrix is established through the production sequence among all process nodes in the original production line, node identifiers are respectively distributed to all process nodes from a plurality of preset identifiers based on the node relation matrix, adjacent process nodes do not have the same node identifiers, multiple identifier distribution schemes are obtained, a target scheme is determined from all the identifier distribution schemes according to the number of the adopted identifiers, so that the set labels of all the process nodes in the original production line are respectively determined according to the node identifiers of the target scheme, and a linear model is established according to the set labels, so that all the process nodes in the original production line are optimized according to the linear model. Therefore, the production sequence among all process nodes in the original production line is converted from the mixed programming solving problem to the linear programming problem for solving, so that linear reference and linear constraint are provided for the linear theory of the automatic production line, the analysis accuracy of the linear theory on the automatic production line is improved, and the optimization effect of the automatic production line is improved.
In some embodiments, the original production line is abstracted into a two-part logical structure, wherein process nodes are stored through a V set, the process nodes are used for representing machine stations, process flows or production island stations, and edge data are stored through an E set, and the edge data are used for representing upstream and downstream relations between the process nodes.
Optionally, building a node relation matrix according to the production sequence includes: generating edge data for representing the current node to the child node according to the production sequence, wherein the current node and the child node are both process nodes; and establishing a node relation matrix according to the edge data, wherein matrix elements in the node relation matrix are determined according to whether edge data exist among the process nodes.
Optionally, the node relation matrix is established according to the edge data by the following formula:
wherein A is a node relation matrix, a ij E is a matrix element of the ith row and the jth column in the node relation matrix ij For the edge data from the ith process node to the jth process node, N is the number of process nodes.
In some embodiments, the topology diagram of the original production line is shown in fig. 2, where the original production line includes node a, node B, node C, node D, and node E, the child nodes of node a are node C, the child nodes of node B are node C and node D, and the child nodes of node C are node E; generating edge data for representing the current node to the child node according to the production sequence of the original production line, and determining matrix elements in a node relation matrix according to whether the edge data exist between process nodes to obtain the node relation matrix, wherein the node relation matrix is shown in table 1; the precedence relation among the process nodes, father nodes of any process node, father node quantity of any process node, child nodes of any process node and child node quantity of any process node can be obtained through the node relation matrix.
TABLE 1
| Node A | Node B | Node C | Node D | Node E | |
| Node A | 0 | 0 | 1 | 0 | 0 |
| Node B | 0 | 0 | 1 | 1 | 0 |
| Node C | 0 | 0 | 0 | 0 | 1 |
| Node D | 0 | 0 | 0 | 0 | 0 |
| Node E | 0 | 0 | 0 | 0 | 0 |
Optionally, obtaining, according to the node relation matrix, a plurality of identifier allocation schemes meeting a preset constraint condition, including: presetting marks as different types of colors; dyeing any process node by taking one color in the preset mark as a node mark, and determining the process node as a current node; responding to the current node, determining adjacent nodes of the current node from the process nodes according to the node relation matrix, and determining node identifiers of the adjacent nodes from preset identifiers according to preset limiting conditions so as to dye the adjacent nodes; if all adjacent nodes of the current node are dyed, determining a new current node from the adjacent nodes; if all the process nodes are dyed, generating an identification allocation scheme according to the node identifications of all the process nodes.
As shown in fig. 3, an embodiment of the present disclosure provides a method for obtaining an identifier allocation scheme, including:
step S301, presetting a plurality of preset identifiers;
wherein, the preset marks are different types of colors;
step S302, an original production line is obtained;
the original production line comprises a plurality of process nodes and a production sequence among the process nodes;
Step S303, a node relation matrix is established according to the production sequence;
the node relation matrix is used for representing node adjacent relations among process nodes;
step S304, dyeing any process node by taking a color in a preset mark as a node mark, and determining the process node as a current node;
step S305, determining adjacent nodes of the current node according to the node relation matrix;
step S306, judging whether the adjacent nodes have node identifiers, if so, jumping to step S308, and if not, jumping to step S307;
step S307, determining the node identification of the adjacent node from the preset identifications according to the preset limiting conditions so as to dye the adjacent node, and jumping to step S306;
wherein the current node and the adjacent node do not have the same color;
step S308, judging whether all process nodes have node identifiers, if so, jumping to step S310, and if not, jumping to step S309;
step S309, determining a new current node from the adjacent nodes, and jumping to step S305;
step S310, generating an identification allocation scheme according to the node identifications corresponding to the process nodes.
By adopting the method for acquiring the identifier allocation scheme provided by the embodiment of the disclosure, a node relation matrix is established through the production sequence among all process nodes in an original production line, node identifiers are respectively allocated to all process nodes from a plurality of preset identifiers based on the node relation matrix, adjacent process nodes do not have the same node identifier, multiple identifier allocation schemes are obtained, a target scheme is determined from all the identifier allocation schemes according to the number of adopted identifiers, so that the set labels of all the process nodes in the original production line are respectively determined according to the node identifiers of the target scheme, and a linear model is established according to the set labels, so that all the process nodes in the original production line are optimized according to the linear model. Therefore, the production sequence among all process nodes in the original production line is converted from the mixed programming solving problem to the linear programming problem for solving, so that linear reference and linear constraint are provided for the linear theory of the automatic production line, the analysis accuracy of the linear theory on the automatic production line is improved, and the optimization effect of the automatic production line is improved.
Optionally, the preset constraints are characterized by the following formula:
wherein M is the number of preset marks, x i Node identification, x, for the ith process node p Node identification, x, for the p-th process node q Node identification, a, for the q-th process node pi A is the matrix element of the ith row and the ith column in the node relation matrix iq Is the matrix element of the ith row and the qth column in the node relation matrix, N is the number of process nodes and Z + Representing a positive integer.
In some embodiments, in order to improve the operation efficiency, the variable types in the preset limiting conditions are all determined to be integers; establishing a one-dimensional array to obtain an array X, and storing node identifiers of process nodes through the array X; m is the number of preset marks, wherein the initial value of M is set to be 4 based on a four-color principle, and dyeing is carried out on process nodes; if the four-color principle is met, the value of M is reduced; in the process of traversing each process node for dyeing, determining a father node and a child node of the current node according to the node relation matrix, so as to determine the node identification of the current node according to the node identification of the father node and the node identification of the child node of the current node.
Optionally, determining a target scheme M from the identifier allocation schemes according to the number of identifier adoption includes: if the number of the identifiers of the identifier allocation scheme is equal to 2, determining the identifier allocation scheme as a target scheme; if the number of the identifiers of the identifier allocation scheme is not equal to 2, discarding the identifier allocation scheme.
In some embodiments, the objective solution is determined by solving an objective function, wherein the objective function is represented by the following formula:
min(z)＝M，
where z is an optimization target, and the optimization target z is set to the minimum number of preset identifiers, that is, min (z) =2.
In some embodiments, node identifiers are allocated to each process node according to the principle that adjacent process nodes do not have the same node identifier, then a target scheme is determined from the identifier allocation scheme, and the topology diagram of the original production line is halved through the target scheme, which means that the linear problem of the original production line is solved or can be decomposed.
In some embodiments, the topology diagram of the original production line is shown in fig. 2, where the original production line includes node a, node B, node C, node D, and node E, the child nodes of node a are node C, the child nodes of node B are node C and node D, and the child nodes of node C are node E; acquiring a plurality of mark distribution schemes meeting preset limiting conditions according to the node relation matrix, and determining a target scheme from the mark distribution schemes according to the number of the marks to obtain a target scheme Result of the original production line: [2,2,1,1,2]; based on the target scenario, node a, node B, and node E belong to one set, and node C and node D belong to one set.
Therefore, through the linear decomposition of the original production line, the dependence and independence between the technological processes can be judged by the linear decomposition result without considering the upstream and downstream problems between nodes in the topological graph, and the mixed planning solving problem is converted into the linear planning problem to be solved, so that references are provided for constructing efficient capacity paths and reasonable capacity quantity.
Optionally, building a linear model according to the set of labels to optimize each process node in the original production line according to the linear model, including: acquiring an original capacity threshold corresponding to each process node, and acquiring a capacity plan of an original production line, wherein the capacity plan comprises raw material data, a capacity target and a capacity constraint condition; establishing a linear model according to at least one of an original capacity threshold, a set label, a capacity target and a capacity constraint condition to obtain a capacity model corresponding to an original production line; raw material data are input into a capacity model, an original production line is simulated based on linear constraint through the capacity model, a simulation result is obtained, and an original capacity threshold and/or production sequence are adjusted according to the simulation result until the simulation result meets a preset first capacity requirement.
In some embodiments, the topology diagram of the original production line is shown in fig. 2, where the original production line includes node a, node B, node C, node D, and node E, the child nodes of node a are node C, the child nodes of node B are node C and node D, and the child nodes of node C are node E; because the production line has larger capacity data scale, the selection of initial nodes is needed to be considered when traversing process nodes, and the connectivity of all process nodes in the graph is usually checked through multiple circulation or recursion call, but the algorithm needs to trace back logic coding through the algorithm, so that the engineering coding is not facilitated; and (3) distributing the process nodes with the set labels, and when the number of the sets is 2, establishing a full single-mode matrix according to the set labels to obtain a linear matrix model corresponding to the production line, wherein the linear verification model is shown in a table 2.
Thus, even if the line topology has multiple connected components, the linear problem of the line directed graph bisection is solved while the connectivity between the process nodes does not need to be checked.
TABLE 2
| Edge 1 | Edge 2 | Edge 3 | Edge 4 | |
| Node A | 1 | 0 | 0 | 0 |
| Node B | 0 | 1 | 1 | 0 |
| Node C | -1 | -1 | 0 | 1 |
| Node D | 0 | 0 | -1 | 0 |
| Node E | 0 | 0 | 0 | -1 |
Optionally, building a linear model according to the set of labels to optimize each process node in the original production line according to the linear model, including: the process nodes comprise production line input nodes and production line output nodes; raw material data and a productivity model of an original production line are obtained; layering all process nodes in an original production line according to the set labels and the production sequence to obtain a plurality of production layers arranged according to the production sequence, and establishing linear constraint based on the process nodes in the production layers; inputting raw material data into production line input nodes of a capacity model to simulate an original production line based on linear constraint through the capacity model and determine capacity data of each process node; and adjusting the production sequence among the associated nodes according to the capacity data until the capacity data of the output nodes of the production line meet the preset second capacity requirement, wherein the associated nodes are two different process nodes and are respectively arranged in adjacent node layers.
In some embodiments, the topology diagram of the original production line is shown in fig. 2, where the original production line includes node a, node B, node C, node D, and node E, the child nodes of node a are node C, the child nodes of node B are node C and node D, and the child nodes of node C are node E; based on the target scheme, the node A, the node B and the node E belong to one set, and the node C and the node D belong to one set; layering all process nodes in the original production line according to the set labels and the production sequence to obtain 3 production layers arranged according to the production sequence, wherein the first layer of production layer comprises a node A and a node B, the second layer of production layer comprises a node C and a node D, and the third layer of production layer comprises a node E.
In some embodiments, the decision variables representing the production sequence between process nodes through the production line topology map, often with both integer and continuous variables, is a mixed overall planning problem; even if the mathematical model is not complex, the feasible region of the integer programming problem comprises feasible points of a plurality of production lines, the way of exhausting all the feasible points is not suitable for the automobile industry with larger data dimension, and the calculation complexity of problem solving is exponentially increased along with the expansion of the data dimension, so that direct calculation cannot be selected; in the productivity plan of the production line, the productivity data are integers, the objective function and the constraint condition are expressed by linear functions, and a linear model is built according to the set labels; through a full single-mode matrix established based on the set labels, the mixed integer programming problem is converted into a linear programming problem to be solved, and through other mature methods such as calculus and the like, an optimality condition aiming at capacity optimization can be established, so that the complexity of problem calculation is reduced; according to the linear programming solution, the following problems of the production line can be solved: firstly, improving the overall solving efficiency of an algorithm; secondly, obtaining a feasible solution which can meet the linear constraint condition; third, if the linear programming problem is a slightly convex optimization problem, the optimal filling condition can be provided for the production line globally.
As shown in conjunction with fig. 4, an embodiment of the present disclosure provides a production line optimization system, which includes an acquisition module 401, a setup module 402, a determination module 403, and an optimization module 404. The obtaining module 401 is configured to obtain an original production line, where the original production line includes a plurality of process nodes and a production sequence between the process nodes; the establishing module 402 is configured to establish a node relation matrix according to the production order, where the node relation matrix is used to characterize a node adjacent relation between process nodes; the determining module 403 is configured to obtain multiple identifier allocation schemes that satisfy a preset constraint condition according to the node relation matrix, and determine a target scheme from each identifier allocation scheme according to the number of identifier adoption, where the identifier allocation scheme is obtained by respectively allocating a node identifier to each process node from multiple preset identifiers, and the preset constraint condition includes that adjacent process nodes do not have the same node identifier, and the number of identifier adoption is the number of preset identifiers adopted by the identifier allocation scheme; the optimization module 404 is configured to determine an aggregate label of each process node in the original production line according to the node identifier of the target solution, and build a linear model according to the aggregate label, so as to optimize each process node in the original production line according to the linear model.
By adopting the identifier allocation scheme acquisition system provided by the embodiment of the disclosure, a node relation matrix is established through the production sequence among all process nodes in an original production line, node identifiers are respectively allocated to all process nodes from a plurality of preset identifiers based on the node relation matrix, adjacent process nodes do not have the same node identifier, multiple identifier allocation schemes are obtained, a target scheme is determined from all the identifier allocation schemes according to the number of adopted identifiers, so that the set labels of all the process nodes in the original production line are respectively determined according to the node identifiers of the target scheme, and a linear model is established according to the set labels, so that all the process nodes in the original production line are optimized according to the linear model. Therefore, the production sequence among all process nodes in the original production line is converted from the mixed programming solving problem to the linear programming problem for solving, so that linear reference and linear constraint are provided for the linear theory of the automatic production line, the analysis accuracy of the linear theory on the automatic production line is improved, and the optimization effect of the automatic production line is improved.
Fig. 5 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application. It should be noted that, the computer system 500 of the electronic device shown in fig. 5 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 5, the computer system 500 includes a central processing unit (CentralProcessingUnit, CPU) 501, which can perform various appropriate actions and processes, such as performing the methods in the above-described embodiments, according to a program stored in a Read-only memory (ROM) 502 or a program loaded from a storage section 508 into a random access memory (RandomAccessMemory, RAM) 503. In the RAM503, various programs and data required for the system operation are also stored. The CPU501, ROM502, and RAM503 are connected to each other through a bus 504. An Input/Output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a cathode ray tube (CathodeRayTube, CRT), a liquid crystal display (LiquidCrystalDisplay, LCD), and the like, a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN (local area network) card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. When executed by a Central Processing Unit (CPU) 501, performs the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an Erasable programmable read-only memory (EraseR ProgrammableReadOnlyMemory, EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The electronic device disclosed in this embodiment includes a processor, a memory, a transceiver, and a communication interface, where the memory and the communication interface are connected to the processor and the transceiver and perform communication therebetween, the memory is used to store a computer program, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer program, so that the electronic device performs each step of the above method.
In this embodiment, the memory may include a Random Access Memory (RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Network Processor (NP), etc.; but also Digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), field-Programmable GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and sub-samples of some embodiments may be included in or substituted for portions and sub-samples of other embodiments. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. In addition, when used in this application, the terms "comprises," "comprising," and/or "includes," and variations thereof, mean the presence of the stated sub-sample, integer, step, operation, element, and/or component, but do not exclude the presence or addition of one or more other sub-samples, integers, steps, operations, elements, components, and/or groups of these. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled person may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
In the embodiments disclosed herein, the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be practiced in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements may be merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some sub-samples may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to implement the present embodiment. In addition, each functional unit in the embodiments of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method of line optimization, comprising:
acquiring an original production line, wherein the original production line comprises a plurality of process nodes and a production sequence among the process nodes;
establishing a node relation matrix according to the production sequence among the process nodes, wherein the node relation matrix is used for representing the node adjacent relation among the process nodes;
acquiring a plurality of mark distribution schemes meeting preset limiting conditions according to the node relation matrix, and determining a target scheme from the mark distribution schemes according to the number of mark adoption, wherein the mark distribution schemes are obtained by respectively distributing a node mark to each process node from a plurality of preset marks, the preset limiting conditions comprise that adjacent process nodes do not have the same node mark, and the number of mark adoption is the number of preset marks adopted by the mark distribution schemes;
and respectively determining the set labels of all the production line nodes according to the node identifiers of the target schemes, and establishing a linear model according to the set labels so as to optimize all the process nodes in the original production line according to the linear model.
2. The method of claim 1, wherein establishing a node relationship matrix according to a production order between the process nodes comprises:
generating edge data for representing a current node to a child node according to the production sequence, wherein the current node and the child node are both process nodes;
and establishing a node relation matrix according to the edge data, wherein matrix elements in the node relation matrix are determined according to whether edge data exist among the process nodes.
3. The method of claim 2, wherein a node relation matrix is established from the edge data by the following formula:
wherein A is a node relation matrix, a ij E, matrix elements of the ith row and the jth column in the node relation matrix ij For the edge data from the ith process node to the jth process node, N is the number of process nodes.
4. The method of claim 2, wherein obtaining, from the node relation matrix, a plurality of identifier allocation schemes that satisfy a preset constraint, comprises:
the preset marks are of different colors;
dyeing any process node by taking one color in the preset mark as a node mark, and determining the process node as a current node;
Responding to a current node, determining adjacent nodes of the current node from the process nodes according to the node relation matrix, and determining node identifiers of the adjacent nodes from the preset identifiers according to the preset limiting conditions so as to dye the adjacent nodes;
if all adjacent nodes of the current node are dyed, determining a new current node from the adjacent nodes;
and if all the process nodes are dyed, generating an identification allocation scheme according to the node identification of each process node.
5. The method of claim 4, wherein the preset constraints are characterized by the following formula:
wherein M is the number of preset marks, x i Node identification, x, for the ith process node p Node identification, x, for the p-th process node q Node identification, a, for the q-th process node pi A is the matrix element of the ith row and the ith column in the node relation matrix iq For the matrix elements of the ith row and the qth column in the node relation matrix, N is the number of process nodes and Z + Representing a positive integer.
6. The method of any one of claims 1 to 5, wherein determining a target scheme from each of the identifier allocation schemes based on the number of identifier adoption comprises:
If the number of the identifiers of the identifier allocation scheme is equal to 2, determining the identifier allocation scheme as a target scheme;
and if the number of the identifiers of the identifier allocation scheme is not equal to 2, discarding the identifier allocation scheme.
7. The method of any one of claims 1 to 5, wherein building a linear model from the set of labels to optimize each process node in the original production line from the linear model comprises:
acquiring an original capacity threshold corresponding to each process node, and acquiring a capacity plan of the original production line, wherein the capacity plan comprises raw material data, a capacity target and capacity constraint conditions;
establishing a linear model according to at least one of the original capacity threshold, the set label, the capacity target and the capacity constraint condition to obtain a capacity model corresponding to the original production line;
inputting the raw material data into the productivity model to simulate the original production line based on the linear constraint through the productivity model to obtain a simulation result, and adjusting the original productivity threshold and/or the production sequence according to the simulation result until the simulation result meets a preset first productivity requirement.
8. The method of any one of claims 1 to 5, wherein building a linear model from the set of labels to optimize each process node in the original production line from the linear model comprises:
the process node comprises a production line input node and a production line output node;
acquiring raw material data and a capacity model of the original production line;
layering all process nodes in the original production line according to the set labels and the production sequence to obtain a plurality of production layers arranged according to the production sequence, and establishing linear constraint based on the process nodes in the production layers;
inputting the raw material data into a line input node of the capacity model to simulate the original line based on the linear constraints by the capacity model and to determine capacity data for each of the process nodes;
and adjusting the production sequence among the associated nodes according to the capacity data until the capacity data of the output nodes of the production line meet the preset second capacity requirement, wherein the associated nodes are two different process nodes and are respectively existing in adjacent node layers.
9. A production line optimization system, comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an original production line, and the original production line comprises a plurality of process nodes and production sequences among the process nodes;
the building module is used for building a node relation matrix according to the production sequence among the process nodes, wherein the node relation matrix is used for representing the node adjacent relation among the process nodes;
the determining module is used for obtaining a plurality of mark distribution schemes meeting preset limiting conditions according to the node relation matrix, and determining a target scheme from the mark distribution schemes according to the number of mark adoption, wherein the mark distribution schemes are obtained by respectively distributing a node mark to each process node from a plurality of preset marks, the preset limiting conditions comprise that adjacent process nodes do not have the same node mark, and the number of mark adoption is the number of preset marks adopted by the mark distribution schemes;
and the optimization module is used for respectively determining the set labels of all the production line nodes according to the node identifiers of the target scheme, and establishing a linear model according to the set labels so as to optimize all the process nodes in the original production line according to the linear model.
10. An electronic device, comprising: a processor and a memory;
the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, to cause the electronic device to perform the method according to any one of claims 1 to 8.
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