A method and device for workshop scheduling modeling based on extended disjunctive graph model

Abstract

The present invention discloses a method and device for workshop scheduling modeling based on an extended disjunctive graph model, which relates to the field of data processing technology. The main technical scheme includes: first, constructing a first disjunctive graph model, which is used to construct workshop process sequence constraints and machine resource constraints; second, constructing a second disjunctive graph model, which is used to construct process material constraints and workshop process correlation constraints; finally, modeling workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model. By stacking the first disjunctive graph model and the second disjunctive graph model, the correlation relationship between the process and the material and between different processes in the workshop is increased, so that the modeled model contains the relationship between multiple machine resources in the same process, the correlation relationship between processes, and the constraint relationship related to materials, so that the scheduling result finally solved is closer to the actual production process and meets the needs of complex discrete manufacturing.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for workshop scheduling modeling based on an extended disjunctive graph model. Background Art

[0002] At present, most workshop scheduling problems in discrete manufacturing industries are simplifications of some practical problems. The modeling methods of production scheduling problems mainly include mathematical models, disjunctive graph models and simulation models. Due to the complexity of discrete manufacturing industries, mathematical models and simulation models generally need to be analyzed for specific instances or projects. When the problem structure and parameters change, the mathematical models and simulation models also need to be modified accordingly.

[0003] The disjunctive graph model is a general description method for scheduling problems, but the current disjunctive graph model only describes simplified production scheduling problems and can only express process sequence constraints and resource operation constraints based on process routes, but cannot express more complex discrete manufacturing processes. Summary of the invention

[0004] The present disclosure provides a method and device for shop scheduling modeling based on an extended disjunctive graph model, so as to at least solve the problem in the related art that the disjunctive graph model cannot express more complex discrete manufacturing processes.

[0005] The technical solution of the present disclosure is as follows:

[0006] According to a first aspect of an embodiment of the present disclosure, a method for shop scheduling modeling based on an extended disjunctive graph model is provided, comprising:

[0007] Constructing a first disjunctive graph model, wherein the first disjunctive graph model is used to construct a shop process sequence constraint and a machine resource constraint;

[0008] Constructing a second disjunctive graph model, wherein the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints;

[0009] The shop scheduling is modeled based on the first disjunctive graph model and the second disjunctive graph model.

[0010] Optionally, constructing the first disjunctive graph model includes:

[0011] Establish corresponding process nodes according to the processing procedures of all tasks;

[0012] Each process node is associated with any machine resource that can be used by the process to create an activity node, wherein the process node and the activity node are in a one-to-many relationship;

[0013] According to the sequence of the process nodes and the activity nodes, construct an undirected disjunctive edge constraint;

[0014] Construct undirected disjunctive edge sets;

[0015] Creating directed connection edges based on the process processing routes of each task, constructing a directed graph model of the process nodes, and representing the sequential constraints of the process processing start time;

[0016] A first disjunctive graph model is constructed based on the undirected disjunctive edge constraints and the directed graph model of the process nodes.

[0017] Optionally, the constructing an undirected disjunctive edge set includes:

[0018] For any machine resource, a variable of 0 or 1 is used to represent the order relationship between the first active node and the second active node;

[0019] All active nodes are traversed and grouped according to the same machine resources. For any two active nodes in the group, an undirected disjunctive edge set is created to represent the resource constraints of the active nodes.

[0020] Optionally, the directed connection edges are created based on the process processing routes of each task, and the directed graph model of the process nodes is constructed. The sequential constraints representing the start time of the process processing include:

[0021]

[0022] Where i represents the processing sequence of task j; Indicates process O ji Start processing time; Indicates the previous process O of the task j(i-1) Start processing time; Indicates process O ji processing time.

[0023] Optionally, constructing the second disjunctive graph model includes:

[0024] Based on the materials consumed and produced at any process node, corresponding material nodes are established, and material connection edges are constructed;

[0025] Calculate the production duration of the process node or activity node based on the process takt time and the quantity of materials consumed or produced during the takt time;

[0026] Calculate the duration and material quantity of all process nodes or activity nodes in sequence according to the process sequence route;

[0027] Establish dependency constraints between processes of different tasks.

[0028] Optionally, the correlation constraint includes any of the following:

[0029] The front process starts processing, and the back process begins;

[0030] The previous process starts and the next process ends;

[0031] The previous process ends and the next process begins;

[0032] The previous process is completed, and the next process is completed.

[0033] Optionally, the modeling of shop scheduling based on the first disjunctive graph model and the second disjunctive graph model includes:

[0034] The first disjunctive graph model and the second disjunctive graph model are respectively constructed to complete the modeling of the workshop scheduling.

[0035] According to a second aspect of an embodiment of the present disclosure, a device for workshop scheduling modeling based on an extended disjunctive graph model is provided, comprising:

[0036] A first construction unit is used to construct a first disjunctive graph model, wherein the first disjunctive graph model is used to construct a shop process sequence constraint and a machine resource constraint;

[0037] A second construction unit is used to construct a second disjunctive graph model, wherein the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints;

[0038] A modeling unit is used to model the shop scheduling based on the first disjunctive graph model and the second disjunctive graph model.

[0039] Optionally, the first building unit includes:

[0040] Establish a module to establish corresponding process nodes according to the processing procedures of all tasks;

[0041] A first creation module is used to create an activity node by linking each process node with any machine resource that can be used by the process, wherein the process node and the activity node are in a one-to-many relationship;

[0042] A second construction module is used to construct an undirected disjunctive edge constraint according to the sequence of the process nodes and the activity nodes;

[0043] The third building module is used to construct an undirected disjunctive edge set;

[0044] A fourth construction module is used to create directed connection edges based on the process processing routes of each task, construct a directed graph model of the process nodes, and represent the sequence constraints of the process processing start time;

[0045] The fifth construction module is used to construct a first disjunctive graph model based on the undirected disjunctive edge constraints and the directed graph model of the process nodes.

[0046] Optionally, the third building block is further used for:

[0047] For any machine resource, a variable of 0 or 1 is used to represent the order relationship between the first active node and the second active node;

[0048] All active nodes are traversed and grouped according to the same machine resources. For any two active nodes in the group, an undirected disjunctive edge set is created to represent the resource constraints of the active nodes.

[0049] Optionally, the fourth building block is further used for:

[0050]

[0051] Among them, i represents the processing sequence of task j; Indicates process O ji Start processing time; Indicates the previous process O of the task j(i-1) Start processing time; Indicates process O ji processing time.

[0052] Optionally, the second building unit includes:

[0053] The first establishment module is used to establish corresponding material nodes and construct material connection edges based on the materials consumed and produced at any process node;

[0054] A first calculation module is used to calculate the production time of the process node or activity node according to the process takt time and the quantity of materials consumed or produced within the takt time;

[0055] The second calculation module is used to calculate the duration and material quantity of all process nodes or activity nodes in sequence according to the process sequence route;

[0056] The second establishment module is used to establish correlation constraints between processes of different tasks.

[0057] Optionally, the correlation constraint includes any of the following:

[0058] The front process starts processing, and the back process begins;

[0059] The previous process starts and the next process ends;

[0060] The previous process ends and the next process begins;

[0061] The previous process is completed, and the next process is completed.

[0062] Optionally, the modeling unit is further used for:

[0063] The first disjunctive graph model and the second disjunctive graph model are respectively constructed to complete the modeling of the workshop scheduling.

[0064] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0065] processor;

[0066] a memory for storing instructions executable by the processor;

[0067] The processor is configured to execute the instructions to implement a method for shop scheduling modeling based on an extended disjunctive graph model as described in any one of the first aspects above.

[0068] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute a method for workshop scheduling modeling based on an extended disjunctive graph model as described in any one of the first aspects above.

[0069] The disclosed embodiment provides a method and device for workshop scheduling modeling based on an extended disjunctive graph model, and the main technical scheme includes: first, constructing a first disjunctive graph model, the first disjunctive graph model is used to construct workshop process sequence constraints and machine resource constraints; second, constructing a second disjunctive graph model, the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints; finally, modeling workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model. Compared with the related art, the embodiment of the present application adds the correlation relationship between processes and materials and between different processes in the workshop by stacking the first disjunctive graph model and the second disjunctive graph model, so that the modeled model contains the relationship between multiple machine resources in the same process, the correlation relationship between processes, and the constraint relationship related to materials, so that the scheduling result finally solved is closer to the actual production process and meets the needs of complex discrete manufacturing.

[0070] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0072] Figure 1 A flow chart of a method for workshop scheduling modeling based on an extended disjunctive graph model provided by an embodiment of the present disclosure;

[0073] Figure 2 A schematic diagram of consumed materials and output materials at a process node provided by an embodiment of the present disclosure;

[0074] Figure 3 An extended disjunctive graph model provided by an embodiment of the present disclosure;

[0075] Figure 4 A schematic flow chart of a method for constructing a first disjunctive graph model provided by an embodiment of the present disclosure;

[0076] Figure 5 A schematic diagram of creating an activity node according to a process node provided by an embodiment of the present disclosure;

[0077] Figure 6 A schematic flow chart of a method for constructing a second disjunctive graph model provided by an embodiment of the present disclosure;

[0078] Figure 7 A schematic diagram of a process dependency constraint type provided by an embodiment of the present disclosure;

[0079] Figure 8 A schematic diagram of a modeling solution result provided by an embodiment of the present disclosure;

[0080] Fig. 9 A schematic diagram of the structure of a device for workshop scheduling modeling based on an extended disjunctive graph model provided in an embodiment of the present disclosure;

[0081] Fig.10 A schematic diagram of the structure of another device for workshop scheduling modeling based on an extended disjunctive graph model provided in an embodiment of the present disclosure;

[0082] Fig.11 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0083] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0084] A method or device for shop scheduling modeling based on an extended disjunctive graph model according to an embodiment of the present disclosure will be described below with reference to the accompanying drawings.

[0085] Figure 1 A flowchart of a method for workshop scheduling modeling based on an extended disjunctive graph model provided in an embodiment of the present disclosure.

[0086] like Figure 1 As shown, the method comprises the following steps:

[0087] Step 101: construct a first disjunctive graph model, where the first disjunctive graph model is used to construct workshop process sequence constraints and machine resource constraints.

[0088] Among the different products produced in the same workshop, each product has several processes to go through in sequence, and each process uses corresponding machine resources, as well as the duration of using the machine resources determined by the product data. Different products may have the same processes or the same machine resources; therefore, a comprehensive consideration is given to the process sequence of each product, the machine resources used in each process, and the usage time.

[0089] Step 102: construct a second disjunctive graph model, where the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints.

[0090] See also Figure 2 , Figure 2 This is a schematic diagram of a process node consumption material and output material provided in an embodiment of the present application, for example, at the process node O of the first product ji Input materials 1, 2 and 3 are processed into two output materials 4 and 5. Output material 4 is used as the input material of the next process of the first product, but output material 5 is the input material of a process of the second product. When determining the product process, the process of the second product is set in the O of the first product. ji After the node.

[0091] Step 103: Modeling the shop scheduling based on the first disjunctive graph model and the second disjunctive graph model.

[0092] See also Figure 3 , Figure 3 An extended disjunctive graph model is provided in an embodiment of the present application; a model is built based on the first disjunctive graph and the second disjunctive graph, and a comprehensive analysis is performed. During the analysis, the solution can be obtained through operations research algorithms or by using methods such as deep learning based on neural networks. The embodiment of the present application does not limit the solution algorithm.

[0093] The disclosed embodiment provides a method for workshop scheduling modeling based on an extended disjunctive graph model, and the main technical scheme includes: first, constructing a first disjunctive graph model, the first disjunctive graph model is used to construct workshop process sequence constraints and machine resource constraints; second, constructing a second disjunctive graph model, the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints; finally, modeling workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model. Compared with the related art, the embodiment of the present application adds the correlation relationship between processes and materials and between different processes in the workshop by stacking the first disjunctive graph model and the second disjunctive graph model, so that the modeled model contains the relationship between multiple machine resources in the same process, the correlation relationship between processes, and the constraint relationship related to materials, so that the scheduling result finally solved is closer to the actual production process and meets the needs of complex discrete manufacturing.

[0094] When constructing the first disjunctive graph model, it is constructed according to the process sequence constraints and resource constraints of different products; see Figure 4 , Figure 4 A schematic flow chart of a method for constructing a first disjunctive graph model provided in an embodiment of the present application includes:

[0095] Step 201, establishing corresponding process nodes according to the processing procedures of all tasks.

[0096] According to the processing procedures of each product, process nodes are established respectively. For example, if product A has three processing procedures, three process nodes A1, A2, and A3 are established, and each process node corresponds to the processing procedure of product A respectively. This application does not limit the number of processing nodes corresponding to each product.

[0097] Step 202 , each process node is associated with any machine resource that can be used by the process to create an activity node, wherein the process node and the activity node are in a one-to-many relationship.

[0098] For the same process, there may be multiple machine resources that can complete it. Figure 5 , Figure 5 A schematic diagram of creating an activity node according to a process node provided in an embodiment of the present application; wherein, ji is a process node, and the current process node has two machine resources m0 and m1, then two active nodes O can be generated. jim0 With O jim1 Specifically, it can be set according to the number of process nodes and machine resources, and the specific embodiments of the present application do not limit this.

[0099] Step 203: construct an undirected disjunctive edge constraint according to the sequence of the process nodes and the activity nodes.

[0100] Process Node O ji With active node O jim It is a one-to-many relationship, using 0-1 variables to represent active nodes Whether it is valid, where 0 means invalid and 1 means valid.

[0101] For any process O ji , its corresponding active node has only one valid one, and the constraint expression is:

[0102]

[0103] Process Node O ji Start processing time With active node O jim Start processing time The relationship is:

[0104]

[0105] Process Node O ji Processing time With active node O jim Processing time The relationship is:

[0106]

[0107] Step 204: construct an undirected disjunctive edge set.

[0108] For any machine resource, a variable of 0 or 1 is used to represent the order relationship between the first active node and the second active node;

[0109]

[0110] Traverse all active nodes and group them according to the same machine resources. For any two active nodes in the group, create an undirected disjunctive edge set to represent the resource constraints E of the active nodes. MO The constraints are:

[0111]

[0112] in, and Respectively represent activities O jim and activities htm Start processing time; and Respectively represent activities O jim and activities htmprocessing time; M is an infinite constant, which serves as a penalty item. To facilitate understanding of the penalty item, the penalty item is set according to the production scheduling time. For example, if the production is scheduled for one month, the penalty item can be set to a constant corresponding to 3 months (120 days), or it can be set to a larger value, but the calculation will be slower. This embodiment does not specifically limit the penalty item.

[0113] Resource constraints of active nodes E MO In the constraint, if When is 0, formula (1) is always valid, and formula (2) is valid;

[0114] if When it is 1, formula (1) is valid and formula (2) is always valid;

[0115] It represents the time sequence constraints when two processes are produced on the same equipment.

[0116] Step 205 , creating directed connection edges based on the process processing routes of each task, constructing a directed graph model of the process nodes, and representing the sequential constraints of the process processing start time.

[0117] Based on the process route of each task (such as J1 processing requires three processes O 11 , O 12 , O 13 ), create directed connecting edges E, build a directed graph model of process nodes, and represent the sequential constraints of process processing start time:

[0118]

[0119] Among them, i represents the processing sequence of task j; Indicates process O ji Start processing time; Indicates the previous process O of the task j(i-1) Start processing time; Indicates process O ji The processing time, Indicates the previous process O of the task j(i-1) processing time.

[0120] Step 206: construct a first disjunctive graph model based on the undirected disjunctive edge constraints and the directed graph model of the process nodes.

[0121] When constructing the second disjunctive graph model, you can build it based on material constraints and process-related constraints; see Figure 6 , Figure 6 A schematic flow chart of a method for constructing a second disjunctive graph model provided in an embodiment of the present application includes:

[0122] Step 301: Based on the materials consumed and produced at any process node, corresponding material nodes are established, and material connection edges are constructed.

[0123] Step 302, calculating the production duration of the process node or activity node according to the process takt time and the quantity of materials consumed or produced within the takt time.

[0124] For process nodes and material nodes, it is known that process O ji Takt time And the quantity of materials consumed or produced during the cycle time. If the total demand quantity of material 4 required by the subsequent node is Then the production time of the process node (or activity node) can be calculated as

[0125] Step 303, according to the process sequence route, calculate the duration and material quantity of all process nodes or activity nodes in sequence.

[0126] According to the process sequence route, the duration and material quantity of all process nodes (or activity nodes) can be calculated in sequence. ji In takt time Consumable materials The number is: Production Materials The number is: When material is required The number of When jim Processing time The calculation formula is as follows:

[0127]

[0128] Materials consumed The total quantity is:

[0129]

[0130] Step 304: Establish dependency constraints between processes of different tasks.

[0131] In the production process, in addition to the process order constraints within the same task, there may also be correlation constraints between processes in different tasks. OO Indicates that common constraint types are as follows (see Figure 7 ): If the earliest start time and the earliest end time of a process are subject to the previous related processes, for two processes of different tasks, ht and O ji , the types of mutual constraints are as follows:

[0132] See also Figure 7 , Figure 7 A schematic diagram of a process correlation constraint type provided in an embodiment of the present application;

[0133] The previous process starts processing, and the next process begins; please refer to Figure 7 As shown in SS working condition: the previous process O ji Start (START), post-process O ht START:

[0134] When process O ji After starting processing for d time, allow process O ht Start processing, and its constraint expression is:

[0135]

[0136] in and represents the start time of the previous and next processes, and d represents the time constraint.

[0137] The previous process starts and the next process ends; please refer to Figure 7 The SE working condition in the middle shows: the previous process starts (START) and the next process ends (END):

[0138] When process O ji After starting processing for d time, allow process O ht The processing is completed, and its constraint expression is:

[0139] Arranged

[0140]

[0141] The previous process ends and the next process begins. Please continue to refer to Figure 7 The ES working condition shows: the previous process ends (END) and the next process starts (START):

[0142] When process O ji After completing the processing time d, allow process O ht Start processing, and its constraint expression is:

[0143]

[0144] The previous process is finished, the next process is finished, please refer to Figure 7 The EE working condition shows: the previous process ends (END), the next process ends (END):

[0145] When process O ji After completing the processing time d, allow process Oht Completed, its constraint expression is:

[0146] Arranged

[0147]

[0148] In one possible implementation of the embodiment of the present application, the first disjunctive graph model and the second disjunctive graph model are respectively constructed to complete the modeling of the workshop scheduling. After the modeling is completed, the data required by the model is input into the trained model, and the process sequence and process operation time of each production line can be obtained. Figure 8 As shown, Figure 8 A schematic diagram of a modeling solution result provided in an embodiment of the present application; the solution result is visually displayed.

[0149] Corresponding to the above-mentioned method for workshop scheduling modeling based on an extended disjunctive graph model, the present invention also proposes a device for workshop scheduling modeling based on an extended disjunctive graph model. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0150] Fig. 9 A schematic diagram of a structure of a device for workshop scheduling modeling based on an extended disjunctive graph model provided in an embodiment of the present disclosure is shown in FIG. Fig. 9 As shown, including:

[0151] A first construction unit 41 is used to construct a first disjunctive graph model, wherein the first disjunctive graph model is used to construct a workshop process sequence constraint and a machine resource constraint;

[0152] A second construction unit 42 is used to construct a second disjunctive graph model, wherein the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints;

[0153] The modeling unit 43 is used to model the shop scheduling based on the first disjunctive graph model and the second disjunctive graph model.

[0154] The disclosed embodiment provides a device for workshop scheduling modeling based on an extended disjunctive graph model, and the main technical scheme includes: first, constructing a first disjunctive graph model, the first disjunctive graph model is used to construct workshop process sequence constraints and machine resource constraints; second, constructing a second disjunctive graph model, the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints; finally, modeling workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model. Compared with the related art, the embodiment of the present application adds the correlation relationship between processes and materials and between different processes in the workshop by stacking the first disjunctive graph model and the second disjunctive graph model, so that the modeled model contains the relationship between multiple machine resources in the same process, the correlation relationship between processes, and the constraint relationship related to materials, so that the scheduling result finally solved is closer to the actual production process and meets the needs of complex discrete manufacturing.

[0155] Furthermore, in a possible implementation of this embodiment, as Fig.10 As shown, the first construction unit 41 includes:

[0156] Establishing module 411, used to establish corresponding process nodes according to the processing procedures of all tasks;

[0157] A first creation module 412 is used to create an activity node by linking each process node with any machine resource that can be used by the process, wherein the process node and the activity node are in a one-to-many relationship;

[0158] A second construction module 413 is used to construct an undirected disjunctive edge constraint according to the sequence of the process nodes and the activity nodes;

[0159] The third construction module 414 is used to construct an undirected disjunctive edge set;

[0160] The fourth construction module 415 is used to create directed connection edges based on the process processing routes of each task, construct a directed graph model of the process nodes, and represent the sequence constraints of the process processing start time;

[0161] The fifth construction module 416 is used to construct a first disjunctive graph model based on the undirected disjunctive edge constraints and the directed graph model of the process nodes.

[0162] Furthermore, in a possible implementation of this embodiment, the third building module 414 is further used to:

[0163] For any machine resource, a variable of 0 or 1 is used to represent the order relationship between the first active node and the second active node;

[0164] All active nodes are traversed and grouped according to the same machine resources. For any two active nodes in the group, an undirected disjunctive edge set is created to represent the resource constraints of the active nodes.

[0165] Furthermore, in a possible implementation of this embodiment, the fourth building module 415 is further used to:

[0166]

[0167] Among them, i represents the processing sequence of task j; Indicates process O ji Start processing time; Indicates the previous process O of the task j(i-1) Start processing time; Indicates process O ji processing time.

[0168] Furthermore, in a possible implementation of this embodiment, as Fig.10 As shown, the second construction unit 42 includes:

[0169] The first establishing module 421 is used to establish corresponding material nodes based on the materials consumed and produced by any process node, and to construct material connection edges;

[0170] A first calculation module 422 is used to calculate the production duration of the process node or activity node according to the process takt time and the quantity of materials consumed or produced within the takt time;

[0171] The second calculation module 423 is used to calculate the duration and material quantity of all process nodes or activity nodes in sequence according to the process sequence route;

[0172] The second establishing module 424 is used to establish correlation constraints between processes of different tasks.

[0173] Furthermore, in a possible implementation of this embodiment, the correlation constraint includes any of the following:

[0174] The front process starts processing, and the back process begins;

[0175] The previous process starts and the next process ends;

[0176] The previous process ends and the next process begins;

[0177] The previous process is completed, and the next process is completed.

[0178] Furthermore, in a possible implementation of this embodiment, the modeling unit 43 is further configured to:

[0179] The first disjunctive graph model and the second disjunctive graph model are respectively constructed to complete the modeling of the workshop scheduling.

[0180] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0181] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0182] Fig.11 A schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0183] like Fig.11 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 to a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0184] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0185] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as a method for shop scheduling modeling based on an extended disjunctive graph model. For example, in some embodiments, a method for shop scheduling modeling based on an extended disjunctive graph model may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured in any other appropriate manner (for example, by means of firmware) to execute the aforementioned method for shop scheduling modeling based on an extended disjunctive graph model.

[0186] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0187] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0188] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0189] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0190] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0191] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0192] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0193] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0194] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for shop scheduling modeling based on an extended disjunctive graph model, characterized in that: include: Constructing a first disjunctive graph model, wherein the first disjunctive graph model is used to construct a shop process sequence constraint and a machine resource constraint; Constructing a second disjunctive graph model, wherein the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints; Modeling shop scheduling based on the first disjunctive graph model and the second disjunctive graph model; The constructing of the first disjunctive graph model comprises: Establish corresponding process nodes according to the processing procedures of all tasks; Each process node is associated with any machine resource that can be used by the process to create an activity node, wherein the process node and the activity node are in a one-to-many relationship; According to the sequence of the process nodes and the activity nodes, construct an undirected disjunctive edge constraint; Construct undirected disjunctive edge sets; Creating directed connection edges based on the process processing routes of each task, constructing a directed graph model of the process nodes, and representing the sequential constraints of the process processing start time; Constructing a first disjunctive graph model based on the undirected disjunctive edge constraint and the directed graph model of the process node; The constructing of the second disjunctive graph model comprises: Based on the materials consumed and produced at any process node, corresponding material nodes are established, and material connection edges are constructed; Calculate the production duration of the process node or activity node based on the process takt time and the quantity of materials consumed or produced during the takt time; Calculate the duration and material quantity of all process nodes or activity nodes in sequence according to the process sequence route; Establish dependency constraints between processes of different tasks.

2. The method according to claim 1, characterized in that The construction of an undirected disjunctive edge set includes: For any machine resource, a variable of 0 or 1 is used to represent the order relationship between the first active node and the second active node; All active nodes are traversed and grouped according to the same machine resources. For any two active nodes in the group, an undirected disjunctive edge set is created to represent the resource constraints of the active nodes.

3. The method according to claim 2, characterized in that The directed connection edges are created based on the process processing route of each task, and the directed graph model of the process node is constructed. The sequential constraints representing the start time of the process processing include: Among them, i represents the processing sequence of the task; Indicates process O ji Start processing time; Indicates the previous process O of the task j(i-1) Start processing time; Indicates process O ji The processing time, Indicates the previous process O of the task j(i-1) processing time.

4. The method according to claim 1, characterized in that: The correlation constraint includes any of the following: The front process starts processing, and the back process begins; The previous process starts and the next process ends; The previous process ends and the next process begins; The previous process is completed, and the next process is completed.

5. The method according to any one of claims 1 to 4, characterized in that The modeling of shop scheduling based on the first disjunctive graph model and the second disjunctive graph model includes: The first disjunctive graph model and the second disjunctive graph model are respectively constructed to complete the modeling of the workshop scheduling.

6. A device for workshop scheduling modeling based on an extended disjunctive graph model, characterized in that: include: A first construction unit is used to construct a first disjunctive graph model, wherein the first disjunctive graph model is used to construct a shop process sequence constraint and a machine resource constraint; A second construction unit is used to construct a second disjunctive graph model, wherein the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints; A modeling unit, configured to model the shop scheduling based on the first disjunctive graph model and the second disjunctive graph model; The first building block comprises: Establish a module to establish corresponding process nodes according to the processing procedures of all tasks; A first creation module is used to create an activity node by linking each process node with any machine resource that can be used by the process, wherein the process node and the activity node are in a one-to-many relationship; A second construction module is used to construct an undirected disjunctive edge constraint according to the sequence of the process nodes and the activity nodes; The third building module is used to construct an undirected disjunctive edge set; A fourth construction module is used to create directed connection edges based on the process processing routes of each task, construct a directed graph model of the process nodes, and represent the sequence constraints of the process processing start time; A fifth construction module, configured to construct a first disjunctive graph model based on the undirected disjunctive edge constraint and the directed graph model of the process node; The second building block comprises: The first establishment module is used to establish corresponding material nodes and construct material connection edges based on the materials consumed and produced at any process node; A first calculation module is used to calculate the production time of the process node or activity node according to the process takt time and the quantity of materials consumed or produced within the takt time; The second calculation module is used to calculate the duration and material quantity of all process nodes or activity nodes in sequence according to the process sequence route; The second establishment module is used to establish correlation constraints between processes of different tasks.

7. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method for shop scheduling modeling based on an extended disjunctive graph model as described in any one of claims 1 to 5.

8. A storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for shop scheduling modeling based on an extended disjunctive graph model as described in any one of claims 1 to 5.

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