CN115903653A

CN115903653A - Workshop scheduling modeling method and device based on extended disjunctive graph model

Info

Publication number: CN115903653A
Application number: CN202211405759.9A
Authority: CN
Inventors: 彭朝; 余坤; 宋佳; 姜云倩; 赵娟
Original assignee: Shanghai Shuyi Technology Co ltd
Current assignee: Shanghai Shuyi Technology Co ltd
Priority date: 2022-11-10
Filing date: 2022-11-10
Publication date: 2023-04-04

Abstract

The invention discloses a workshop scheduling modeling method and device based on an extended disjunctive graph model, which relate to the technical field of data processing and mainly adopt the technical scheme that: firstly, constructing a first disjunctive graph model, wherein the first disjunctive graph model is used for constructing workshop process sequence constraint and machine resource constraint; secondly, constructing a second disjunctive graph model, wherein the second disjunctive graph model is used for constructing process material constraints and workshop process correlation constraints; and finally, modeling the 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 among the processes, different processes of materials and workshops is increased, so that the modeled model comprises the multi-machine resource relation of the same process, the correlation among the processes and the material related constraint relation, the finally solved scheduling result is closer to the actual production process, and the complex discrete manufacturing requirement is met.

Description

Workshop scheduling modeling method and device based on extended disjunctive graph model

Technical Field

The disclosure relates to the technical field of data processing, in particular to a workshop scheduling modeling method and device based on an extended disjunctive graph model.

Background

At present, most of workshop scheduling problems of the discrete manufacturing industry are simplified aiming at some practical problems, a production scheduling problem modeling method mainly comprises a mathematical model, an extraction graph model, a simulation model and the like, due to the complexity of the discrete manufacturing industry, the mathematical model and the simulation model generally need to be analyzed aiming at specific examples or projects, and when problem structures and parameters change, the mathematical model and the simulation model also need to be correspondingly modified.

The disjunctive graph model is a general description method aiming at scheduling problems, but the existing disjunctive graph model still describes the simplified production scheduling problems, can only express the process sequence constraint and the resource operation constraint based on the process route, and cannot express the more complex discrete manufacturing process.

Disclosure of Invention

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

The technical scheme of the disclosure is as follows:

according to a first aspect of the embodiments of the present disclosure, there is provided a method for plant scheduling modeling based on an extended disjunctive graph model, including:

constructing a first disjunctive graph model, wherein the first disjunctive graph model is used for constructing workshop process sequence constraint and machine resource constraint;

constructing a second disjunctive graph model, wherein the second disjunctive graph model is used for constructing process material constraints and workshop process correlation constraints;

and modeling the workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model.

Optionally, the constructing the first disjunctive graph model includes:

establishing corresponding process nodes according to the processing processes of all tasks;

creating an active node by using any machine resource for each process node and the process, wherein the process node and the active node are in one-to-many relationship;

constructing undirected disjunctive edge constraints according to the sequence of the process nodes and the active nodes;

constructing an undirected disjunctive edge set;

establishing a directed connecting edge based on the process machining route of each task, constructing a directed graph model of the process node, and representing sequence constraint of process machining starting time;

and constructing a first disjunctive graph model based on the undirected disjunctive edge constraint and the directed graph model of the process node.

Optionally, the constructing an undirected disjuncted edge set includes:

for any machine resource, representing the precedence order relationship between the first active node and the second active node by using a variable 0 or 1;

and traversing all the active nodes, grouping according to the same machine resource, and establishing an undirected disjunctive edge set for any two active nodes in the group to represent the resource constraint of the active nodes.

Optionally, the creating a directed connecting edge based on the process processing route of each task, constructing a directed graph model of the process node, and characterizing the sequence constraint of the process processing start time includes:

wherein i represents a sequence of machining processes for task j;

represents the step O _ji The start of machining time of (2); />

A preamble process O representing the task _j(i-1) The start of machining time of (2); />

Represents the step O _ji The processing time of (2).

Optionally, the constructing the second disjunctive graph model includes:

establishing corresponding material nodes based on materials consumed and produced by any process node, and constructing material connecting edges;

calculating the production duration of the process node or the activity node according to the takt time of the process and the quantity of consumed or produced materials in the takt time;

sequentially calculating the duration and the material quantity of all process nodes or movable nodes according to the process sequence route;

correlation constraints between processes between different tasks are established.

Optionally, the dependency constraint includes any of:

starting machining in the former process and starting the latter process;

the former process is started, and the latter process is finished;

the former process is finished, and the latter process is started;

the former step is finished, and the latter step is finished.

Optionally, the modeling the plant scheduling based on the first disjunctive graph model and the second disjunctive graph model includes:

and respectively carrying out graph construction on the first disjunctive graph model and the second disjunctive graph model to complete the modeling of the workshop scheduling.

According to a second aspect of the embodiments of the present disclosure, there is provided an apparatus for plant scheduling modeling based on an extended disjunctive graph model, including:

the system comprises a first construction unit, a second construction unit and a third construction unit, wherein the first construction unit is used for constructing a first disjunctive graph model which is used for constructing workshop process sequence constraint and machine resource constraint;

the second construction unit is used for constructing a second disjunctive graph model, and the second disjunctive graph model is used for constructing process material constraints and workshop process correlation constraints;

and the modeling unit is used for modeling the workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model.

Optionally, the first building unit includes:

the establishing module is used for establishing corresponding process nodes according to the processing processes of all the tasks;

the first establishing module is used for establishing an active node by using any machine resource of each process node and the process, wherein the process node and the active node are in one-to-many relationship;

the second construction module is used for constructing undirected disjunctive edge constraints according to the sequence of the process nodes and the active nodes;

the third construction module is used for constructing a non-directional disjunctive edge set;

the fourth construction module is used for creating directed connecting edges based on the process machining routes of all tasks, constructing a directed graph model of the process nodes and representing sequence constraint of process machining starting time;

and the fifth construction module is used for constructing a first disjunct graph model based on the undirected disjunct edge constraint and the directed graph model of the process node.

Optionally, the third building module is further configured to:

Optionally, the fourth building block is further configured to:

wherein i represents a machining process sequence of task j;

represents the step O _ji The start processing time of (2); />

A preamble process O representing the task _j(i-1) The start processing time of (2); />

Represents the step O _ji The processing time of (2).

Optionally, the second building unit includes:

the first establishing module is used for establishing corresponding material nodes and establishing material connecting edges based on materials consumed by any process node and materials produced by any process node;

the first calculation module is used for calculating the production duration of the process node or the activity node according to the process takt time and the quantity of consumed or produced materials in the takt time;

the second calculation module is used for calculating the duration and the material quantity of all the process nodes or the movable nodes in sequence according to the process sequence route;

and the second establishing module is used for establishing correlation constraint between the processes among different tasks.

Optionally, the dependency constraint includes any of:

starting machining in the former process and starting in the latter process;

the former process is started, and the latter process is ended;

the former process is finished, and the latter process is started;

the former step is finished, and the latter step is finished.

Optionally, the modeling unit is further configured to:

According to a third aspect of an embodiment of the present disclosure, there is provided an electronic apparatus including:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement a method of extended disjunctive graph model based plant scheduling modeling as claimed in any one of the above first aspects.

According to a fourth aspect of the embodiments of the present disclosure, there is provided a storage medium, wherein instructions that, when executed by a processor of an electronic device, enable the electronic device to perform a method for plant scheduling modeling based on an extended disjunctive graph model as described in any one of the above first aspects.

The embodiment of the disclosure provides a workshop scheduling modeling method and device based on an extended disjunctive graph model, and the main technical scheme comprises the following steps: firstly, constructing a first disjunctive graph model, wherein the first disjunctive graph model is used for constructing workshop process sequence constraint and machine resource constraint; secondly, constructing a second disjunctive graph model, wherein the second disjunctive graph model is used for constructing process material constraints and workshop process correlation constraints; and finally, modeling the workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model. Compared with the prior art, the method and the device have the advantages that the first disjunctive graph model and the second disjunctive graph model are stacked, the correlation among the processes, different processes of materials and a workshop is increased, the modeled model comprises the multi-machine resource relation of the same process, the correlation among the processes and the constraint relation related to the materials, the finally solved scheduling result is closer to the actual production process, and the complex discrete manufacturing requirement is met.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a schematic flowchart of a method for plant scheduling modeling based on an extended disjunctive graph model according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of material consumed and material produced by a process node according to an embodiment of the present disclosure;

FIG. 3 is an extended disjunctive graph model provided by embodiments of the present disclosure;

FIG. 4 is a schematic flow chart diagram illustrating a method for constructing a first disjunctive graph model according to an embodiment of the disclosure;

FIG. 5 is a schematic diagram illustrating the creation of an active node from a process node according to an embodiment of the present disclosure;

FIG. 6 is a schematic flow chart diagram illustrating a method for constructing a second disjunctive graph model according to an embodiment of the disclosure;

FIG. 7 is a schematic diagram illustrating process dependency constraint types provided by an embodiment of the present disclosure;

FIG. 8 is a diagram illustrating modeling solution results provided by an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of a plant scheduling modeling apparatus based on an extended disjunctive graph model according to an embodiment of the present disclosure;

FIG. 10 is a schematic structural diagram of another plant scheduling modeling apparatus based on an extended disjunctive graph model according to an embodiment of the present disclosure;

fig. 11 is a schematic block diagram of an example electronic device provided by embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The following describes a method and an apparatus for plant scheduling modeling based on an extended disjunctive graph model according to an embodiment of the present disclosure with reference to the drawings.

Fig. 1 is a schematic flowchart of a workshop scheduling modeling method based on an extended disjunctive graph model according to an embodiment of the present disclosure.

As shown in fig. 1, the method comprises the following steps:

step 101, constructing a first disjunctive graph model, wherein the first disjunctive graph model is used for constructing workshop process sequence constraint and machine resource constraint.

In different products produced in the same workshop, each product has a plurality of working procedures which are sequentially carried out, each execution working procedure also uses corresponding machine resources, the duration of using the machine resources is determined according to product data, and the same working procedures or the same machine resources may exist among different products; therefore, the overall consideration is based on the order of the steps of each product, the machine resources used in each step, and the time of use.

And 102, constructing a second disjunctive graph model, wherein the second disjunctive graph model is used for constructing process material constraints and workshop process correlation constraints.

Referring to fig. 2, fig. 2 is a schematic diagram of a process node consuming materials and producing materials according to an embodiment of the present application, such as a process node O of a first product _ki The method comprises the steps of inputting

materials

1, 2 and 3, obtaining two

output materials

4 and 5 through process processing, wherein the output material 4 is used for processing the input material of the next process of a first product, but the output material 5 is the input material of a certain process of a second product, and when the product process is determined, the process of the second product is set to be O of the first product _ji And (5) after the node.

And 103, modeling workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model.

Referring to fig. 3, fig. 3 is a diagram illustrating an extended disjunctive graph model according to an embodiment of the present application; modeling is carried out according to the first disjunctive graph and the second disjunctive graph, comprehensive analysis is carried out, and during analysis, the solution can be carried out through an operation and research algorithm or a deep learning method based on a neural network and the like.

The embodiment of the disclosure provides a workshop scheduling modeling method based on an extended disjunctive graph model, which mainly adopts the technical scheme that: firstly, constructing a first disjunctive graph model, wherein the first disjunctive graph model is used for constructing workshop process sequence constraint and machine resource constraint; secondly, constructing a second disjunctive graph model, wherein the second disjunctive graph model is used for constructing process material constraints and workshop process correlation constraints; and finally, modeling the workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model. Compared with the prior art, the method and the device have the advantages that the first disjunctive graph model and the second disjunctive graph model are stacked, the correlation among the processes, different processes of materials and a workshop is increased, the modeled model comprises the multi-machine resource relation of the same process, the correlation among the processes and the constraint relation related to the materials, the finally solved scheduling result is closer to the actual production process, and the complex discrete manufacturing requirement is met.

When a first disjunctive graph model is constructed, construction is carried out according to the sequence constraint and the resource constraint of each procedure of different products; referring to fig. 4, fig. 4 is a schematic flowchart illustrating a method for constructing a first disjunctive graph model according to an embodiment of the present disclosure, including:

step 201, establishing corresponding process nodes according to the processing processes of all tasks.

For example, if the product a has three machining processes, three process nodes A1, A2, and A3 are established, and each process node corresponds to a machining process of the product a, respectively.

Step 202, creating an active node by using any machine resource for each process node and the process, wherein the process node and the active node are in a one-to-many relationship.

For the same process, there may be multiple machine resources capable of completing, please refer to fig. 5, where fig. 5 is a schematic diagram illustrating the creation of an active node according to a process node according to an embodiment of the present application; wherein, O _ji Is a process node, and the current process node has two machine resources m0 and m1, two active nodes O can be generated _jim0 And O _jim1 Specifically, the number of process nodes and machine resources may be set according to, but is not limited to, the specific embodiment of the present application.

And 203, constructing undirected disjunctive edge constraints according to the sequence of the process nodes and the active nodes.

Process node O _ji And active node O _jim For one-to-many relationships, the active node is represented by a 0-1 variable

And whether the data is valid, wherein 0 represents invalid and 1 represents valid. />

For any step O _ji Its corresponding active node is only 1 valid, and the constraint expression is

Process node O _ji Start of machining time of

i and active node O _jim Is started to process time>

The relationship is as follows:

process node O _ji Length of working

And active node O _jim Processing time in (1)>

The relationship is as follows:

and step 204, constructing an undirected disjunctive edge set.

traversing all the active nodes, grouping according to the same machine resource, establishing an undirected disjunctive edge set for any two active nodes in the group, and representing the resource constraint E of the active nodes _MO The constraints are:

wherein the content of the first and second substances,

and &>

Respectively represent activity O _jim And Activity O _htm The start processing time of (2); />

And &>

Respectively represent activity O _jim And Activity O _htm The processing time of (2); m is an infinite constant, and serves as a penalty term, in order to facilitate understanding of the penalty term, the penalty term is set according to a production scheduling duration, for example, production scheduling is performed according to a month, the penalty term may be set to a constant (120 days) corresponding to 3 months, or may be set to a larger value, but the calculation is slower, and the penalty term is not specifically limited in this embodiment.

Resource constraint E of active nodes _MO In a restriction if

When the value is 0, the formula (1) is always true, and the formula (2) is true;

if it is not

When the value is 1, the formula (1) is established, and the formula (2) is always established;

showing temporal precedence constraints if two processes are produced on the same equipment.

And step 205, creating directed connecting edges based on the process machining routes of all tasks, constructing a directed graph model of the process nodes, and representing sequence constraints of the process machining starting time.

Process route based on each task (for example, J1 processing needs to go through three procedures O ₁₁ 、O ₁₂ 、O ₁₃ ) Creating a directed connecting edge E, constructing a directed graph model of a process node, and representing the sequence constraint of the process processing starting time:

wherein i represents a machining process sequence of task j;

represents the step O _ji The start processing time of (2); />

A preliminary step O representing the task _j(i-1) The start processing time of (2); />

Represents a step O _ji The processing time of (2).

And step 206, constructing a first disjunct graph model based on the undirected disjunct edge constraint and the directed graph model of the process node.

When the second disjunctive graph model is constructed, the second disjunctive graph model can be constructed according to material constraints and process related constraints; referring to fig. 6, fig. 6 is a flowchart illustrating a method for constructing a second disjunctive graph model according to an embodiment of the present application, including:

and 301, establishing corresponding material nodes and constructing material connecting edges based on the materials consumed and produced by any process node.

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

For process and material nodes, process O is known _ji Time of beat

And the amount of material consumed or produced during that beat time. If the total demand quantity of the materials 4 required to be consumed by the subsequent node is->

Then a production time length of the process node (or active node) can be calculated which is/is ≥ h>

And 303, sequentially calculating the duration and the material quantity of all process nodes or movable nodes according to the process sequence route.

According to the process sequence route, the time length and the material quantity of all process nodes (or movable nodes) can be calculated in sequence. Process O _ji At the time of the beat

In the interior, the consumed material is->

The number of (A) is: />

Produce material->

The number of (A) is: />

When the material is required>

Is counted as->

When so, process activity O _jim Processing time in (1)>

The calculation formula is as follows:

consumed material

The total amount of (c) is:

at step 304, dependency constraints between processes between different tasks are established.

In the production process, besides the process processing sequence constraint in the same task, the process between different tasks may have correlation constraint, and the edge E is used _OO The common constraint types are shown below (see fig. 7 for details): if the earliest start time and earliest end time of a process are subject to pre-correlation processes, two processes O for different tasks _ht And O _ji The types of their mutual constraints are as follows:

referring to fig. 7, fig. 7 is a schematic diagram illustrating a process dependency constraint type according to an embodiment of the present application;

starting machining in the former process and starting in the latter process; please refer to fig. 7 for SS condition: preceding Process O _ji Start (START), post-Process O _ht START (START):

when the process O is performed _ji After the start of working for d time, the process O is allowed _ht Starting processing, wherein a constraint expression is as follows:

wherein

And &>

The start time of the preceding and following steps is shown, and d is a time constraint.

The former process is started, and the latter process is ended; please refer to SE in fig. 7: START of preceding process (START), END of following process (END):

when the process O is carried out _ji After the start of the working d time, the process O is allowed _ht And (3) finishing the processing, wherein the constraint expression is as follows:

is finished to obtain

The former process is finished, the latter process is started, please continue to refer to the ES condition shown in fig. 7: END of pre-process (END), START of post-process (START):

when the process O is performed _ji After finishing the working d time, allowing the procedure O _ht Starting processing, wherein a constraint expression is as follows:

the former process is finished, and the latter process is finished, please refer to the EE condition in fig. 7: END of preceding step (END), END of subsequent step (END):

when the process O is performed _ji After finishing the working d time, allowing the procedure O _ht And completing, wherein the constraint expression is as follows:

is finished to obtain

In an implementation manner of the embodiment of the present application, the first disjunctor model and the second disjunctor model are respectively graph-constructed to complete modeling for workshop scheduling, and after the modeling is completed, data required by the models are input into the trained models, so that the process sequence and the process operation duration of each production line can be known, as shown in fig. 8, where fig. 8 is a schematic diagram of a modeling solution result provided in the embodiment of the present application; and visually displaying the solving result.

Corresponding to the workshop scheduling modeling method based on the extended disjunctive graph model, the invention also provides a workshop scheduling modeling device based on the extended disjunctive graph model. Since the device embodiment of the present invention corresponds to the method embodiment described above, details that are not disclosed in the device embodiment may refer to the method embodiment described above, and are not described again in the present invention.

Fig. 9 is a schematic structural diagram of an apparatus for plant scheduling modeling based on an extended disjunctive graph model according to an embodiment of the present disclosure, as shown in fig. 9, including:

a first constructing unit 41, configured to construct a first disjunctive graph model, where the first disjunctive graph model is used to construct a workshop process sequence constraint and a machine resource constraint;

a second constructing unit 42, configured to construct a second disjunctive graph model, where the second disjunctive graph model is used to construct process material constraints and workshop process correlation constraints;

a modeling unit 43, configured to model a plant schedule based on the first disjunctive graph model and the second disjunctive graph model.

The embodiment of the disclosure provides a workshop scheduling modeling device based on an extended disjunctor model, which adopts the main technical scheme that: firstly, constructing a first disjunctive graph model, wherein the first disjunctive graph model is used for constructing workshop process sequence constraint and machine resource constraint; secondly, constructing a second disjunctive graph model, wherein the second disjunctive graph model is used for constructing process material constraints and workshop process correlation constraints; and finally, modeling the workshop scheduling based on the first disjunctive graph model and the second disjunctive graph model. Compared with the prior art, the method and the device have the advantages that the first disjunctive graph model and the second disjunctive graph model are stacked, the correlation among the processes, the materials and different processes of a workshop is increased, the modeled model comprises the relation of resources of multiple machines in the same process, the correlation among the processes and the constraint relation related to the materials, the finally solved scheduling result is closer to the actual production process, and the complex discrete manufacturing requirement is met.

Further, in a possible implementation manner of this embodiment, as shown in fig. 10, the first building unit 41 includes:

the establishing module 411 is used for establishing corresponding process nodes according to the processing processes of all the tasks;

a first creation module 412, configured to create an active node from each process node and the process using any machine resource, wherein the process node and the active node are in a one-to-many relationship;

a second constructing module 413, configured to construct an undirected disjuncted edge constraint according to the sequence of the process nodes and the active nodes;

a third construction module 414 for constructing a directionless disjunctive edge set;

a fourth construction module 415, configured to create a directed connection edge based on a process route of each task, construct a directed graph model of the process node, and represent sequence constraints of process starting times;

a fifth constructing module 416, configured to construct a first disjunctive graph model based on the undirected disjunctive edge constraints and the directed graph model of the process node.

Further, in a possible implementation manner of this embodiment, the third building module 414 is further configured to:

Further, in a possible implementation manner of this embodiment, the fourth building module 415 is further configured to:

wherein i represents a machining process sequence of task j;

represents a step O _ji The start processing time of (2); />

Represents a step O _ji The processing time of (2).

Further, in a possible implementation manner of this embodiment, as shown in fig. 10, the second constructing unit 42 includes:

a first establishing module 421, configured to establish a corresponding material node based on a material consumed by any process node and a material produced by any process node, and establish a material connecting edge;

the first calculating module 422 is configured to calculate a production duration of the process node or the active node according to a takt time of the process and a quantity of consumed or produced materials in the takt time;

the second calculating module 423 is used for sequentially calculating the duration and the material quantity of all the process nodes or the active nodes according to the process sequence route;

a second establishing module 424 for establishing dependency constraints between processes between different tasks.

Further, in a possible implementation manner of this embodiment, the relevance constraint includes any one of the following:

starting machining in the former process and starting in the latter process;

the former process is started, and the latter process is finished;

the former process is finished, and the latter process is started;

the former process is finished, and the latter process is finished.

Further, in a possible implementation manner of this embodiment, the modeling unit 43 is further configured to:

It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of the present embodiment, and the principle is the same, and the present embodiment is not limited thereto.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 11 shows a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may 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 meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 11, the apparatus 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 into 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 calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An I/O (Input/Output) interface 505 is also connected to the bus 504.

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, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, 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.

The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing Unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (graphics Processing Unit), various dedicated AI (Artificial Intelligence) computing chips, various computing Units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable Processor, controller, microcontroller, and the like. The computing unit 501 performs the various methods and processes described above, such as a method of plant scheduling modeling based on an extended disjunctive graph model. For example, in some embodiments, a method of plant scheduling modeling based on an extended disjunctive graph model may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto 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 by any other suitable means (e.g., by means of firmware) to perform the aforementioned method of plant scheduling modeling based on an extended disjunctive graph model.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, FPGAs (Field Programmable Gate arrays), ASICs (Application-Specific Integrated circuits), ASSPs (Application Specific Standard products), SOCs (System On Chip), CPLDs (Complex Programmable Logic devices), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods 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, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The 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, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only-Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, 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 may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can 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), internet, and blockchain Network.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware and software technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A workshop scheduling modeling method based on an extended disjunctive graph model is characterized by comprising the following steps:

2. The method of claim 1, wherein constructing the first disjunctive graph model comprises:

constructing an undirected disjunctive edge set;

establishing a directed connection edge based on the process machining route of each task, constructing a directed graph model of the process node, and representing sequence constraint of process machining starting time;

3. The method of claim 2, wherein constructing the undirected disjunctive edge set comprises:

for any machine resource, representing the precedence order relation between the first active node and the second active node by using a variable 0 or 1;

4. The method of claim 3, wherein the creating a directed connecting edge based on the process route of each task, constructing a directed graph model of the process nodes, and wherein characterizing the order constraint of the process start time comprises:

wherein i represents a sequence of machining steps of a task;

represents the step O _ji The start processing time of (2); />

Represents a step O _ki The processing time of (2).

5. The method of claim 2, wherein constructing the second disjunctive graph model comprises:

establishing corresponding material nodes and establishing material connecting edges based on materials consumed and produced by any process node;

calculating the production duration of the process node or the activity node according to the process takt time and the quantity of the consumed or produced materials in the takt time;

sequentially calculating the duration and the material quantity of all process nodes or movable nodes according to a process sequence route;

and establishing correlation constraints among the procedures among different tasks.

6. The method of claim 5, wherein the relevance constraint comprises any of:

starting machining in the former process and starting in the latter process;

the former process is started, and the latter process is ended;

the former process is finished, and the latter process is started;

the former process is finished, and the latter process is finished.

7. The method according to any one of claims 1-6, wherein said modeling a plant schedule based on said first and second disjunctive graph models comprises:

8. An apparatus for modeling plant scheduling based on an extended extraction graph model, comprising:

9. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the method of extended disjunctive graph model based plant scheduling modeling according to any of claims 1 to 7.

10. A storage medium having instructions that, when executed by a processor of an electronic device, enable the electronic device to perform the method of extended disjunctive graph model based plant scheduling modeling of any of claims 1 to 7.