CN115438931B - Method, device, equipment and medium for scheduling assembly operation of production line - Google Patents

Method, device, equipment and medium for scheduling assembly operation of production line Download PDF

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CN115438931B
CN115438931B CN202211009734.7A CN202211009734A CN115438931B CN 115438931 B CN115438931 B CN 115438931B CN 202211009734 A CN202211009734 A CN 202211009734A CN 115438931 B CN115438931 B CN 115438931B
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CN115438931A (en
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田长乐
蓝玉龙
谢颖
范进步
刘春�
贺长征
江敏
孔卫光
李帅
郑和银
陈亮
喻龙
许亚鹏
郝龙
陶萍
薛广库
唐雪
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Chengdu Aircraft Industrial Group Co Ltd
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Abstract

The application discloses a method, a device, equipment and a medium for scheduling assembly operations of a production line, relates to the technical field of aircraft assembly operation scheduling, and is used for solving the technical problem that the scheduling of the assembly operations of the production line of an aircraft cannot meet the assembly requirements in the prior art; wherein the assembly operation is an assembly operation for assembling the target aircraft; constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembly period model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation; constructing a non-cooperative game model based on the assembly period model and the carbon emission model; and scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model. By the technical scheme, the assembly operation scheduling of the aircraft production line can meet the assembly requirement.

Description

Method, device, equipment and medium for scheduling assembly operation of production line
Technical Field
The application relates to the technical field of aircraft assembly job scheduling, in particular to a production line assembly job scheduling method, device, equipment and medium.
Background
The aircraft assembly using the robots as assembly carriers can meet the requirement of the flexible assembly technology of the simultaneous assembly of different types of aircraft, and the aircraft assembly technology becomes the development trend of a new generation of intelligent flexible production line of aircraft. Meanwhile, as a core technology for assembly operation, robots and assembly resource decision of an aircraft assembly production line, the green efficient production line assembly operation scheduling technology is an optimal way for aviation manufacturing enterprises to realize efficient delivery, energy conservation and emission reduction win-win.
However, the scheduling of assembly operations on the aircraft production line in the prior art cannot meet the assembly requirements of flexibility and emission reduction.
Disclosure of Invention
The main purpose of the application is to provide a method, a device, equipment and a medium for scheduling assembly operations of a production line, and aims to solve the technical problem that the scheduling of the assembly operations of the production line of an airplane in the prior art cannot meet the assembly requirements.
To achieve the above object, a first aspect of the present application provides a method for scheduling assembly jobs of a production line, the method comprising:
constructing a scheduling framework of a plurality of assembly operations based on a target aircraft production line; wherein the plurality of assembly operations are assembly operations involved in assembling a target aircraft using the target aircraft production line;
constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembly period model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation;
constructing a non-cooperative game model based on the assembly period model and the carbon emission model;
and scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model.
Preferably, the non-cooperative game model comprises a strategy aggregation module, a benefit function module and a Nash equilibrium solution module; the scheduling of the assembly operations of the target aircraft production line based on the non-cooperative game model includes:
obtaining a solution of the strategy collection module, a solution of the benefit function module and a solution of the Nash equilibrium solution module;
and obtaining a Nash equilibrium solution of the non-cooperative game model through a fitness function and a local search algorithm based on the solution of the strategy collection module, the solution of the benefit function module and the solution of the Nash equilibrium solution module.
Preferably, the obtaining the nash equilibrium solution of the non-cooperative game model through the fitness function and the local search algorithm based on the solution of the policy set module, the solution of the profit function module and the solution of the nash equilibrium solution module includes:
the Nash equilibrium solution of the non-cooperative game model is obtained through the following local search algorithm:
wherein,representing the first ideal strategy, +.>Represents the nth ideal strategy, +.>Representing any one strategy->For each strategy->Reason for representing the ith assembly jobTime of assembly, ->Indicating the ideal total carbon emission amount, T, during the operation of the robot i Assembly time, CE, for the ith assembly job i For the total carbon emission during robot operation, < >>Represents fitness function omega a And->All represent equalization factors, F i Indicating the target degree of balance for the ith assembly job,/-)>U i 、H i And->Each representing an intermediate variable, and λ and ε each representing a convergence threshold.
Preferably, the fitness function is obtained by the following formula:
where i=1, … N, a=1, … numtop, b=1, … maI, a represents the number of iterations, b represents the population number;an assembly cycle representing the i-th assembly operation in the b-th chromosome in the a-th iteration,/->Representing the amount of carbon emissions from the assembly of the ith assembly job in the h chromosome in the a-th iteration.
Preferably, the solution of the policy set module is obtained by the following relation:
wherein M is ij Operational robot set selectable for the j-th assembly outline of the i-th assembly job, j e n i ，i∈f；
Obtaining a solution of the benefit function module by the following relation:
Y＝[T i (x)，CE i (x)]。
preferably, the solution of the nash equalization solution module is obtained by the following relation:
preferably, the constructing an assembly cycle model and a carbon emission model based on the scheduling framework includes:
the assembly cycle model is obtained by the following relation:
wherein T is i For the assembly time of the ith assembly job,starting time, t, of the jth assembly line on the kth robot for the ith assembly job ijk Assembly time on the kth robot for the jth assembly outline of the ith assembly job.
Preferably, the constructing an assembly cycle model and a carbon emission model based on the scheduling framework further includes:
the carbon emission model is obtained by the following relation:
wherein CE is i Is the total carbon emission when the robot is running,the amount of carbon emissions for the i-th assembly operation,carbon emission amount, P, for the j-th assembly outline of the i-th assembly job ijk Assembly power on the kth robot for the jth assembly line of the ith assembly job, alpha being the carbon emission factor, +.>Transport carbon emissions for the ith assembly operation, P v For the transport power of the automated guided vehicle tp (j-1,l),(j,m) For the transport time of the ith robot to the mth robot or for the transport time of the j-1 th assembly outline to the j-th assembly outline, < +.>Idle power for kth robot, < >>Assembly completion time on the kth robot for the jth assembly outline of the ith assembly job,/->Ideal completion time for the ith assembly operation, CE idle Carbon emission amount for total idling of all robots, +.>For the carbon emission amount allocated to the i-th assembly operation.
In a second aspect, the present application provides a production line assembly job scheduling apparatus, the apparatus comprising:
the first construction module is used for constructing a scheduling frame of a plurality of assembly operations based on a target aircraft production line; wherein the assembly operation is an assembly operation for assembling the target aircraft;
the second construction module is used for constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembly period model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation;
the third building module is used for building a non-cooperative game model based on the assembly period model and the carbon emission model;
and the scheduling module is used for scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model.
In a third aspect, the present application provides a computer device comprising a memory, in which a computer program is stored, and a processor executing the computer program to implement the method described in the embodiments.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method described in the embodiments.
Through above-mentioned technical scheme, this application has following beneficial effect at least:
the method comprises the steps of firstly constructing a scheduling frame of a plurality of assembly operations based on a target aircraft production line; wherein the plurality of assembly operations are assembly operations involved in assembling a target aircraft using the target aircraft production line; then, based on the scheduling framework, constructing an assembly period model and a carbon emission model; the assembly period model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation; constructing a non-cooperative game model based on the assembly period model and the carbon emission model; and finally, scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model. That is, the technical scheme of the method and the device can obtain the assembly period of the aircraft on the production line through the assembly period model, can obtain the carbon emission amount assembled on the production line by the aircraft through the carbon emission model, and can obtain the target assembly mode which enables the assembly period of the aircraft on the production line to be shortest and simultaneously takes account of the minimum carbon emission amount through the non-cooperative game model, so that the assembly operation can be scheduled by utilizing the target assembly mode, and the aircraft can be assembled through the scheduled assembly operation, thereby realizing the demand target with the minimum assembly period and the minimum assembly carbon emission amount, providing direction and data support for the dispatching management and control of the aircraft assembly resources, and further enabling the dispatching of the assembly operation of the aircraft production line to meet the assembly requirement.
Drawings
FIG. 1 is a schematic diagram of a computer device in a hardware operating environment according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of scheduling assembly jobs in a production line in accordance with an embodiment of the present application;
FIG. 3 is a flowchart of a specific implementation method of step S13;
FIG. 4 is a schematic diagram of a Nash equilibrium solution obtained by solving in the embodiment of the present application;
FIG. 5 is a graph of the variation of the ideal values of the assembly process and the assembled carbon emissions versus calculated values using the present method in accordance with the embodiments of the present application;
FIG. 6 is a graph of variation of the ideal values for the assembly operation and assembly cycle versus calculated values using the present method in accordance with an embodiment of the present application;
fig. 7 is a schematic diagram of a production line assembly job scheduling apparatus according to an embodiment of the present application.
The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The intelligent assembly of the number of the airplanes by using the robots as the assembly carriers can meet the requirement of the flexible assembly technology for simultaneously assembling different types of airplanes, and the intelligent assembly technology has become the development trend of a new generation of intelligent flexible production line of the number of the airplanes. Meanwhile, as a core technology for assembly operation, robots and assembly resource decision of an aircraft assembly production line, the green efficient production line assembly operation scheduling technology is an optimal way for aviation manufacturing enterprises to realize efficient delivery, energy conservation and emission reduction win-win. Traditional aircraft assembly production line scheduling mainly focuses on modeling of production line balance, modeling of distribution of production line workers, modeling of production line material distribution and the like, and solving of models by improving different algorithms, however, as new generation aircraft production line assembly develops towards green, intelligent and flexible directions, traditional overall production line scheduling models and algorithms are difficult to adapt to the intelligent and flexible demands of the aircraft production line. Therefore, under the condition that assembly line resources are limited, a production line scheduling method capable of meeting the assembly period requirements of different types of aircrafts and considering the greening indexes is urgently needed. However, the current assembly operation of the aircraft production line cannot well consider the assembly period of the aircraft and the carbon emission in the assembly process, so that the scheduling of the assembly operation of the aircraft production line cannot meet the assembly requirement.
In order to solve the above technical problems, the present application provides a method, an apparatus, a device, and a medium for scheduling assembly operations of a production line, and before introducing a specific technical solution of the present application, a hardware operating environment related to an embodiment of the present application is introduced.
Referring to fig. 1, fig. 1 is a schematic diagram of a computer device structure of a hardware running environment according to an embodiment of the present application.
As shown in fig. 1, the computer device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is not limiting of a computer device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and an electronic program may be included in the memory 1005 as one type of storage medium.
In the computer device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the computer device of the present invention may be provided in the computer device, where the computer device invokes the production line assembly job scheduling device stored in the memory 1005 through the processor 1001, and executes the production line assembly job scheduling method provided in the embodiment of the present application.
Referring to fig. 2-3, based on the hardware environment of the foregoing embodiment, an embodiment of the present application provides a method for scheduling assembly jobs of a production line, the method including:
s10: constructing a scheduling framework of a plurality of assembly operations based on a target aircraft production line; the plurality of assembly operations are assembly operations included in the assembly of the target aircraft by using the target aircraft production line.
In a specific implementation process, the target aircraft refers to an aircraft assembled on a production line, the assembly operation refers to an instructive operation for assembling the target aircraft by adopting a reasonable method in order to fully utilize assembly resources, and in particular, the method can be provided by an assembly shop, and the scheduling frame refers to a frame for performing assembly operations on a flexible production line consisting of a plurality of robots from assembly operations of a plurality of different target aircraft models. Specifically, the method can be used for products produced on a production line, the application is presented by taking an airplane as an example, and a scheduling frame for assembling operations on a flexible production line consisting of a plurality of robots from a plurality of different machine types is provided for the digital intelligent flexible production line.
In this embodiment, for better study of the assembly job scheduling for the target aircraft, the basic scheduling framework has the following assumed conditions:
1. an aircraft intelligent flexible production line comprises m robots, wherein the total number of the robots is n from each machine type assembly operation, each assembly operation has ni assembly AO (assembly outline), and the ni assembly outline of the same assembly operation has a time sequence relationship.
2. At the same time, each robot can only assemble one assembly outline; at the same time, each assembly outline can only be operated by one robot.
3. The production line has flexibility, one assembly outline can be assembled by a plurality of robots, and the assembly time and the assembly power of each robot have difference due to different performances of each robot.
4. The electric automatic guided vehicle is used for transferring and assembling work tasks and materials among the robots, and the power and the transferring time of the electric automatic guided vehicle are measured in advance.
S11: constructing an assembly period model and a carbon emission model based on the scheduling framework; wherein the assembly cycle model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation.
In the specific implementation process, an assembly period model and a carbon emission model of the assembly operation of each model are constructed based on the scheduling framework, and the assembly period model and the carbon emission model are used as assembly targets of the assembly operation of each model.
Specifically, the assembly cycle model is obtained by the following relation:
wherein T is i For the assembly time of the ith assembly job,starting time, t, of the jth assembly line on the kth robot for the ith assembly job ijk Assembly time on the kth robot for the jth assembly outline of the ith assembly job.
Alternatively, the carbon emission model is obtained by the following relation:
wherein CE is i Is the total carbon emission when the robot is running,the amount of carbon emissions for the i-th assembly operation,carbon emission amount, P, for the j-th assembly outline of the i-th assembly job ijk Assembly power on the kth robot for the jth assembly line of the ith assembly job, alpha being the carbon emission factor, +.>Transport carbon emissions for the ith assembly operation, P v For the transport power of the automated guided vehicle tp (j-1,l),(j,m) For the transport time of the ith robot to the mth robot or for the transport time of the j-1 th assembly outline to the j-th assembly outline, < +.>Idle power for kth robot, < >>Assembly completion time on the kth robot for the jth assembly outline of the ith assembly job,/->Ideal completion time for the ith assembly operation, CE idle Carbon emission amount for total idling of all robots, +.>For the carbon emission amount allocated to the i-th assembly operation.
S12: and constructing a non-cooperative game model based on the assembly period model and the carbon emission model.
In the specific implementation process, each model is taken as a participation main body (office man), the assembly working period and the assembly carbon emission of each model are taken as a profit function, an optional assembly robot is taken as a game strategy, and a non-cooperative game theory is utilized to construct a non-cooperative game model of multi-machine type multi-assembly operation.
S13: and scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model. The non-cooperative game model comprises a strategy aggregation module, a benefit function module and a Nash equilibrium solution module;
s131: and obtaining a solution of a strategy aggregation module, a solution of an assembly operation module, a solution of a benefit function module and a solution of a Nash equilibrium solution module in the non-cooperative game model.
The assembly operation module is obtained by the following relation:
W＝{J i |O ij ,1≤i≤N,1≤j≤n i }
wherein N is the total number of assembly operations, J i For the ith assembly job, O ij Jth assembly outline, n, representing ith assembly job i Representing the total number of assembly schematics of the ith assembly job;
the policy set module is obtained by the following relation:
wherein,for each policy, i ε f; m is M ij A set of operating robots selectable for a j-th assembly outline of the i-th assembly job.
Optionally, the benefit function module is obtained by the following relation:
Y＝[T i (x)，CE i ]
the Nash equilibrium solution module is obtained by the following relation:
wherein,representing the first ideal strategy, +.>Represents the nth ideal strategy, +.>Representing any one policy.
S132: and obtaining a Nash equilibrium solution of the non-cooperative game model through a fitness function and a local search algorithm based on the solution of the strategy collection module, the solution of the benefit function module and the solution of the Nash equilibrium solution module.
In order to ensure that the finishing period of each assembly operation is balanced with the total carbon emission of assembly, a Nash equilibrium solution is achieved, and the Nash equilibrium solution of the non-cooperative game model is obtained through the following local search algorithm:
wherein,representing the first ideal strategy, +.>Represents the nth ideal strategy, +.>Representing any one strategy->For each strategy->Indicating the ideal assembly time for the ith assembly job, +.>Indicating the ideal total carbon emission amount, T, during the operation of the robot i Assembly time, CE, for the ith assembly job i For the total carbon emission during robot operation, < >>Represents fitness function omega a And->All represent equalization factors, F i Representing the ith assemblyTarget degree of balance of job, +.>U i 、H i And->Each representing an intermediate variable, and λ and ε each representing a convergence threshold.
Wherein the fitness function is obtained by the following formula:
where i=1, … N, a=1, … numtop, b=1, … maI, a represents the number of iterations, b represents the population number;an assembly cycle representing the i-th assembly operation in the b-th chromosome in the a-th iteration,/->Representing the amount of carbon emissions from the assembly of the ith assembly job in the h chromosome in the a-th iteration.
When the convergence threshold value is reached, the algorithm is terminated, a Nash equilibrium solution of the non-cooperative game model is obtained, and when the Nash equilibrium solution is obtained, the assembly operation is adopted to minimize the assembly period of the aircraft on the production line and simultaneously minimize the carbon emission.
In summary, the application provides an assembly operation scheduling decision model based on a game theory, and in order to obtain a Nash equilibrium solution of the model, provides a genetic and local search mixed solving algorithm for solving the model, ensures the acquisition of the Nash equilibrium solution of the model, realizes the minimization of an aircraft assembly period, simultaneously considers the minimization of carbon emission, models the whole carbon emission of a workshop on an assembly product, provides a novel carbon emission modeling thought, can measure the carbon emission of different assembly operations, and can find out a bottleneck carbon emission amplifying link under a carbon emission policy such as a carbon tag carbon tariff policy, thereby being beneficial to targeted control and improvement. In a word, the assembly period of the aircraft on the production line is obtained through the assembly period model, the carbon emission amount of the aircraft assembled on the production line can be obtained through the carbon emission model, carbon emission data can be effectively provided, what assembly operation can be obtained through the non-cooperative game model, the assembly period of the aircraft on the production line is shortest, meanwhile, the carbon emission amount is minimum, the assembly operation can be reversely scheduled, the aircraft is assembled through the scheduled assembly operation, the requirement target of the minimum assembly period and the minimum assembly carbon emission amount can be achieved, the direction and the data support are provided for the dispatching and control of the aircraft assembly resources, and the dispatching of the assembly operation of the aircraft production line can meet the assembly requirement.
The effectiveness of the method is demonstrated below in connection with specific examples, which are as follows:
step one: the virtual assembly line is constructed, and assuming that a certain number of intelligent and flexible production lines are constructed with 8 assembly robots, the idle power of each assembly robot is shown in table 1, and the transfer time between each assembly robot is shown in table 2.
Table 1 no-load power of assembly robot
M 1 2 3 4 5 6 7 8
P(kw) 2.12 2.41 3.11 1.00 2.00 2.31 3.00 2.50
TABLE 2 transfer time between Assembly robots
Step two: the assembly line can simultaneously assemble the assembly operation of 8 types of aircrafts, each assembly operation has 6 AO's, the time sequence relation of AO has strict priority relation, the specific AO relation is 1- & gt 2- & gt 3- & gt 4- & gt 5- & gt 6, and meanwhile, each AO's feasible assembly robot and assembly operation power are shown in a table 3.
TABLE 3 Assembly robot and Assembly operating Power per AO viable
In table 3, each cell has 3 brackets, the first bracket represents the set of possible assembly robots for the AO, the second bracket represents the set of assembly times for the AO on the corresponding robot, and the third bracket represents the set of assembly powers for the AO on the corresponding robot.
Step three: based on each model and algorithm constructed, algorithm parameters are set according to the inputs of steps one and two, as shown in table 4 below:
table 4 algorithm parameters
Parameter name Value of
Population group 40
Maximum number of iterations 200
Crossover probability 0.8
Probability of variation 0.1
P v 1.5
G 3
n 1000
n_namx 100
ε 0.1
η 0.1
λ 0.1
Step four: the Gantt chart of this model is output, and as shown in FIG. 4, the numbers in the boxes in FIG. 4 represent the assembly operations, the number of times the numbers appear in turn represents what number of assembly operations, and η represents the convergence threshold.
Step five: comparing the solution obtained with the ideal assembly period and the assembly carbon emission of each assembly operation, as shown in fig. 5 and 6, the unit of the ordinate in fig. 5 is kg, and the unit of the ordinate in fig. 6 is min, the solution obtained by the model can be seen to conform to the trend of the ideal solution, so that the effectiveness of the method is proved, namely, the method can optimize the assembly period and the assembly carbon emission of each assembly operation at the same time, and the scheduling of the assembly operation of the aircraft production line can meet the assembly requirement.
In another embodiment, as shown in fig. 7, based on the same inventive concept as the previous embodiment, an embodiment of the present application further provides a warehouse logistics distribution path planning apparatus, including:
the first construction module is used for constructing a scheduling frame of a plurality of assembly operations based on a target aircraft production line; wherein the assembly operation is an assembly operation for assembling the target aircraft;
the second construction module is used for constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembly period model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation;
the third building module is used for building a non-cooperative game model based on the assembly period model and the carbon emission model;
and the scheduling module is used for scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model.
It should be noted that, each module in the assembly job scheduling device of the production line in this embodiment corresponds to each step in the assembly job scheduling method of the production line in the foregoing embodiment one by one, so the specific implementation manner and the achieved technical effect of this embodiment may refer to the implementation manner of the assembly job scheduling method of the production line, and will not be repeated herein.
Furthermore, in an embodiment, the present application also provides a computer device, which includes a processor, a memory, and a computer program stored in the memory, which when executed by the processor, implements the method in the foregoing embodiment.
Furthermore, in an embodiment, the present application also provides a computer storage medium, on which a computer program is stored, which when being executed by a processor, implements the method in the foregoing embodiment.
In some embodiments, the computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; but may be a variety of devices including one or any combination of the above memories. The computer may be a variety of computing devices including smart terminals and servers.
In some embodiments, the executable instructions may be in the form of programs, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and they may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
As an example, the executable instructions may, but need not, correspond to files in a file system, may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a hypertext markup language (HTML, hyper Text Markup Language) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
As an example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices located at one site or, alternatively, distributed across multiple sites and interconnected by a communication network.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising several instructions for causing a multimedia terminal device (which may be a mobile phone, a computer, a television receiver, or a network device, etc.) to perform the method described in the embodiments of the present application.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.

Claims (9)

1. A method of scheduling assembly operations for a production line, the method comprising:
constructing a scheduling framework of a plurality of assembly operations based on a target aircraft production line; wherein the plurality of assembly operations are assembly operations involved in assembling a target aircraft using the target aircraft production line;
constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembly period model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation; the assembly cycle model is constructed by the following relation:
wherein,assembly time for the ith assembly operation, +.>Starting time of the jth assembly outline on the kth robot for the ith assembly job, +.>The assembly time of the jth assembly outline on the kth robot for the ith assembly job;
the carbon emission model is constructed by the following relation:
wherein,for the total carbon emission during robot operation, < >>The amount of carbon emissions for the i-th assembly operation,assembly carbon emission for the j-th assembly outline of the i-th assembly job, +.>Assembly power on the kth robot for the jth assembly line of the ith assembly job, +.>For the ith assembly jobAssembly time of the j-th assembly outline on the k-th robot, +.>Is carbon emission factor, < >>Transport carbon emissions for the ith assembly operation,/->For the transport power of the automated guided vehicle, +.>For the transport time of the ith robot to the mth robot or for the transport time of the j-1 th assembly outline to the j-th assembly outline, < +.>Idle power for kth robot, < >>Assembly start time of the jth assembly outline on the kth robot for the ith assembly job, +.>Assembly completion time on the kth robot for the jth assembly outline of the ith assembly job,/->For the actual completion time of the ith assembly job, +.>Ideal finishing time for the ith assembly operation, +.>Carbon emission amount for total idling of all robots, +.>Carbon emission amount for allocation to the i-th assembly operation;
constructing a non-cooperative game model based on the assembly period model and the carbon emission model;
and scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model.
2. The production line assembly job scheduling method of claim 1, wherein the non-cooperative game model comprises a strategy aggregation module, a profit function module and a nash equilibrium solution module;
the scheduling of the assembly operations of the target aircraft production line based on the non-cooperative game model includes:
obtaining a solution of the strategy collection module, a solution of the benefit function module and a solution of the Nash equilibrium solution module;
and obtaining a Nash equilibrium solution of the non-cooperative game model through a fitness function and a local search algorithm based on the solution of the strategy collection module, the solution of the benefit function module and the solution of the Nash equilibrium solution module.
3. The method of claim 2, wherein the obtaining a nash equilibrium solution of the non-cooperative game model by a fitness function and a local search algorithm based on the solution of the strategy aggregation module, the solution of the profit function module, and the solution of the nash equilibrium solution module comprises:
the Nash equilibrium solution of the non-cooperative game model is obtained through the following local search algorithm:
，i=1，2，…，N
wherein,representing the first ideal strategy, +.>Represents the nth ideal strategy, +.>Which represents an arbitrary one of the policies,for each strategy->Operational robot selectable for the j-th assembly outline of the i-th assembly operation,/->A set of operating robots selectable for a j-th assembly outline of the i-th assembly job,ideal assembly representing the ith assembly jobTime (F)>Ideal total carbon emission during robot operation, < >>Assembly time for the ith assembly operation, +.>For the total carbon emission during robot operation, < >>() Representing fitness function, ++>And->All represent equalization factors, +.>Indicating the target degree of balance for the ith assembly job,/-)>、/>、/>And->All represent intermediate variables, +.>And->All represent convergence thresholdsValues.
4. A production line assembly job scheduling method as set forth in claim 3, wherein the fitness function is obtained by the following formula:
wherein,the method comprises the steps of carrying out a first treatment on the surface of the a=1, …; b=1, …; a represents the iteration number, b represents the population number; />An assembly cycle representing the i-th assembly operation in the b-th chromosome in the a-th iteration,/->Representing the amount of carbon emissions from the assembly of the ith assembly job in the h chromosome in the a-th iteration.
5. The method for scheduling assembly line operations according to claim 3,
the solution of the policy set module is obtained by the following relation:
wherein,operational robot set selectable for the j-th assembly outline of the i-th assembly job, j e ∈ ->，i∈f；
Obtaining a solution of the benefit function module by the following relation:
。
6. the method for scheduling assembly line operations according to claim 3,
obtaining a solution of the Nash equilibrium solution module by the following relation:
。
7. a production line assembly job scheduling device, the device comprising:
the first construction module is used for constructing a scheduling frame of a plurality of assembly operations based on a target aircraft production line; wherein the assembly operation is an assembly operation for assembling the target aircraft;
the second construction module is used for constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembly period model is used for representing the time duration of each assembly operation, and the carbon emission model is used for representing the carbon emission of each assembly operation; the assembly cycle model is constructed by the following relation:
wherein,assembly time for the ith assembly operation, +.>Starting time of the jth assembly outline on the kth robot for the ith assembly job, +.>The assembly time of the jth assembly outline on the kth robot for the ith assembly job;
the carbon emission model is constructed by the following relation:
wherein,for the total carbon emission during robot operation, < >>The amount of carbon emissions for the i-th assembly operation,assembly carbon emission for the j-th assembly outline of the i-th assembly job, +.>Assembly power on the kth robot for the jth assembly line of the ith assembly job, +.>Assembly time of the jth assembly outline on the kth robot for the ith assembly job, +.>Is carbon emission factor, < >>Transport carbon emissions for the ith assembly operation,/->For the transport power of the automated guided vehicle, +.>For the transport time of the ith robot to the mth robot or for the transport time of the j-1 th assembly outline to the j-th assembly outline, < +.>Idle power for kth robot, < >>Assembly start time of the jth assembly outline on the kth robot for the ith assembly job, +.>Assembly completion time on the kth robot for the jth assembly outline of the ith assembly job,/->For the actual completion time of the ith assembly job, +.>Ideal finishing time for the ith assembly operation, +.>Carbon emission amount for total idling of all robots, +.>Carbon emission amount for allocation to the i-th assembly operation;
the third building module is used for building a non-cooperative game model based on the assembly period model and the carbon emission model;
and the scheduling module is used for scheduling the assembly operation of the target aircraft production line based on the non-cooperative game model.
8. A computer device, characterized in that it comprises a memory in which a computer program is stored and a processor which executes the computer program, implementing the method according to any of claims 1-6.
9. A computer readable storage medium, having stored thereon a computer program, the computer program being executable by a processor to implement the method of any of claims 1-6.
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