CN115438931A - Production line assembly operation scheduling method, device, equipment and medium - Google Patents

Production line assembly operation scheduling method, device, equipment and medium Download PDF

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CN115438931A
CN115438931A CN202211009734.7A CN202211009734A CN115438931A CN 115438931 A CN115438931 A CN 115438931A CN 202211009734 A CN202211009734 A CN 202211009734A CN 115438931 A CN115438931 A CN 115438931A
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CN115438931B (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 scheduling of aircraft assembly operations, and is used for solving the technical problem that the scheduling of the assembly operations of the aircraft production line in the prior art cannot meet the assembly requirements; wherein the assembling operation is an assembling operation of assembling the target aircraft; constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling 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 scheduling of the assembly operation of the aircraft production line can meet the assembly requirement.

Description

Production line assembly operation scheduling method, device, equipment and medium
Technical Field
The application relates to the technical field of airplane assembly operation scheduling, in particular to a method, a device, equipment and a medium for scheduling assembly operation of a production line.
Background
The airplane assembly by using a robot as an assembly carrier and the flexible assembly technology capable of meeting the simultaneous assembly of airplanes of different models become the development trend of a new generation of airplane intelligent flexible production line. Meanwhile, as a core technology for the assembly operation, the robot and the assembly resource decision of the aircraft assembly line, the green and efficient assembly operation scheduling technology of the production line is an optimal way for aviation manufacturers to realize win-win of efficient delivery, energy conservation and emission reduction.
However, the scheduling of the assembly operation of the aircraft production line in the prior art cannot meet the assembly requirements of flexibility and emission reduction.
Disclosure of Invention
The application mainly aims to provide a production line assembly operation scheduling method, a production line assembly operation scheduling device, production line assembly operation equipment and a production line assembly operation scheduling medium, and aims to solve the technical problem that in the prior art, scheduling of aircraft production line assembly operation cannot meet assembly requirements.
In order 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 including:
constructing a plurality of dispatching frames for assembly operation based on a target aircraft production line; wherein the plurality of assembly operations are assembly operations involved in target aircraft assembly using the target aircraft production line;
constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling 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 profit function module and a Nash equilibrium solution module; the scheduling of the assembly operation of the target aircraft production line based on the non-cooperative game model comprises the following steps:
obtaining a solution for the policy set module, a solution for the revenue function module, and a solution for the nash equilibrium module;
and obtaining the 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 income function module and the solution of the Nash equilibrium solution module.
Preferably, the 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 policy aggregation module, the solution of the revenue function module, and the solution of the nash equilibrium solution module includes:
obtaining a Nash equilibrium solution of the non-cooperative game model through the following local area search algorithm:
Figure BDA0003808767820000021
Figure BDA0003808767820000022
Figure BDA0003808767820000023
Figure BDA0003808767820000024
Figure BDA0003808767820000031
Figure BDA0003808767820000032
wherein the content of the first and second substances,
Figure BDA0003808767820000033
it is shown that the first of the desired strategies,
Figure BDA0003808767820000034
it is shown that the N-th ideal strategy,
Figure BDA0003808767820000035
it is meant that any one of the policies,
Figure BDA00038087678200000315
for each of the policies, the policy server may,
Figure BDA0003808767820000036
indicating the ideal assembly time for the ith assembly job,
Figure BDA0003808767820000037
represents the ideal total carbon emission, T, of the robot during operation i Assembly time for the ith assembly job, CE i Is the total carbon emission when the robot is operating,
Figure BDA0003808767820000038
representing the fitness function, ω a And
Figure BDA0003808767820000039
all represent an equalization factor, F i Indicating the target balance for the ith assembly job,
Figure BDA00038087678200000310
U i 、H i and
Figure BDA00038087678200000311
both represent intermediate variables and both λ and ε represent convergence thresholds.
Preferably, the fitness function is obtained by the following formula:
Figure BDA00038087678200000312
wherein i =1, … N, a =1, … numtop, b =1, … maI, a represents the number of iterations, and b represents the number of populations;
Figure BDA00038087678200000313
representing the assembly cycle of the i-th assembly job in the b-th chromosome in the a-th iteration,
Figure BDA00038087678200000314
represents the assembly carbon emission of the ith assembly job in the h chromosome of the a-th iteration.
Preferably, the solution of the policy set module is obtained by the following relation:
Figure BDA00038087678200000316
wherein M is ij Set of selectable operating robots for the jth assembly schema of the ith assembly job, j ∈ n i ，i∈f；
Obtaining a solution to the revenue function module by the following relationship:
Y＝[T i (x)，CE i (x)]。
preferably, the solution of the nash equilibrium solution module is obtained by the following relation:
Figure BDA0003808767820000041
Figure BDA0003808767820000042
Figure BDA0003808767820000043
preferably, the building of an assembly cycle model and a carbon emission model based on the scheduling framework includes:
obtaining the assembly period model by the following relation:
Figure BDA0003808767820000044
wherein, T i For the assembly time of the ith assembly job,
Figure BDA0003808767820000045
starting time, t, on kth robot for jth assembly outline of ith assembly job ijk Assembly time on kth robot for jth assembly outline of ith assembly job.
Preferably, the building of an assembly cycle model and a carbon emission model based on the scheduling framework further includes:
obtaining the carbon emission model by the following relation:
Figure BDA0003808767820000046
Figure BDA0003808767820000047
Figure BDA0003808767820000048
Figure BDA0003808767820000049
Figure BDA0003808767820000051
Figure BDA0003808767820000052
wherein, CE i Is the total carbon emission when the robot is operating,
Figure BDA0003808767820000053
is as followsThe assembly carbon emission of i assembly jobs,
Figure BDA0003808767820000054
carbon emission, P, for assembly of the jth assembly line of the ith assembly work ijk The assembly power on the kth robot for the jth assembly outline of the ith assembly job, alpha is the carbon emission factor,
Figure BDA0003808767820000055
transporting carbon emissions, P, for the ith assembly operation v For transport capacity of automated guided vehicles, tp (j-1,l),(j,m) The transportation time from the ith robot to the mth robot or the transportation time from the (j-1) th assembly outline to the jth assembly outline,
Figure BDA0003808767820000056
is the idle power of the kth robot,
Figure BDA0003808767820000057
for the assembly completion time of the jth assembly outline of the ith assembly job on the kth robot,
Figure BDA0003808767820000058
ideal completion time for the ith assembly job, CE idle The carbon emissions for the total idle running of all robots,
Figure BDA0003808767820000059
is the amount of carbon emissions assigned to the ith assembly job.
In a second aspect, the present application provides a production line assembly job scheduling device, the device comprising:
the first construction module is used for constructing a plurality of dispatching frames of assembly operation based on a target aircraft production line; wherein the assembling operation is an assembling operation of 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 assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling operation;
the third construction module is used for constructing 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, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the embodiment.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program 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 plurality of dispatching frames of assembly operation based on a target aircraft production line; wherein the plurality of assembly operations are assembly operations involved in assembly of the target aircraft by using the target aircraft production line; then constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling 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 this application can obtain the assembly cycle of aircraft on the production line through the assembly cycle model, can obtain the carbon emission of aircraft assembly on the production line through the carbon emission model, can obtain the target assembly mode that makes the assembly cycle of aircraft on the production line shortest while considering carbon emission fewest through non-cooperative game model, so can utilize this target assembly mode to schedule the assembly operation, assemble the aircraft through the assembly operation of scheduling again, thereby can realize the minimum demand target of assembly cycle and assembly carbon emission, direction and data support have been provided for aircraft assembly resource scheduling management and control, and then can make the scheduling of aircraft production line assembly operation satisfy the assembly requirement.
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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 flowchart of a method for scheduling assembly operations of a production line according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating a specific implementation of step S13;
FIG. 4 is a schematic diagram of solving the obtained Nash equilibrium solution according to an embodiment of the present application;
FIG. 5 is a graph showing the variation of the ideal values of the carbon emissions for the assembly operation and assembly according to the embodiment of the present invention compared with the calculated values using the present method;
FIG. 6 is a graph showing the comparison between the ideal values of the assembling operation and the assembling cycle and the calculated values using the method according to the embodiment of the present application;
fig. 7 is a schematic view of a production line assembly job scheduling device according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The airplane intelligent assembly with the robot as the assembly carrier and the flexible assembly technology capable of meeting the simultaneous assembly of different types of airplanes become the development trend of the airplane intelligent flexible production line of the new generation. Meanwhile, as a core technology for the assembly operation, the robot and the assembly resource decision of the aircraft assembly line, the green and efficient assembly operation scheduling technology of the production line is an optimal way for aviation manufacturers to realize win-win of efficient delivery, energy conservation and emission reduction. The traditional airplane assembly line scheduling mainly focuses on modeling of line balance, distribution modeling of production line workers, modeling of production line material distribution and the like and solving of models by improving different algorithms, however, as the assembly of a new generation of airplane production line develops towards the direction of green, intellectualization and flexibility, the traditional overall production line scheduling model and algorithm are difficult to adapt to the intellectualization and flexibility requirements of the airplane production line. Therefore, under the condition of limited assembly line resources, a production line scheduling method which can simultaneously meet the assembly period requirements of airplanes of different models and consider the greening indexes is urgently needed. However, the assembly work of the aircraft production line at present cannot well take the assembly period of the aircraft and the carbon emission in the assembly process into consideration, so that the scheduling of the assembly work 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 a specific technical solution of the present application is introduced, a hardware operating environment related to a scheme of an embodiment of the present application is introduced first.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a computer device in a hardware operating environment according to an embodiment of the present application.
As shown in fig. 1, the computer apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also 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 Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of a computer device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and an electronic program.
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 a computer device, and the computer device calls the production line assembly job scheduling device stored in the memory 1005 through the processor 1001 and executes the method for scheduling the production line assembly job provided by the embodiment of the present invention.
Referring to fig. 2 to 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, including:
s10: constructing a plurality of dispatching frames for assembly operation based on a target aircraft production line; wherein the plurality of assembly operations are assembly operations involved in assembly of the target aircraft using the target aircraft production line.
In a specific implementation process, the target aircraft is an aircraft assembled on a production line, the assembling operation is an instructional operation for assembling the target aircraft by adopting a reasonable method in order to fully utilize assembling resources, and specifically, the assembly operation can be provided by an assembling workshop, and the scheduling frame is a frame for assembling operation of assembling operations from a plurality of different target aircraft models on a flexible production line consisting of a plurality of robots. Specifically, the method can be used for products produced on a production line, the method is introduced by taking an airplane as an example, and provides a scheduling framework for assembling operation of a plurality of different machine types on a flexible production line consisting of a plurality of robots for an intelligent flexible production line.
In this embodiment, in order to better study the assembly job scheduling of the target aircraft, the basic scheduling framework has the following assumed conditions:
1. an aircraft digital intelligent flexible production line comprises m robots, wherein n assembling operations are performed on each aircraft type, each assembling operation comprises ni assembling AO (assembling outline), and the ni assembling outlines of the same assembling operation have time sequence relation.
2. At the same time, each robot can only be assembled with one assembly outline; each assembly outline can be operated by only one robot at a time.
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 are different due to different performances of each robot.
4. The robots transport assembly tasks and materials through the electric automatic guided vehicles, and the power and the transport time of the electric automatic guided vehicles are measured in advance.
S11: constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling operation.
In the specific implementation process, an assembly cycle model and a carbon emission model of each model assembly work are constructed based on a scheduling framework and are used as assembly targets of the assembly work of each model.
Specifically, the assembly cycle model is obtained by the following relational expression:
Figure BDA0003808767820000101
wherein, T i For the assembly time of the ith assembly job,
Figure BDA0003808767820000102
for the ith assembly workStarting time, t, of the individual assembly outline on the kth robot ijk Assembly time on kth robot for jth assembly outline of ith assembly job.
Optionally, the carbon emission model is obtained by the following relation:
Figure BDA0003808767820000103
Figure BDA0003808767820000104
Figure BDA0003808767820000105
Figure BDA0003808767820000106
Figure BDA0003808767820000107
Figure BDA0003808767820000108
wherein, CE i Is the total carbon emission when the robot is operating,
Figure BDA0003808767820000109
the amount of carbon emissions for the assembly for the ith assembly operation,
Figure BDA00038087678200001010
carbon emission, P, for assembly of the jth assembly line of the ith assembly work ijk The assembly power on the kth robot for the jth assembly outline of the ith assembly job, alpha is the carbon emission factor,
Figure BDA0003808767820000111
transport of carbon emissions, P, for the ith assembly operation v Transport power, tp, for automated guided vehicles (j-1,l),(j,m) The transport time from the ith robot to the mth robot or the transport time from the (j-1) th assembly outline to the jth assembly outline,
Figure BDA0003808767820000112
is the idle power of the kth robot,
Figure BDA0003808767820000113
for the assembly completion time of the jth assembly outline of the ith assembly job on the kth robot,
Figure BDA0003808767820000114
ideal completion time for the ith assembly job, CE idle The carbon emissions for the total idle running of all robots,
Figure BDA0003808767820000115
is the amount of carbon emissions assigned to the ith assembly job.
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 machine type is taken as a participating main body (man in the office), the assembling operation period and the assembling carbon emission of each machine type are taken as income functions, an optional assembling robot is taken as a game strategy, and a non-cooperative game model of multi-machine type multi-assembling operation is constructed by applying a non-cooperative game theory.
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 revenue function module and a Nash equilibrium solution module;
s131: and obtaining a solution of a strategy integration module, a solution of an assembly operation module, a solution of a revenue function module and a solution of a Nash equilibrium module in the non-cooperative game model.
Obtaining the assembly operation module through the following relational expression:
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 operation, O ij Jth assembly outline, n, representing ith assembly job i Representing the total number of assembly outlines of the ith assembly operation;
obtaining the policy set module by the following relation:
Figure BDA0003808767820000116
wherein the content of the first and second substances,
Figure BDA00038087678200001210
for each policy, i ∈ f; m ij A set of selectable operating robots for a jth assembly outline of an ith assembly job.
Optionally, the gain function module is obtained by the following relation:
Y＝[T i (x)，CE i ]
obtaining the Nash equilibrium solution module by the following relation:
Figure BDA0003808767820000121
Figure BDA0003808767820000122
Figure BDA0003808767820000123
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003808767820000124
it is shown that the first of the desired strategies,
Figure BDA0003808767820000125
the nth desired policy is represented by the value of,
Figure BDA0003808767820000126
representing either policy.
S132: and obtaining the 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 income function module and the solution of the Nash equilibrium solution module.
In order to ensure that the completion period of each assembly operation and the total carbon emission of assembly are balanced and achieve a Nash equilibrium solution, the Nash equilibrium solution of the non-cooperative game model is obtained through the following local search algorithm:
Figure BDA0003808767820000127
Figure BDA0003808767820000128
Figure BDA0003808767820000129
Figure BDA0003808767820000131
Figure BDA0003808767820000132
Figure BDA0003808767820000133
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003808767820000134
it is shown that the first of the desired strategies,
Figure BDA0003808767820000135
the nth desired policy is represented by the value of,
Figure BDA0003808767820000136
it is meant that any one of the policies,
Figure BDA00038087678200001316
for each of the policies, the policy server may,
Figure BDA0003808767820000137
indicating the ideal assembly time for the ith assembly job,
Figure BDA0003808767820000138
represents the ideal total carbon emission, T, when the robot is running i Assembly time for the ith assembly job, CE i Is the total carbon emission when the robot is operating,
Figure BDA0003808767820000139
representing the fitness function, ω a And
Figure BDA00038087678200001310
all represent an equalization factor, F i Indicating the target balance for the ith assembly job,
Figure BDA00038087678200001311
U i 、H i and
Figure BDA00038087678200001312
both represent intermediate variables and both λ and ε represent convergence thresholds.
Wherein the fitness function is obtained by the following formula:
Figure BDA00038087678200001313
wherein i =1, … N, a =1, … numtop, b =1, … maI, a represents the number of iterations, and b represents the population number;
Figure BDA00038087678200001314
representing the assembly cycle of the i-th assembly job in the b-th chromosome in the a-th iteration,
Figure BDA00038087678200001315
represents the assembly carbon emission of the ith assembly job in the h chromosome of the a-th iteration.
And when the convergence threshold is reached, the convergence threshold of epsilon and lambda 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 assembling operation can make the assembling period of the airplane on the production line shortest and simultaneously considers the carbon emission amount to be the minimum.
In summary, the assembly operation scheduling decision model based on the non-cooperative game is provided based on the game theory, in order to obtain the Nash equilibrium solution of the model, a solving algorithm for solving the inheritance and local search mixed solution of the model is provided, the Nash equilibrium solution of the model is obtained, the minimization of carbon emission is considered while the minimization of the assembly period of the airplane is realized, the whole carbon emission of a workshop is modeled on an assembly product, a novel carbon emission modeling thought is provided, the carbon emission of different assembly operations can be measured, and therefore a link with large bottleneck carbon emission can be found out under a carbon emission policy such as a carbon label carbon tariff policy, and the targeted control and improvement are facilitated. In a word, the assembly period of the airplane on the production line is obtained through the assembly period model, the carbon emission amount of the airplane assembled on the production line can be obtained through the carbon emission model, the carbon emission data can be effectively provided, what assembly operation can be obtained through the non-cooperative game model, the shortest assembly period of the airplane on the production line is ensured while the carbon emission amount is the smallest, the assembly operation can be dispatched in reverse, the airplane is assembled through the dispatched assembly operation, the requirement target that the assembly period and the assembly carbon emission amount are the smallest can be achieved, direction and data support are provided for scheduling and controlling the airplane assembly resources, and then the scheduling of the airplane production line assembly operation can meet the assembly requirement.
The effectiveness of the method is demonstrated below with reference to specific examples, which are as follows:
the method comprises the following steps: a virtual assembly production line is constructed, and a certain intelligent flexible production line is assumed to be constructed to have 8 assembly robots, wherein table 1 represents the no-load power of each assembly robot, and table 2 represents the transfer time among the assembly robots.
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 the individual assembly robots
Figure BDA0003808767820000141
Figure BDA0003808767820000151
Step two: the assembly line can simultaneously assemble assembly operations of 8-type airplanes, each assembly operation has 6 BOs, the time sequence relationship of the AO has strict priority, and the specific AO relationship is 1 → 2 → 3 → 4 → 5 → 6, and simultaneously the feasible assembly robots and the assembly operation power of each AO are shown in Table 3.
TABLE 3 Per present AO feasible Assembly robot and Assembly operating Power
Figure BDA0003808767820000152
In table 3, each cell has 3 middle brackets, the first middle bracket represents a feasible set of fitting robots of the present AO, the second bracket represents a set of fitting times of the present AO on the corresponding robot, and the third bracket represents a set of fitting powers of the present AO on the corresponding robot.
Step three: based on each constructed model and algorithm, algorithm parameters are set according to the input of the first step and the second step, as shown in the following table 4:
TABLE 4 Algorithm parameters
Parameter name Value of
Population 40
Maximum number of iterations 200
Probability of cross 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 the model is output, as shown in fig. 4, and the numbers in the boxes in fig. 4 represent the assembly jobs, the number of times the numbers appear in turn represents the several assembly jobs, and η represents the convergence threshold.
Step five: comparing the obtained solution 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, it can be seen that the solution obtained by the model conforms to the trend of the ideal solution, thereby proving the effectiveness of the method, i.e. the method can simultaneously optimize the assembly period and the assembly carbon emission of each assembly operation, and thus can make the scheduling of the assembly operation of the aircraft production line meet the assembly requirements.
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, which includes:
the first construction module is used for constructing a plurality of dispatching frames of assembly operation based on a target aircraft production line; wherein the assembling operation is an assembling operation of 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 assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling operation;
the third construction module is used for constructing 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, in this embodiment, each module in the production line assembly job scheduling apparatus corresponds to each step in the production line assembly job scheduling method in the foregoing embodiment one to one, and therefore, the specific implementation manner and the achieved technical effect of this embodiment may refer to the implementation manner of the production line assembly job scheduling method, which is not described herein again.
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, and when the computer program is executed by the processor, the method in the foregoing embodiment is implemented.
Furthermore, in an embodiment, the present application further provides a computer storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the method in the foregoing embodiment.
In some embodiments, the computer-readable storage medium may be memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories. The computer may be a variety of computing devices including intelligent terminals and servers.
In some embodiments, executable instructions may be written in any form of programming language (including compiled or interpreted languages), in the form of programs, software modules, scripts or code, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
By way of example, executable instructions may correspond, but do not necessarily have to correspond, to files in a file system, and may be stored in a portion of a file that holds other programs or data, such as in one or more scripts in a hypertext Markup Language (HTML) 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).
By way of example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or 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 a … …" does not exclude the presence of another identical element in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a multimedia terminal (e.g., a mobile phone, a computer, a television receiver, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims (11)

1. A method for scheduling assembly operations in a production line, the method comprising:
constructing a plurality of dispatching frames for assembly operation based on a target aircraft production line; wherein the plurality of assembly operations are assembly operations involved in assembly of the target aircraft by using the target aircraft production line;
constructing an assembly period model and a carbon emission model based on the scheduling framework; the assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling 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 method for scheduling assembly operations on a production line as claimed in claim 1, wherein the non-cooperative game model comprises a strategy aggregation module, a revenue function module and a nash equilibrium solution module;
the scheduling of the assembly operation of the target aircraft production line based on the non-cooperative game model comprises the following steps:
obtaining a solution of the policy set module, a solution of the revenue function module, and a solution of the nash equilibrium solution module;
and obtaining the 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 income function module and the solution of the Nash equilibrium solution module.
3. The production line assembly job scheduling method of claim 2, wherein the obtaining of 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 aggregation module, the solution of the revenue function module, and the solution of the nash equilibrium solution module comprises:
obtaining a Nash equilibrium solution of the non-cooperative game model through the following local search algorithm:
Figure FDA0003808767810000021
Figure FDA0003808767810000022
Figure FDA0003808767810000023
Figure FDA0003808767810000024
Figure FDA0003808767810000025
Figure FDA0003808767810000026
wherein the content of the first and second substances,
Figure FDA0003808767810000027
it is shown that the first of the desired strategies,
Figure FDA0003808767810000028
it is shown that the N-th ideal strategy,
Figure FDA0003808767810000029
it is meant that any one of the policies,
Figure FDA00038087678100000210
for each of the policies, the policy server may,
Figure FDA00038087678100000211
indicating the ideal assembly time for the ith assembly job,
Figure FDA00038087678100000212
represents the ideal total carbon emission, T, when the robot is running i Assembly time for the ith assembly job, CE i Is the total carbon emission when the robot is operating,
Figure FDA00038087678100000213
representing the fitness function, ω a And
Figure FDA00038087678100000217
all represent an equalization factor, F i Indicating the target balance for the ith assembly job,
Figure FDA00038087678100000214
U i 、H i and
Figure FDA00038087678100000215
both represent intermediate variables and both λ and ε represent convergence thresholds.
4. The production line assembly job scheduling method of claim 3, wherein the fitness function is obtained by the following formula:
Figure FDA00038087678100000216
wherein i =1, … N; a =1, … numtop; b =1, … maI; a represents iteration times, and b represents population quantity;
Figure FDA0003808767810000031
representing the assembly cycle of the i-th assembly job in the b-th chromosome in the a-th iteration,
Figure FDA0003808767810000032
represents the assembly carbon emission of the ith assembly job in the h chromosome of the a-th iteration.
5. The production line assembly job scheduling method according to claim 3,
obtaining a solution of the policy set module by the following relation:
Figure FDA0003808767810000033
wherein, M ij Set of selectable operating robots for the jth assembly schema of the ith assembly job, j ∈ n i ，i∈f；
Obtaining a solution to the revenue function module by the following relationship:
Y＝[T i (x)，CE i (x)]。
6. the production line assembly job scheduling method according to claim 3,
obtaining a solution for the nash-equalization solution module by the following relation:
Figure FDA0003808767810000034
Figure FDA0003808767810000035
Figure FDA0003808767810000036
7. the production line assembly job scheduling method of claim 1, wherein said building an assembly cycle model and a carbon emission model based on said scheduling framework comprises:
obtaining the assembly period model by the following relation:
Figure FDA0003808767810000041
wherein, T i For the assembly time of the ith assembly job,
Figure FDA0003808767810000042
starting time, t, on kth robot for jth assembly outline of ith assembly job ijk Assembly time on kth robot for jth assembly outline of ith assembly job.
8. The method for scheduling assembly jobs on a production line according to claim 1, wherein the building an assembly cycle model and a carbon emission model based on the scheduling framework further comprises:
the carbon emission model is obtained by the following relation:
Figure FDA0003808767810000043
Figure FDA0003808767810000044
Figure FDA0003808767810000045
Figure FDA0003808767810000046
Figure FDA0003808767810000047
Figure FDA0003808767810000048
wherein, CE i Is the total carbon emission when the robot is operating,
Figure FDA0003808767810000049
the amount of assembly carbon emissions for the ith assembly operation,
Figure FDA00038087678100000410
carbon emission, P, for the assembly of the jth assembly outline of the ith assembly job ijk The assembly power on the kth robot for the jth assembly outline of the ith assembly job, alpha is a carbon emission factor,
Figure FDA0003808767810000051
transport of carbon emissions, P, for the ith assembly operation v For transport capacity of automated guided vehicles, tp (j-1,l),(j,m) The transport time from the ith robot to the mth robot or the transport time from the (j-1) th assembly outline to the jth assembly outline,
Figure FDA0003808767810000052
is the idle power of the kth robot,
Figure FDA0003808767810000053
assembly completion time, T, for the jth assembly outline of the ith assembly job on the kth robot i * Ideal completion time for the ith assembly job, CE idle The carbon emissions for the total idle running of all robots,
Figure FDA0003808767810000054
to be allocated to the ith assembly operationCarbon emissions.
9. A production line assembly job scheduling device, the device comprising:
the first construction module is used for constructing a plurality of dispatching frames of assembly operation based on a target aircraft production line; wherein the assembling operation is an assembling operation of 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 assembling cycle model is used for representing the time length of each assembling operation, and the carbon emission model is used for representing the carbon emission of each assembling operation;
the third construction module is used for constructing 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.
10. A computer arrangement, characterized in that the computer arrangement comprises a memory in which a computer program is stored and a processor which executes the computer program for implementing the method as claimed in any one of claims 1-8.
11. A computer-readable storage medium, having a computer program stored thereon, which, when executed by a processor, performs the method of any one of claims 1-8.
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