Aircraft pulsation assembly line production scheduling optimization method and device and electronic equipment

Abstract

The invention relates to the technical field of aircraft pulsation assembly lines, in particular to an aircraft pulsation assembly line scheduling optimization method and device and electronic device.The aircraft pulsation assembly line scheduling optimization method comprises the steps that an aircraft pulsation assembly line scheduling model is established, the airplane pulsation assembly line production scheduling model comprises a process, a process constraint, a resource constraint, a station constraint and a limited station constraint; by taking the minimum total number of personnel configuration and the minimum pulsation beat as optimization objectives, expanding the priority value scheme, adding an assembly line personnel configuration scheme, and generating a solution scheme code; and inputting the solution scheme codes into the airplane pulsation assembly line production scheduling model, carrying out iterative optimization on the solution scheme codes, and processing target values of various complex constraint output solutions. According to the method, the resource sharing characteristic of the assembly line can be effectively processed, and the multi-target production scheduling optimization problem of the pulsation assembly line with resource sharing and coexistence of multiple complex resources can be solved.

Description

Technical Field

[0001] This application relates to the field of aircraft pulse assembly line technology, and in particular to a method, apparatus and electronic equipment for optimizing production scheduling in an aircraft pulse assembly line. Background Technology

[0002] Based on the assumption that resources belong to private stations, existing methods for calculating production scheduling on aircraft pulse assembly lines mainly fall into three categories: exact algorithms, heuristic methods, and metaheuristic methods. Exact algorithms typically aim to find the optimal solution to the problem, but because the computational cost increases exponentially with the size of the scheduling problem, they are only suitable for solving simple, small-scale scheduling problems. Heuristic methods directly utilize intuitive or experiential knowledge about the scheduling problem to construct a scheduling scheme, typically including priority rule methods and bottleneck transfer-based methods. Metaheuristic algorithms are an improvement on heuristic algorithms. Unlike heuristic algorithms, metaheuristic algorithms use heuristic information learned during the search process to guide the search towards an optimal solution. Many metaheuristic algorithms simulate optimization behaviors in nature to construct optimization algorithms. Depending on the simulated natural phenomena, metaheuristic algorithms include genetic algorithms, ant colony optimization algorithms, particle swarm optimization algorithms, simulated annealing algorithms, and tabu search algorithms, among others.

[0003] Compared to exact algorithms and heuristic methods, metaheuristic algorithms are more suitable for solving large-scale multi-objective scheduling optimization problems. However, existing metaheuristic scheduling methods generally assume that all resources on the assembly line are private to each workstation, neglecting the existence of shared resources. This makes traditional methods unsuitable for scheduling optimization problems on pulsed assembly lines where resource sharing is a characteristic. Unlike typical pulsed production lines, personnel on aircraft pulsed assembly lines are flexible and can be deployed between different workstations, representing a shared resource. This characteristic renders multi-objective scheduling optimization methods applicable to typical pulsed production lines ineffective. Since domestic aircraft pulsed assembly lines are primarily personnel-based, the flexible deployment of personnel between workstations is a key characteristic. This feature renders traditional multi-objective scheduling optimization methods based on the assumption of private resource allocation in pulsed assembly lines inapplicable. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for optimizing the scheduling of aircraft pulse assembly lines, which can be used for scheduling optimization calculations of pulse assembly lines with resource sharing characteristics.

[0005] Firstly, this application provides a method for optimizing production scheduling on an aircraft pulse assembly line, including: Establish an aircraft pulse assembly line scheduling model, which includes process constraints, resource constraints, station constraints, and restricted station constraints. With the optimization objectives of minimizing the total number of personnel and minimizing the pulse beat, the priority value scheme is expanded by adding an assembly line personnel configuration scheme, and a solution code is generated. The solution scheme is encoded and input into the aircraft pulse assembly line scheduling model. The encoded solution scheme is iteratively optimized, and the target value of the solution is output after handling various complex constraints.

[0006] Furthermore, the establishment of the aircraft pulse assembly line scheduling model includes: A B-tree structure was used to create a curve showing the changes in resource capacity of an aircraft pulsed assembly line. Establish a process model and express the resource requirements, technological relationships, station allocation, and constraints of the process.

[0007] Furthermore, each node in the B-tree structure represents a point where the resource capability curve changes. The index part stores the time value of the resource change of the node, and the value part stores the capability value of the node after the change.

[0008] Furthermore, the process model stores each process to be scheduled in the form of an array. Each process is represented by its array position index as a unique process number in the scheduling process. The process includes the process start time, end time, and processing time.

[0009] Furthermore, before expanding the priority value scheme by adding an assembly line personnel configuration scheme and generating the solution code, with the optimization objectives of minimizing the total number of personnel and minimizing the pulse rate, the following steps are also included: The beat is mapped and extended in the time dimension, and the original physical position is mapped into a virtual pulsating cycle in the time dimension; The processes of multiple aircraft are treated as a common set of solution processes, and restrictive station constraints are added to all processes of each aircraft.

[0010] Furthermore, the step of inputting the encoded solution into the aircraft pulsed assembly line scheduling model, iteratively optimizing the encoded solution, and processing various complex constraints to output the target value of the solution includes: The total number of personnel is the sum of the numbers in the personnel configuration plan as the objective function. The minimum cycle time optimization objective is to decode the solution based on the priority value solution encoding scheme and the safe waiting time. Set a feasible cycle value, and consider process constraints, resource constraints, station constraints and restricted station constraints during decoding; If the beat meets the requirements, the beat continues to be reduced for decoding until the smallest beat value that meets the requirements is found, and this beat value is used as the target beat value.

[0011] Furthermore, after encoding the solution scheme and inputting it into the aircraft pulsed assembly line scheduling model, iteratively optimizing the encoded solution scheme, and processing various complex constraints to output the target value of the solution, the process further includes: The particle swarm optimization algorithm is used to optimize the encoded solution, and the non-dominated solutions encountered during the solution process are stored. When the maximum number of iterations or the time limit is reached, the Pareto solution set is output.

[0012] Secondly, this application provides a production scheduling optimization device for aircraft pulse assembly lines, comprising: The model building module is used to build a production scheduling model for an aircraft pulse assembly line. The production scheduling model for an aircraft pulse assembly line includes process, process constraints, resource constraints, station constraints, and restricted station constraints. The coding generation module is used to expand the priority value scheme with the optimization objectives of minimizing the total number of personnel and minimizing the pulse beat, add assembly line personnel configuration schemes, and generate solution code; The target output module is used to input the solution scheme encoding into the aircraft pulse assembly line scheduling model, iteratively optimize the solution scheme encoding, and process various complex constraints to output the target value of the solution.

[0013] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aircraft pulse assembly line scheduling optimization method as described above.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aircraft pulse assembly line scheduling optimization method as described above.

[0015] The above-mentioned technical solution of this application has the following advantages: The first aspect of this application provides a production scheduling optimization method for aircraft pulsed assembly lines. It establishes a multi-objective production scheduling optimization model to handle the multiple complex constraints involved in aircraft pulsed assembly lines, reducing the difficulty of solving such problems. The optimization objectives for aircraft pulsed assembly lines are minimizing the total number of personnel resources and minimizing the pulsed cycle time. Multi-objective production scheduling optimization calculations are performed under various resource constraints such as personnel, equipment, tooling, fixtures, and space, as well as process relationship constraints specified by the technology. This effectively handles the resource-sharing characteristics of assembly lines and overcomes the shortcomings of traditional methods in solving such problems. Based on the ability to generate feasible solutions that satisfy all constraints, iterative optimization can be performed on the feasible solutions for the objectives of minimizing the total number of personnel resources and minimizing the pulsed cycle time.

[0016] It is understood that the beneficial effects of the second, third and fourth aspects mentioned above can be found in the relevant descriptions in the first aspect above, and will not be repeated here. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart of the aircraft pulse assembly line scheduling optimization method provided in this application; Figure 2 A schematic diagram of a simple aircraft pulsed assembly line scenario provided in this application; Figure 3 A schematic diagram illustrating the input problem for the mapping pulsation period method provided in this application; Figure 4 The resource capacity change curve provided for this application; Figure 5 The process model and related data diagram provided for this application; Figure 6 A schematic diagram of the multiple encoding solution scheme provided in this application; Figure 7 The decoding flowchart provided for this application. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. "A plurality" means "two or more."

[0023] This application provides a scheduling optimization method for aircraft pulsed assembly lines with resource sharing. It can solve multi-objective scheduling optimization problems of pulsed assembly lines with resource sharing and coexistence of multiple complex resources. Moreover, this method is applicable to the updating calculation of the scheduling scheme of pulsed assembly lines after process iteration.

[0024] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0025] like Figure 1 As shown in the figure, this application provides a method for optimizing the scheduling of an aircraft pulse assembly line, which specifically includes the following steps: establishing an aircraft pulse assembly line scheduling model, the aircraft pulse assembly line scheduling model including process, process constraints, resource constraints, station constraints, and restricted station constraints; taking the minimum total number of personnel and the minimum pulse cycle as optimization objectives, expanding the priority value scheme, adding assembly line personnel configuration schemes, and generating solution scheme codes; inputting the solution scheme codes into the aircraft pulse assembly line scheduling model, iteratively optimizing the solution scheme codes, processing various complex constraints, and outputting the target value of the solution.

[0026] In some embodiments, establishing a production scheduling model for an aircraft pulse assembly line includes: establishing a resource capacity change curve of the aircraft pulse assembly line using a B-tree structure; establishing a process model and expressing the resource requirements, technological relationships, station allocation, and constraints of the process.

[0027] In some embodiments, each node in the B-tree structure represents a point where the resource capability curve changes, the index part stores the time value of the node's resource change, and the value part stores the capability value of the node after the change.

[0028] In some embodiments, the process model stores each process to be scheduled in the form of an array. Each process is assigned a unique process number in the scheduling process by its array position index. The process includes the process start time, end time, and processing time.

[0029] In some embodiments, before expanding the priority value scheme, adding an assembly line personnel configuration scheme, and generating the solution code with the optimization objectives of minimizing the total number of personnel and minimizing the pulse beat, the method further includes: mapping and expanding the pulse beat in the time dimension, mapping the original physical station positions to virtual pulse cycles in the time dimension; treating the processes of multiple aircraft as a common set of solution processes, and adding restricted station constraints to all processes of each aircraft.

[0030] In some embodiments, the step of encoding the solution scheme into the aircraft pulse assembly line scheduling model, iteratively optimizing the encoded solution scheme, and processing various complex constraints to output the target value of the solution includes: using the sum of the quantities in the personnel configuration scheme as the objective function for the total number of personnel; decoding the solution based on the priority value encoding scheme and the safe waiting time for the minimum cycle time optimization objective; setting a feasible cycle time value, considering process constraints, resource constraints, station constraints, and restricted station constraints during decoding; if the cycle time meets the requirements, continuing to reduce the cycle time for decoding until the smallest cycle time value that meets the requirements is found, and using this cycle time value as the target value for the cycle time.

[0031] In some embodiments, after encoding the solution scheme into the aircraft pulse assembly line scheduling model, iteratively optimizing the encoded solution scheme, and processing various complex constraints to output the target value of the solution, the method further includes: using a particle swarm optimization algorithm to perform optimization calculations on the encoded solution, storing the non-dominated solutions encountered during the solution process, and outputting the Pareto solution set when the maximum number of iterations or time limit is reached.

[0032] like Figure 2 The diagram illustrates a simple aircraft pulse assembly line scenario. Assuming there is only one assembly worker on the line, when aircraft 1's task is completed at station 1, aircraft 1 moves from station 1 to station 2 to perform its task. Simultaneously, due to the characteristics of the pulse assembly line, aircraft 2 moves to station 1 and begins its task. At this point, both stations 1 and 2 are performing two tasks, requiring two workers, but there is only one worker on the production line. Therefore, a special solution is needed to address the issue of personnel transferability.

[0033] The aircraft pulse assembly line scheduling optimization method provided in this application addresses the pulse assembly line scheduling optimization problem involving process iteration and resource sharing, and is mainly divided into three stages: assembly line scheduling model establishment, problem input, and solution.

[0034] 1. Assembly line scheduling model establishment The assembly line scheduling model consists of three parts: processes, resources, and solution representation. Processes are the basic computational objects, containing three important parameters: processing time, planned start time, and planned end time. They are the main objects involved in solving the scheduling problem, which involves determining the planned start time of all processes involved in the scheduling calculation. Resources on the assembly line include personnel, equipment, tooling, fixtures, and space, among others. The scheduling process requires establishing a resource representation for the assembly line. The scheduling model uses an indirect solution representation scheme. The direct decision variable in the scheduling calculation process is not the task start time, but rather the task priority value. A decoding method is then used to generate the final scheduling solution.

[0035] Assembly line scheduling models can handle various constraints, including process constraints, resource constraints, station constraints, and restricted station constraints. The process constraints for executing processing operations on a pulsed aircraft assembly line can be represented using a directed acyclic graph (DAG), where each node represents an operation, and arrows indicate that the pointed-to operation can only begin after the pointed-to operation is completed. Resource constraints are established based on the assembly line resource representation to ensure that resources on the assembly line are adequately supplied relative to the scheduling plan at any given time. For pulsed assembly lines, operations can only be completed at a single station and cannot be performed across stations. Furthermore, the allocation of tasks that are not permitted to work at certain stations needs to be restricted.

[0036] 2. Question Input Traditional multi-objective scheduling optimization problems in pulsed assembly lines do not consider the resource sharing characteristic between workstations. Therefore, it is possible to calculate the scheduling scheme for only one aircraft and infer the scheduling schemes for other subsequent aircraft based on the cycle time. However, for problems with resource sharing, the scheduling schemes for other aircraft derived from the scheduling scheme of a single aircraft may conflict in the occupation of shared resources, making this approach no longer applicable. Therefore, the problem input of this method is to map and extend the cycle time in the time dimension, mapping the original physical workstations to virtual pulsed cycles in the time dimension, and treating the processes of multiple aircraft as a common set of solution processes. Furthermore, to ensure that each aircraft completes its work at an actual workstation, restrictive workstation constraints need to be added to all processes of each aircraft. Figure 3 In the case of the Chinese example, the original three stations are mapped to five pulse cycles in the time dimension. At the same time, all processes of aircraft 1 should be limited to cycles 1-3, aircraft 2 should be limited to station cycles 2-4, and aircraft 3 should be limited to cycles 3-5.

[0037] 3. Solve This application addresses a multi-objective scheduling optimization problem for a pulsed assembly line, aiming to minimize both the total number of personnel and the pulsed cycle time. This requires expanding the priority scheme by adding personnel configuration schemes for the assembly line. Therefore, a new set of data representing the resource capacity configuration on the assembly line needs to be added to the priority scheme, with values ​​indicating the number of assembly personnel allocated to each specialty. The total number of personnel is calculated as the sum of the quantities in the personnel configuration schemes, serving as the objective function. The minimum cycle time optimization objective requires decoding the solution based on the priority solution encoding scheme and a safe waiting time. First, a feasible cycle time value is set. During decoding, process constraints, resource constraints, station constraints, and restricted station constraints are considered. If the cycle time meets the requirements, the decoding continues by decreasing the cycle time until the minimum satisfactory cycle time value is found, which is then used as the target cycle time value. A particle swarm optimization algorithm is used to optimize the problem, and the Pareto solution is saved. After a certain number of iterations, the Pareto solution set is output. When performing production scheduling update calculations, the cycle time of the station positions and constraints is assigned to the actual cycle time of the pulsating assembly line. At this time, the assembly line scheduling model outputs a judgment on the feasibility of the corresponding solution and the magnitude of the conflict if it is not feasible. In this application, the target value for judging the magnitude of the conflict is the delay time. Since the cycle time no longer changes iteratively, the decoding process is executed once. If it is not feasible, the particle swarm optimization algorithm optimizes in the direction of minimizing the conflict value.

[0038] The following is a description through specific embodiments.

[0039] Example Step 1: Establish an assembly line resource capability model.

[0040] The assembly line resource capability model can be abstracted as an assembly line resource capability change curve. Each resource corresponds to a curve, and the curve is built with a B-tree structure. Each node in the B-tree represents the point where the resource capability curve changes. The index part stores the time value when the node's resource changes, and the value part stores the capability value of the node after the change. Figure 4 The text shows that the task start time is set to... At any given time, the processing time is... This task requires certain resource capabilities. The resource capability curve is in The ability to generate data decreases. This change is reflected in the B-tree as the newly added index. The value is and index as The value is Two new nodes. To determine whether the resource constraints are satisfied, we only need to check if there are any nodes in the resource B-tree with values ​​less than the safe capacity. If not, the constraints are satisfied.

[0041] Step 2: Establish a process model and express the resource requirements, technological relationships, station allocation, and constraints of the process.

[0042] Figure 5 The intermediate process set stores each process to be scheduled in the form of an array. Each process has a unique process number in the scheduling process, with its array position index serving as the unique process number. The process includes the process start time, end time, and processing time. There is a resource capacity requirement array associated with each process. The numbers in the array represent the capacity value of the resource required by the process, and the array index represents different resource types. The station position restriction array has a length related to the total number of stations. After adopting the mapping pulse cycle method, the size of the station position restriction array is consistent with the number of pulse cycles. The array index represents the corresponding cycle number, and the array stores Boolean values: 0 indicates that allocation to this station is not allowed, and 1 indicates that allocation to the corresponding station is allowed, for use in the decoding process. Pre-processes and post-processes are stored in linked lists, representing all pre-processes and post-processes of this process, respectively.

[0043] Step 3: Generate the coded representation of the solution.

[0044] Figure 6 This application demonstrates the multiple encoding methods for the solution, including priority value encoding and personnel configuration quantity encoding. The size of the priority value encoding array is equal to the number of steps in the input problem, and the size of the personnel configuration encoding array is equal to the number of skill types of the personnel. The priority value encoding takes values ​​from real numbers within the range allowed by the programming language, while the personnel configuration is represented by non-negative integers.

[0045] Step 4: Decode to obtain the target beat value. Figure 7 The decoding process is shown in Table 1.

[0046] Table 1 Decoding Process Step 5: Use the particle swarm optimization algorithm to optimize the solution.

[0047] The particle swarm optimization algorithm is used to optimize the encoded solution, and the non-dominated solutions encountered during the solution process are stored. When the maximum number of iterations or the time limit is reached, the Pareto solution set is output.

[0048] The aircraft pulse assembly line scheduling optimization method provided in this application has the following beneficial effects: (1) A multi-objective scheduling optimization model for aircraft pulse assembly lines was established. The optimization model consists of process, process constraints, resource constraints, station positions, and restricted station position constraints. Using this model, multiple constraints on aircraft pulse assembly lines can be effectively handled. Based on the construction of this model, the multi-objective scheduling optimization algorithm only needs to iteratively optimize the encoding of the solution. The model receives the encoding and processes various complex constraints to output the target value of the solution, which effectively reduces the implementation difficulty and versatility of the algorithm. At the same time, it is beneficial to study the performance and optimization effect of different algorithm parameters and different algorithms, so as to select an optimization algorithm more suitable for specific problems and improve the production efficiency of aircraft pulse assembly lines.

[0049] (2) This method designs a mapping pulse cycle method for the multi-objective scheduling optimization problem of aircraft pulse assembly lines where personnel can be reassigned across stations. By merging the assembly tasks of multiple aircraft as a common problem input, it effectively addresses the problem of resource sharing and makes up for the shortcomings of traditional scheduling optimization methods. At the same time, combined with the use of a multi-objective scheduling optimization model, the mapping pulse cycle method is transformed into only needing to input the number of aircraft. The scheduling optimization model adds station constraints to all processes of each aircraft, effectively reducing the difficulty of solving the multi-objective optimization scheduling problem of aircraft pulse assembly lines with resource sharing characteristics.

[0050] Corresponding to the aircraft pulse assembly line scheduling optimization method described in the above embodiments, this application also provides an aircraft pulse assembly line scheduling optimization device, which includes: The model building module is used to build a production scheduling model for an aircraft pulse assembly line. The production scheduling model for an aircraft pulse assembly line includes process, process constraints, resource constraints, station constraints, and restricted station constraints. The coding generation module is used to expand the priority value scheme with the optimization objectives of minimizing the total number of personnel and minimizing the pulse beat, add assembly line personnel configuration schemes, and generate solution code; The target output module is used to input the solution scheme encoding into the aircraft pulse assembly line scheduling model, iteratively optimize the solution scheme encoding, and process various complex constraints to output the target value of the solution.

[0051] It should be noted that the information interaction and execution process between the above modules / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0052] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0053] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aircraft pulse assembly line scheduling optimization method provided in the first aspect.

[0054] In applications, electronic devices may include more or fewer components, or combinations of certain components, or different components, such as input / output devices, network access devices, etc. Input / output devices may include cameras, audio capture / playback devices, displays, etc. Network access devices may include network modules for wireless network communication with external devices.

[0055] In applications, the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0056] In applications, memory can be an internal storage unit of an electronic device in some embodiments, such as a hard drive or RAM. In other embodiments, memory can be an external storage device of the electronic device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Memory can also include both internal and external storage units of the electronic device. Memory is used to store operating systems, applications, bootloaders, data, and other programs, such as program code for computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0057] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.

[0058] This application implements all or part of the processes in the methods of the above embodiments, which can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0059] Those skilled in the art will recognize that the device and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0060] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interface, or the device may be indirectly coupled or communicated, and may be electrical, mechanical, or other forms.

[0061] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing production scheduling in an aircraft pulse assembly line, characterized in that, include: Establish an aircraft pulse assembly line scheduling model, which includes process constraints, resource constraints, station constraints, and restricted station constraints. With the optimization objectives of minimizing the total number of personnel and minimizing the pulse beat, the priority value scheme is expanded by adding an assembly line personnel configuration scheme, and a solution code is generated. The solution scheme is encoded and input into the aircraft pulse assembly line scheduling model. The encoded solution scheme is iteratively optimized, and the target value of the solution is output after handling various complex constraints.

2. The aircraft pulse assembly line scheduling optimization method as described in claim 1, characterized in that, The establishment of the aircraft pulse assembly line scheduling model includes: A B-tree structure was used to create a curve showing the changes in resource capacity of an aircraft pulsed assembly line. Establish a process model and express the resource requirements, technological relationships, station allocation, and constraints of the process.

3. The aircraft pulse assembly line scheduling optimization method as described in claim 2, characterized in that, Each node in the B-tree structure represents a point where the resource capability curve changes. The index part stores the time value of the resource change of the node, and the value part stores the capability value of the node after the change.

4. The aircraft pulse assembly line scheduling optimization method as described in claim 2, characterized in that, The process model stores each process to be scheduled in the form of an array. Each process is uniquely numbered in the scheduling process by its array position index. The process includes the process start time, end time and processing time.

5. The aircraft pulse assembly line scheduling optimization method as described in claim 1, characterized in that, The optimization objective of minimizing the total number of personnel and the minimum pulse rate, before expanding the priority value scheme by adding an assembly line personnel configuration scheme and generating solution code, also includes: The beat is mapped and extended in the time dimension, and the original physical position is mapped into a virtual pulsating cycle in the time dimension; The processes of multiple aircraft are treated as a common set of solution processes, and restrictive station constraints are added to all processes of each aircraft.

6. The aircraft pulse assembly line scheduling optimization method as described in claim 1, characterized in that, The process involves encoding the solution scheme and inputting it into the aircraft pulsed assembly line scheduling model, iteratively optimizing the encoded solution scheme, and processing various complex constraints to output the target value of the solution, including: The total number of personnel is the sum of the numbers in the personnel configuration plan as the objective function. The minimum cycle time optimization objective is to decode the solution based on the priority value solution encoding scheme and the safe waiting time. Set a feasible cycle value, and consider process constraints, resource constraints, station constraints and restricted station constraints during decoding; If the beat meets the requirements, the beat continues to be reduced for decoding until the smallest beat value that meets the requirements is found, and this beat value is used as the target beat value.

7. The aircraft pulse assembly line scheduling optimization method as described in claim 1, characterized in that, After encoding the solution scheme and inputting it into the aircraft pulsed assembly line scheduling model, iteratively optimizing the encoded solution scheme, and processing various complex constraints to output the target value of the solution, the process further includes: The particle swarm optimization algorithm is used to optimize the encoded solution, and the non-dominated solutions encountered during the solution process are stored. When the maximum number of iterations or the time limit is reached, the Pareto solution set is output.

8. A production scheduling optimization device for an aircraft pulse assembly line, characterized in that, include: The model building module is used to build a production scheduling model for an aircraft pulse assembly line. The production scheduling model for an aircraft pulse assembly line includes process, process constraints, resource constraints, station constraints, and restricted station constraints. The coding generation module is used to expand the priority value scheme with the optimization objectives of minimizing the total number of personnel and minimizing the pulse beat, add assembly line personnel configuration schemes, and generate solution code; The target output module is used to input the solution scheme encoding into the aircraft pulse assembly line scheduling model, iteratively optimize the solution scheme encoding, and process various complex constraints to output the target value of the solution.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the aircraft pulse assembly line scheduling optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the aircraft pulse assembly line scheduling optimization method as described in any one of claims 1 to 7.

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