Complex assembly scheduling method based on complex AOE network key node analysis and related system

Abstract

The invention belongs to the technical field of complex assembly scheduling, and discloses a complex assembly scheduling method based on complex AOE network key node analysis and a related system, which can dynamically adjust the priority of each task node by calculating the influence degree and sequence of task nodes in combination with an improved PageRank algorithm. The distribution of the dynamic priority can make a quick response according to real-time data in the production process, so that production disturbance and plan change can be coped with in time, and the scheduling result is ensured to have high flexibility. According to the method, the weighted directed acyclic network model is constructed, and based on the analysis of the influence degree matrix, the relative importance of each task node can be scientifically evaluated, so that the resources are effectively allocated, the task priority is effectively adjusted, the resource conflict or excessive concentration is avoided, and the overall efficiency of production scheduling is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of complex assembly scheduling, and in particular relates to a complex assembly scheduling method and a related system based on complex AOE network key node analysis. Background Art

[0002] Complex products, such as aircraft, spacecraft, and automobiles, are characterized by complex manufacturing processes, complex production management, and complex product technologies. As China gradually transforms to high-end manufacturing, the requirements for complex product manufacturing are getting higher and higher, and the manufacturing of complex products faces higher challenges. Complex products involve the coordinated operation of a large number of assembly plans, personnel, materials, tooling parts, etc., and there are many production disturbances and changeable plans, which makes production scheduling difficult and complicated. In order to increase the production capacity of complex products and strengthen the rapid response of scheduling to complex production and assembly, how to design an effective scheduling strategy and quickly achieve the best scheduling under limited resources is an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings that production scheduling becomes difficult and complicated due to many production disturbances and changeable plans, and to provide a complex assembly scheduling method based on key node analysis of complex AOE networks.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a complex assembly scheduling method based on complex AOE network key node analysis, comprising the following steps:

[0006] Collect relevant information of complex assembly operations and construct a weighted directed acyclic network model based on the relevant information of complex assembly operations;

[0007] According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed, and the influence and ranking of all task nodes are obtained by combining the improved PageRank algorithm;

[0008] Based on the weighted directed acyclic network model and the influence and ranking of all task nodes, the dynamic priority of all task nodes is obtained as the scheduling priority to form the first-stage production scheduling result;

[0009] The first-stage production scheduling results are locally optimized and supplemented with auxiliary search to obtain the second-stage local search optimization results.

[0010] A further improvement of the present invention is that the relevant information of the complex assembly operation includes the task context relationship information, the task personnel resource usage information and the task time resource usage information.

[0011] A further improvement of the present invention is that, based on a weighted directed acyclic network model, a task node influence matrix is ​​constructed, and combined with an improved PageRank algorithm, a specific method for obtaining the influence and ranking of all task nodes is as follows:

[0012] According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed and initialized;

[0013] Import the initialized task node influence matrix into the improved PageRank algorithm to calculate the influence value;

[0014] The loop is iterated until the convergence condition is met, and the influence and ranking of all task nodes are obtained.

[0015] A further improvement of the present invention is that, based on the weighted directed acyclic network model and the influence and ranking of all task nodes, the dynamic priority of all task nodes is obtained as the scheduling priority, and the specific method for forming a stage production schedule is as follows:

[0016] Based on the weighted directed acyclic network model, dynamic programming is used to reversely calculate the static priority of task nodes according to the influence and ranking of all task nodes;

[0017] According to the static priority of the task node and the scheduling constraints, the highest static priority of the task node is selected for scheduling;

[0018] During the scheduling process, task delays are taken into account and the dynamic priorities of all task nodes are updated;

[0019] Use dynamic priority as scheduling priority;

[0020] The loop is iterated until all tasks are scheduled and the first-stage production scheduling results are obtained.

[0021] A further improvement of the present invention is that the specific method of performing local optimization and auxiliary search on the first-stage production scheduling result to obtain the second-stage local search optimization result is as follows:

[0022] The improved variable neighborhood search algorithm is used to locally optimize the first-stage production scheduling results;

[0023] During the optimization process, an auxiliary search algorithm for important task nodes is added to perform auxiliary search;

[0024] The loop is iterated until the stop condition is met to obtain the second stage production scheduling result.

[0025] In a second aspect, the present invention provides a complex assembly scheduling system based on complex AOE network key node analysis, comprising:

[0026] AOE network model building module, used to collect relevant information of complex assembly operations and build a weighted directed acyclic network model based on the relevant information of complex assembly operations;

[0027] The influence and ranking acquisition module is used to construct the task node influence matrix based on the weighted directed acyclic network model, and combine the improved PageRank algorithm to obtain the influence and ranking of all task nodes;

[0028] The first-stage production scheduling result acquisition module is used to obtain the dynamic priority of all task nodes as the scheduling priority based on the weighted directed acyclic network model module and the influence and ranking of all task nodes to form the first-stage production scheduling result;

[0029] The second-stage local search optimization result acquisition module is used to perform local optimization and auxiliary search on the first-stage production scheduling results to obtain the second-stage local search optimization results.

[0030] A further improvement of the present invention is that the function of the influence and ranking acquisition module is realized by the following method:

[0031] According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed and initialized;

[0032] Import the initialized task node influence matrix into the improved PageRank algorithm to calculate the influence value;

[0033] The loop is iterated until the convergence condition is met, and the influence and ranking of all task nodes are obtained.

[0034] A further improvement of the present invention is that the function of the first-stage production scheduling result acquisition module is realized by the following method:

[0035] Based on the weighted directed acyclic network model, dynamic programming is used to reversely calculate the static priority of task nodes according to the influence and ranking of all task nodes;

[0036] According to the static priority of the task node and the scheduling constraints, the highest static priority of the task node is selected for scheduling;

[0037] During the scheduling process, task delays are taken into account and the dynamic priorities of all task nodes are updated;

[0038] Use dynamic priority as scheduling priority;

[0039] The loop is iterated until all tasks are scheduled and the first-stage production scheduling results are obtained.

[0040] A further improvement of the present invention is that the function of the two-stage local search optimization result acquisition module is realized by the following method:

[0041] The improved variable neighborhood search algorithm is used to locally optimize the first-stage production scheduling results;

[0042] During the optimization process, an auxiliary search algorithm for important task nodes is added to perform auxiliary search;

[0043] The loop is iterated until the stop condition is met to obtain the second stage production scheduling result.

[0044] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a complex assembly scheduling method based on complex AOE network key node analysis when executing the computer program.

[0045] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a complex assembly scheduling method based on complex AOE network key node analysis.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention can dynamically adjust the priority of each task node by combining the improved PageRank algorithm to calculate the influence and ranking of the task node. This dynamic priority allocation can make a quick response according to real-time data in the production process, respond to production disturbances and plan changes in a timely manner, and ensure that the scheduling results are highly flexible. The present invention can scientifically evaluate the relative importance of each task node by constructing a weighted directed acyclic network model and based on the analysis of the influence matrix, so as to effectively allocate resources and adjust task priorities, avoid resource conflicts or excessive concentration, and improve the overall efficiency of production scheduling. The two-stage local optimization and auxiliary search of the present invention can further improve the initial production scheduling results, reduce the problems of local optimal solutions, and improve the quality and adaptability of the overall scheduling plan. Through the optimization process, the scheduling plan can be continuously revised so that the scheduling system has better adaptability to emergencies (such as production line interruption, raw material shortage, etc.). The present invention is often accompanied by disturbance factors such as equipment failure and raw material supply fluctuations in the production process, and traditional static scheduling methods are difficult to deal with these emergencies. However, this method can respond to these disturbances in a timely manner by dynamically adjusting task priorities and local optimization, reduce scheduling confusion caused by production disturbances or plan changes, and keep the production tasks going smoothly. The present invention can sort the scheduling tasks according to the influence of the task nodes through the improved PageRank algorithm, so that important tasks are executed first, thereby improving the stability of production scheduling. In addition, the scheduling method based on the network model can achieve more accurate scheduling prediction and reduce scheduling uncertainty caused by sudden changes. In summary, the present invention can effectively improve the flexibility of the production scheduling system, optimize scheduling efficiency, cope with production disturbances, and provide stable and predictable scheduling results in a dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the present invention;

[0049] Figure 2 is a system diagram of the present invention;

[0050] Figure 3 It is a two-stage scheduling framework diagram of the present invention;

[0051] Figure 4 It is a schematic diagram of the AOE network of the present invention;

[0052] Figure 5 It is a schematic diagram of the dynamic and static critical paths of the present invention;

[0053] Figure 6 It is a flowchart of calculating the importance of task nodes of the present invention;

[0054] Figure 7 It is the second stage scheduling flow chart of the present invention;

[0055] Figure 8 A flow chart of the neighborhood transformation operation designed for the present invention;

[0056] Fig. 9 This is a system diagram of Example 6. DETAILED DESCRIPTION

[0057] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0058] See also Figure 1 ,The complex assembly scheduling method based on the key node analysis of complex AOE network includes the following steps:

[0059] S1, collect relevant information of complex assembly operations, and construct a weighted directed acyclic network model based on the relevant information of complex assembly operations.

[0060] S2, based on the weighted directed acyclic network model, construct the task node influence matrix, and combine it with the improved PageRank algorithm to obtain the influence and ranking of all task nodes.

[0061] S3, based on the weighted directed acyclic network model and the influence and ranking of all task nodes, obtains the dynamic priority of all task nodes as the scheduling priority to form a first-stage production scheduling result.

[0062] S4, perform local optimization and auxiliary search on the first-stage production scheduling results to obtain the second-stage local search optimization results.

[0063] See also Figure 2 , a complex assembly scheduling system based on complex AOE network key node analysis, including:

[0064] AOE network model building module, used to collect relevant information of complex assembly operations and build a weighted directed acyclic network model based on the relevant information of complex assembly operations;

[0065] The influence and ranking acquisition module is used to construct the task node influence matrix based on the weighted directed acyclic network model, and combine the improved PageRank algorithm to obtain the influence and ranking of all task nodes;

[0066] The first-stage production scheduling result acquisition module is used to obtain the dynamic priority of all task nodes as the scheduling priority based on the weighted directed acyclic network model module and the influence and ranking of all task nodes to form the first-stage production scheduling result;

[0067] The second-stage local search optimization result acquisition module is used to perform local optimization and auxiliary search on the first-stage production scheduling results to obtain the second-stage local search optimization results.

[0068] Embodiment 1:

[0069] Establish a two-stage scheduling framework for complex assembly to determine the work of each link and the overall process;

[0070] See also Figure 3 , including the first stage scheduling based on dynamic critical path, importance analysis of complex AOE network nodes, and the second stage scheduling based on improved variable neighborhood search algorithm (VNS);

[0071] The first part uses the constructed dynamic and static priorities as heuristic algorithms to achieve the first stage of production scheduling to optimize the critical path length;

[0072] The second part builds a complex assembly AOE network model (AOE network, Activity On Edge Network), and analyzes the importance of task nodes based on it to obtain the importance ranking of different nodes;

[0073] The third part performs local search optimization on the first-stage production scheduling results based on the improved variable neighborhood search algorithm to obtain better second-stage production scheduling results.

[0074] Embodiment 2:

[0075] This embodiment is used to construct a complex assembly scheduling AOE network and mathematical model, and the specific steps are as follows:

[0076] S201. A large number of AOs contained in complex assembly operations can be abstracted as a node network, reflecting the interaction between various elements. The network structure is the carrier of information dissemination, and the process can be abstracted as a weighted directed acyclic network, such as Figure 4 As shown in the figure, nodes represent AO tasks, arcs represent the pre- and post-task relationships between AOs, and weights represent the resource usage of post-task nodes. The AOE network contains the following information:

[0077] AOE=[V,E,ω ij ] (1)

[0078] Among them, V is the node set, E is the directed edge set, ω ij is the weight of the directed edge from i to j, ω ij The weight value represents the amount of resources occupied by node j. In an embodiment, the resources include time resources and personnel resources.

[0079] Resource usage includes:

[0080] ω ij =[t j ,p j ,n j ] (2)

[0081] Among them, t j is the working cycle of node j, representing the time resources consumed; p j is the personnel resources consumed by node j; n j The corresponding type of work of the personnel resources consumed by node j.

[0082] S202: Construct a complex assembly scheduling mathematical model to optimize the critical path length and the total scheduling time, as follows:

[0083] Constraint 1: All AOs can be completed only once, satisfying:

[0084] in, is the start time set of node j, there is only one start time, and n is the number of all nodes.

[0085] Constraint 2: The start and completion time of AO must satisfy the pre- and post-condition relationship, T i <T j ,j∈Ω(i),i∈Ω -1 (j), where Ω(i) is AO i The set of post-nodes, Ω -1 (j) is AO j The set of predecessor nodes.

[0086] Constraint 3: At any time, the total resources occupied by all jobs must not exceed the maximum value.

[0087]

[0088] Constraint 4: The difference between the start time and the end time of any operation must be greater than its theoretical operation cycle.

[0089]

[0090] The first stage optimization goal: min(L max ), in order to minimize the longest AO process chain length, that is, to minimize the critical path.

[0091] The second stage optimization goal: min(T max ), in order to minimize the total duty cycle.

[0092] Embodiment 3:

[0093] Table 1 Pseudocode of static priority algorithm

[0094]

[0095] This embodiment implements the first stage production scheduling based on the heuristic algorithm of the dynamic critical path, including the following steps:

[0096] S301. Calculate the static priority of the task node. The pseudo code is shown in Table 1 to determine the scheduling priority order of each task before production scheduling begins. The specific formula is as follows:

[0097] R i =max(R j )+T i ,j∈Ω(i) (5)

[0098] Among them, Ω(i) is the post-set of node i, T i Working hours for tasks;

[0099]

[0100] First, all task nodes are given an initial value of 0, and the static priority is calculated using formula (5). The recursive formula (6) is used to iterate repeatedly. When |R(t)-R(t-1)|=0 is finally satisfied, the iteration ends and the static priority of all task nodes is obtained.

[0101] S302, during the production scheduling process, the dynamic priority of the task is obtained. Due to the limitation of human resources, some AOs cannot be carried out as planned, resulting in delays, such as Figure 5 As shown in FIG. 1 , the delay time will cause the path length of the AO chain to increase, so the delay time needs to be considered in the priority.

[0102] Table 2 Dynamic priority algorithm pseudo code

[0103]

[0104] S303, the pseudo code for calculating the dynamic priority is shown in Table 2, and the specific process is as follows:

[0105] S3031. According to the initial static priority, select the task with the highest priority among the unscheduled tasks for scheduling.

[0106] S3032. After the selected task is scheduled, its priority is updated. The formula is:

[0107] R j '=R j +Δ delt , j∈Ω -1 (i) (7)

[0108] where Δ delt Delay time for the task.

[0109] S3033. Delete the completed tasks from the schedulable list, and import the subsequent tasks into the schedulable list.

[0110] S3034. When a subsequent task is imported into the schedulable list, it is verified whether all the predecessor tasks of the task have been completed. Otherwise, the task will not be imported into the list.

[0111] S3035. For the tasks successfully imported into the list, update their dynamic priorities. The formula is as follows:

[0112] Ri=max(R j '-T j ) (8)

[0113] Where R' j is the updated priority of the predecessor task, T j The completion time of the predecessor task.

[0114] S3036, loop iteration, until all tasks in the schedulable list are scheduled, the start time and end time of all tasks are obtained, and the first stage of scheduling is completed.

[0115] Embodiment 4:

[0116] In this embodiment, based on the built AOE network, an improved PageRank algorithm is proposed to calculate the importance of task nodes in the network. The flow chart is as follows Figure 6 As shown, the specific steps are as follows:

[0117] S401. The improvements to the PageRank algorithm mainly include the following:

[0118] Change the usage scenario from directed loop network to directed acyclic network;

[0119] All tasks must be completed;

[0120] The importance weight assignment relationship is changed from equally sharing the predecessor importance to completely inheriting the predecessor importance. The importance of the predecessor task is greater than or equal to the successor task, that is:

[0121] PR i ≥PR j ,L i→j =1 (9)

[0122] Change the forward loop calculation importance to the reverse loop calculation importance.

[0123] Furthermore, these improvements aim to make the PageRank algorithm applicable to production scheduling networks.

[0124] S402, input the pre- and post-relationship matrix and the task time matrix. The pre- and post-relationship matrix is ​​as follows:

[0125]

[0126]

[0127] S403, initialize the importance matrix to 0, the matrix is ​​as follows:

[0128] PR=(0,…,0) T (12)

[0129] S404. Calculate the task importance by improving the PageRank algorithm formula, the formula is as follows:

[0130]

[0131] Among them, T i is the working period of the current node, max(PR j ) is the maximum PR value item among all the subsequent nodes, l is the longest path length from the sink to the node, is the coefficient, the selection range is [-1,0], n is the number of successors of the current node, and Ω(i) is the set of successor nodes.

[0132] S405, perform cyclic calculation through an iterative formula, the specific formula is as follows:

[0133]

[0134] S406. Stop iteration when the convergence condition |PR(t)-PR(t-1)|=0 is met.

[0135] Embodiment 5:

[0136] This embodiment uses improved variable neighborhood search to perform local search on the basis of the first stage production scheduling results, optimizes the quality of the solution, and obtains the second stage production scheduling results. The process is as follows Figure 7 , the specific steps are as follows:

[0137] S501. Due to the large scale of the model in complex assembly scheduling, the behavior of traversing all neighborhood structures in the VNS algorithm will lead to excessive calculations, so the neighborhood structure of VNS is improved. Instead of traversing all neighborhoods, randomly extract neighborhoods for search, and the size of all neighborhoods is gradually reduced with iteration, so as to gradually reduce randomness during the iteration process, which is conducive to convergence;

[0138] Furthermore, the process of the neighborhood structure part is as follows:

[0139] S5011, given an initial solution S;

[0140] S5012. Define n neighborhood structures, the set is denoted by N k (k=1,2,3……n),i=1. For the neighborhood structure, take each node as the center, take the node as the center, and R as the radius to determine the neighborhood. The number of neighborhood structures is the same as the number of nodes;

[0141] S5013, the actions of the neighborhood operation include random transformation, inversion, and random insertion;

[0142] S5014. During the iteration process, gradually reduce the neighborhood radius R'=R(1-α*gen / MAXGEN), 0<α<1, where gen is the current iteration number and MAXGEN is the maximum iteration number.

[0143] S502. Based on the improved PageRank algorithm, we obtained the influence of the working nodes in the network. The different orders of these influential nodes in the scheduling will have a greater impact on the subsequent nodes. Therefore, when performing local search operations, we performed more neighborhood searches for these influential nodes.

[0144] S503, design a new neighborhood action, sort the tasks in the selected neighborhood in reverse order according to the importance obtained in the previous text, and divide them into three equal-length sub-codes, and perform actions such as reversing, inserting, and randomly changing the sub-codes separately, as shown in the following example: Figure 8 shown.

[0145] S504: Set the number of iterations and obtain the final scheduling result.

[0146] Embodiment 6:

[0147] See also Fig. 9 As shown, the present invention also provides an electronic device 100 for a complex assembly scheduling method based on complex AOE network key node analysis; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0148] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the complex assembly scheduling method based on complex AOE network key node analysis described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0149] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0150] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a complex assembly scheduling method based on complex AOE network key node analysis, and the processor 102 can execute the plurality of instructions to implement:

[0151] Collect relevant information of complex assembly operations and construct a weighted directed acyclic network model based on the relevant information of complex assembly operations;

[0152] According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed, and the influence and ranking of all task nodes are obtained by combining the improved PageRank algorithm;

[0153] Based on the weighted directed acyclic network model and the influence and ranking of all task nodes, the dynamic priority of all task nodes is obtained as the scheduling priority to form the first-stage production scheduling result;

[0154] The first-stage production scheduling results are locally optimized and supplemented with auxiliary search to obtain the second-stage local search optimization results.

[0155] Embodiment 7:

[0156] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0157] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A complex assembly scheduling method based on complex AOE network key node analysis, characterized in that: The following steps are involved: Collect relevant information of complex assembly operations and construct a weighted directed acyclic network model based on the relevant information of complex assembly operations; According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed, and the influence and ranking of all task nodes are obtained by combining the improved PageRank algorithm; Based on the weighted directed acyclic network model and the influence and ranking of all task nodes, the dynamic priority of all task nodes is obtained as the scheduling priority to form the first-stage production scheduling result; The first-stage production scheduling results are locally optimized and supplemented with auxiliary search to obtain the second-stage local search optimization results.

2. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1 is characterized in that: The relevant information of complex assembly operations includes the task predecessor and successor relationship information, the task personnel resource usage information and the task time resource usage information.

3. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1 is characterized in that: According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed. Combined with the improved PageRank algorithm, the specific method of obtaining the influence and ranking of all task nodes is as follows: According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed and initialized; Import the initialized task node influence matrix into the improved PageRank algorithm to calculate the influence value; The loop is iterated until the convergence condition is met, and the influence and ranking of all task nodes are obtained.

4. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1 is characterized in that: Based on the weighted directed acyclic network model and the influence and ranking of all task nodes, the dynamic priority of all task nodes is obtained as the scheduling priority to form a specific method for one-stage production scheduling as follows: Based on the weighted directed acyclic network model, dynamic programming is used to reversely calculate the static priority of task nodes according to the influence and ranking of all task nodes; According to the static priority of the task node and the scheduling constraints, the highest static priority of the task node is selected for scheduling; During the scheduling process, task delays are taken into account and the dynamic priorities of all task nodes are updated; Use dynamic priority as scheduling priority; The loop is iterated until all tasks are scheduled and the first-stage production scheduling results are obtained.

5. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1 is characterized in that: The specific method of performing local optimization and auxiliary search on the first-stage production scheduling results to obtain the second-stage local search optimization results is as follows: The improved variable neighborhood search algorithm is used to locally optimize the first-stage production scheduling results; During the optimization process, an auxiliary search algorithm for important task nodes is added to perform auxiliary search; The loop is iterated until the stop condition is met to obtain the second stage production scheduling result.

6. Complex assembly scheduling system based on complex AOE network key node analysis, characterized by: include: AOE network model building module, used to collect relevant information of complex assembly operations and build a weighted directed acyclic network model based on the relevant information of complex assembly operations; The influence and ranking acquisition module is used to construct the task node influence matrix based on the weighted directed acyclic network model, and combine the improved PageRank algorithm to obtain the influence and ranking of all task nodes; The first-stage production scheduling result acquisition module is used to obtain the dynamic priority of all task nodes as the scheduling priority based on the weighted directed acyclic network model module and the influence and ranking of all task nodes to form the first-stage production scheduling result; The second-stage local search optimization result acquisition module is used to perform local optimization and auxiliary search on the first-stage production scheduling results to obtain the second-stage local search optimization results.

7. The complex assembly scheduling system based on complex AOE network key node analysis according to claim 6 is characterized in that: The functions of the influence and ranking acquisition module are implemented through the following methods: According to the weighted directed acyclic network model, the task node influence matrix is ​​constructed and initialized; Import the initialized task node influence matrix into the improved PageRank algorithm to calculate the influence value; The loop is iterated until the convergence condition is met, and the influence and ranking of all task nodes are obtained.

8. The complex assembly scheduling system based on complex AOE network key node analysis according to claim 6 is characterized in that: The functions of the first-stage production scheduling result acquisition module are implemented through the following methods: Based on the weighted directed acyclic network model, dynamic programming is used to reversely calculate the static priority of task nodes according to the influence and ranking of all task nodes; According to the static priority of the task node and the scheduling constraints, the highest static priority of the task node is selected for scheduling; During the scheduling process, task delays are taken into account and the dynamic priorities of all task nodes are updated; Use dynamic priority as scheduling priority; The loop is iterated until all tasks are scheduled and the first-stage production scheduling results are obtained.

9. The complex assembly scheduling system based on complex AOE network key node analysis according to claim 6 is characterized in that: The function of the two-stage local search optimization result acquisition module is realized by the following method: The improved variable neighborhood search algorithm is used to locally optimize the first-stage production scheduling results; During the optimization process, an auxiliary search algorithm for important task nodes is added to perform auxiliary search; The loop is iterated until the stop condition is met to obtain the second stage production scheduling result.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the complex assembly scheduling method based on complex AOE network key node analysis described in any one of claims 1 to 5 are implemented.

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