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

Abstract

The application 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. The application can dynamically adjust the priority of each task node by combining the improved PageRank algorithm to calculate the influence degree and ranking of the task node. The dynamic priority allocation can make a quick response according to real-time data in the production process, timely cope with production disturbance and plan changes, and ensure that the scheduling result has high flexibility. The application 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 degree matrix, thereby effectively allocating resources and adjusting the task priority, avoiding resource conflicts or excessive concentration, and improving the overall efficiency of production scheduling.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of complex assembly scheduling, and particularly relates to a complex assembly scheduling method based on complex AOE network key node analysis and a related system. BACKGROUND

[0002] Complex products such as aircraft, spacecraft and automobiles have the characteristics of complex manufacturing processes, complex production management and complex product technologies. With China gradually transforming into high-end manufacturing, the requirements for complex product manufacturing are becoming higher and higher, and the manufacturing of complex products is facing higher challenges. Complex products involve the coordinated operation of a large number of assembly plans, personnel, materials, tooling components, etc., and are subject to many production disturbances and variable plans, which makes production scheduling difficult and complex. In order to improve the production capacity of complex products and strengthen the rapid response of scheduling to complex production assembly, how to design an effective scheduling strategy and quickly achieve the best scheduling under limited resources is a problem that needs to be solved urgently. SUMMARY

[0003] The application aims to overcome the difficulty and complexity of production scheduling caused by many production disturbances and variable plans, and provide a complex assembly scheduling method based on complex AOE network key node analysis.

[0004] In order to achieve the above-mentioned purpose, the application adopts the following technical solutions:

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

[0006] Collecting relevant information of complex assembly work, and constructing a weighted directed acyclic network model according to the relevant information of complex assembly work;

[0007] According to the weighted directed acyclic network model, constructing a task node influence degree matrix, and combining an improved PageRank algorithm to obtain the influence degree and ranking of all task nodes;

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

[0009] Carrying out local optimization and auxiliary search on the one-stage production scheduling result to obtain a two-stage local search optimization result.

[0010] The application is further improved in that the relevant information of complex assembly work includes pre-post relationship information of work tasks, personnel resource use information of tasks and time resource use information of tasks.

[0011] The further improvement of the present application is that, according to the weighted directed acyclic network model, the influence degree matrix of the task nodes is constructed, and the specific method of obtaining the influence degree and the ranking of all the task nodes by combining the improved PageRank algorithm is as follows:

[0012] According to the weighted directed acyclic network model, the influence degree matrix of the task nodes is constructed, and the specific method of obtaining the influence degree and the ranking of all the task nodes by combining the improved PageRank algorithm is as follows:

[0013] The initialized influence degree matrix of the task nodes is imported into the improved PageRank algorithm, and the influence degree value is calculated.

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

[0015] The further improvement of the present application is that, based on the weighted directed acyclic network model and the influence degree and the ranking of all the task nodes, the dynamic priority of all the task nodes is obtained as the scheduling priority, and the specific method of forming the one-stage production scheduling is as follows:

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

[0017] According to the static priority of the task nodes and the scheduling constraint, the highest static priority of the task nodes is selected for scheduling.

[0018] In the scheduling process, the dynamic priority of all the task nodes is updated with the task delay.

[0019] The dynamic priority is taken as the scheduling priority.

[0020] The loop iteration is performed until all the tasks are completed and the one-stage production scheduling result is obtained.

[0021] The further improvement of the present application is that, the one-stage production scheduling result is locally optimized and auxiliary searched, and the specific method of obtaining the two-stage local search optimization result is as follows:

[0022] The one-stage production scheduling result is locally optimized by using the improved variable neighborhood search algorithm.

[0023] In the optimization process, the important task node auxiliary search algorithm is added for auxiliary search.

[0024] The loop iteration is performed until the stop condition is met, and the second-stage production scheduling result is obtained.

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

[0026] The AOE network model construction module is configured to collect relevant information of the complex assembly operation and construct a weighted directed acyclic network model according to the relevant information of the complex assembly operation.

[0027] The influence degree and ranking acquisition module is configured to construct a task node influence degree matrix according to the weighted directed acyclic network model, and obtain the influence degree and ranking of all the task nodes by combining the improved PageRank algorithm.

[0028] The one-stage production scheduling result acquisition module is configured to obtain the dynamic priority of all the task nodes as the scheduling priority based on the weighted directed acyclic network model module and the influence degree and ranking of all the task nodes, and form a one-stage production scheduling result.

[0029] The two-stage local search optimization result acquisition module is configured to perform local optimization and auxiliary search on the one-stage production scheduling result, and obtain a two-stage local search optimization result.

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

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

[0032] The initialized task node influence degree matrix is imported into the improved PageRank algorithm to calculate the influence degree value.

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

[0034] The further improvement of the present application is that the function of the one-stage production scheduling result acquisition module is realized by the following method:

[0035] Based on the weighted directed acyclic network model, the static priority of the task nodes is calculated in reverse by dynamic programming according to the influence degree and ranking of all the task nodes.

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

[0037] During the scheduling process, the dynamic priority of all the task nodes is updated with more task delays.

[0038] The dynamic priority is taken as the scheduling priority.

[0039] The loop is iterated until all the tasks are completed and the one-stage production scheduling result is obtained.

[0040] The further improvement of the present application 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 scheduling result.

[0042] An important task node auxiliary search algorithm is added in the optimization process to perform auxiliary search.

[0043] The loop is iterated until a stop condition is met, and a second-stage scheduling result is obtained.

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

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

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

[0047] The application can dynamically adjust the priority of each task node by combining the improved PageRank algorithm to calculate the influence degree and ranking of the task node. This dynamic priority allocation can quickly respond to real-time data in the production process, timely respond to production disturbances and plan changes, and ensure the high flexibility of the scheduling result. The application 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 degree matrix, thereby effectively allocating resources and adjusting the priority of the task, avoiding resource conflicts or excessive concentration, and improving the overall efficiency of production scheduling. The two-stage local optimization and auxiliary search of the application can further improve the initial production scheduling result, reduce the problem of local optimal solution, and improve the quality and adaptability of the overall scheduling scheme. Through the optimization process, the scheduling scheme can be continuously corrected, so that the scheduling system has better adaptability to unexpected events (such as production line interruption, raw material shortage, etc.). The application is often accompanied by disturbances such as equipment failure and raw material supply fluctuation in the production process, and the traditional static scheduling method is difficult to cope with these unexpected situations. The method can timely respond to these disturbances by dynamically adjusting the priority of the task and local optimization, reduce the scheduling chaos caused by production disturbance or plan change, and keep the smooth progress of the production task. The improved PageRank algorithm can sort the scheduling tasks according to the influence degree of the task node, 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 realize accurate scheduling prediction and reduce the scheduling uncertainty caused by unexpected changes. In summary, the application can effectively improve the flexibility of the production scheduling system, optimize the scheduling efficiency, respond to production disturbances, and provide stable and predictable scheduling results in a dynamic environment. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart of the application;

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

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

[0051] Figure 4 is an AOE network diagram of the application;

[0052] Figure 5 is a dynamic and static critical path diagram of the application;

[0053] Figure 6 is a task node importance calculation flowchart of the application;

[0054] Figure 7 is a second-stage scheduling flowchart of the application;

[0055] Figure 8 A neighborhood transformation operation flowchart designed for the present application;

[0056] Figure 9 A system diagram for Example 6. DETAILED DESCRIPTION

[0057] For a further understanding of the present application, reference will be made to the following description taken in conjunction with the accompanying drawings and specific examples. It is to be understood that the examples are only for explanation and not for limitation.

[0058] Referring to Figure 1 , a complex assembly scheduling method based on complex AOE network key node analysis, comprising the following steps:

[0059] S1, collecting relevant information of complex assembly operation, and constructing a weighted directed acyclic network model according to the relevant information of complex assembly operation.

[0060] S2, constructing a task node influence degree matrix according to the weighted directed acyclic network model, and obtaining the influence degree and ranking of all task nodes in combination with the improved PageRank algorithm.

[0061] S3, obtaining the dynamic priority of all task nodes as the scheduling priority based on the weighted directed acyclic network model and the influence degree and ranking of all task nodes, and forming a one-stage production scheduling result.

[0062] S4, performing local optimization and auxiliary search on the one-stage production scheduling result to obtain a two-stage local search optimization result.

[0063] Referring to Figure 2 , a complex assembly scheduling system based on complex AOE network key node analysis, comprising:

[0064] An AOE network model construction module for collecting relevant information of complex assembly operation, and constructing a weighted directed acyclic network model according to the relevant information of complex assembly operation;

[0065] An influence degree and ranking acquisition module for constructing a task node influence degree matrix according to the weighted directed acyclic network model, and obtaining the influence degree and ranking of all task nodes in combination with the improved PageRank algorithm;

[0066] A one-stage production scheduling result acquisition module for obtaining the dynamic priority of all task nodes as the scheduling priority based on the weighted directed acyclic network model module and the influence degree and ranking of all task nodes, and forming a one-stage production scheduling result;

[0067] A two-stage local search optimization result acquisition module for performing local optimization and auxiliary search on the one-stage production scheduling result to obtain a two-stage local search optimization result.

[0068] Embodiment 1:

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

[0070] Please refer to Figure 3 , respectively including the first stage scheduling based on dynamic critical path, complex AOE network node importance analysis, the second stage scheduling based on improved variable neighborhood search algorithm (VNS);

[0071] The first part realizes the first stage scheduling by constructing dynamic and static priority as a heuristic algorithm to optimize the critical path length;

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

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

[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 elements, and the network structure is the carrier of information propagation, and the process can be abstracted as a weighted directed acyclic network, as shown in Figure 4 . Among them, the node represents the AO task, the arc represents the precedence relationship between AOs, and the weight represents the resource occupation of the post-task node. The AOE network contains information as follows:

[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, and the weight value of ω ij represents the resource amount occupied by node j. In the embodiment, the resources include time resource and personnel resource.

[0079] Resource occupation includes:

[0080] ω ij = [t j , p j , n j ] (2)

[0081] wherein t j is the working period of node j, representing the time resource consumed by it; p j is the personnel resource consumed by node j; n j is the corresponding job type of the personnel resource consumed by node j.

[0082] S202, a complex assembly scheduling mathematical model is constructed to optimize the key path length and total scheduling time, specifically as follows:

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

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

[0085] Constraint 2: the start and end times of AOs need to satisfy the preceding and following relationships, T i <T j ,j∈Ω(i),i∈Ω -1 (j), wherein Ω(i) is the following node set of AO i , and Ω -1 (j) is the preceding node set of AO j .

[0086] Constraint 3: at any time, the total resource occupied by all jobs cannot exceed the maximum value,

[0087]

[0088] Constraint 4: the difference between the start time and the end time of any job must be greater than its theoretical working period.

[0089]

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

[0091] Second stage optimization goal: min(T max ), to minimize the total working period.

[0092] Embodiment 3:

[0093] Table 1 Pseudocode of static priority algorithm

[0094]

[0095] This embodiment realizes the first stage production scheduling based on the heuristic algorithm of 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 the production scheduling starts, and the specific formula is as follows:

[0097] R i = max(R j )+T i ,j∈Ω(i) (5)

[0098] Where Ω(i) is the post-set of node i, T i is the task working time;

[0099]

[0100] First, give all task nodes an initial value of 0, calculate the static priority by formula (5), and use recursive formula (6) to repeatedly iterate until |R(t)-R(t-1)|=0 is met, and the static priority of all task nodes is obtained.

[0101] S302, during the production scheduling, the dynamic priority of the task is obtained, due to the limitation of human resources, part of the AO cannot be carried out according to the original plan, thereby causing delay, as shown in formula (6), the delay time will cause the path length of the AO chain to increase, therefore, the delay time needs to be considered into the priority. Figure 5

[0102] Table 2 pseudo code of dynamic priority algorithm

[0103]

[0104] S303, the pseudo code of 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 in the unscheduled task to schedule.

[0106] S3032, after the selected task is completed, update its priority, and the formula is:

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

[0108] Where Δ delt is the task delay time.

[0109] S3033, delete the completed task in the schedulable list, and import the subsequent task into the schedulable list.

[0110] ​S3034, when importing the subsequent task into the schedulable list, verify whether all the preceding tasks of the task have been completed, otherwise do not import the list.

[0111] S3035, for the task successfully imported into the list, update its dynamic priority, the formula is as follows:

[0112] Ri = max (R j -T j ) (8)

[0113] Where R' j is the priority of the preceding task that has been updated, T j is the completion time of the preceding task.

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

[0115] Embodiment 4:

[0116] In this embodiment, based on the AOE network, the improved PageRank algorithm is used to calculate the importance of the task node in the network, and the flow chart is as shown in Figure 6 The specific steps are as follows:

[0117] S401, the improvement of PageRank algorithm mainly includes the following contents:

[0118] The use scene is changed from directed cyclic network to directed acyclic network;

[0119] All tasks must be completed;

[0120] The importance weight is given to the relationship from the equal division of the importance of the preceding task to the complete inheritance of the importance of the preceding task, and the importance of the preceding task is greater than or equal to the importance of the following task, that is:

[0121] PR i ≥ PR j ,L i→j = 1 (9)

[0122] The forward loop calculation importance is changed to the reverse loop calculation importance.

[0123] Further, the purpose of these improvements is to make the PageRank algorithm applicable to production scheduling network.

[0124] S402, input the preceding and following relationship matrix and task time matrix. The preceding and following 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] where, T i is the working period of the current node, max(PR j ) is the maximum item of PR value in all post-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 post-nodes of the current node, and Ω(i) is the post-node set.

[0132] S405, loop calculation is performed by iteration formula, and 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 is based on the first-stage scheduling result, and uses improved variable neighborhood search for local search to optimize the quality of the solution and obtain the second-stage scheduling result. The flow is as Figure 7 , and the specific steps are as follows:

[0137] S501, because the model size is too large in complex assembly scheduling, the behavior of traversing all neighborhood structures in VNS algorithm will cause too large calculation amount, therefore, the neighborhood structure of VNS is improved. The all-neighborhood traversal is replaced by random neighborhood extraction for search, and the size of all neighborhood structures is gradually reduced with iteration, so that the randomness is gradually reduced in the iteration process, which is helpful for convergence;

[0138] Further, the flow of the neighborhood structure part is as follows:

[0139] S5011, give an initial solution S;

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

[0141] S5013, the action of the neighborhood operation includes random transformation, reversal, random insertion;

[0142] S5014, in the iteration process, the neighborhood radius is gradually reduced R'=R(1-alpha*gen / MAXGEN), 0<alpha<1, wherein, gen is the current iteration round, and MAXGEN is the maximum iteration number.

[0143] S502, based on the improved PageRank algorithm, the influence degree of the working node in the network is obtained, different orders of these nodes with greater influence in scheduling will have greater influence on the post-nodes, therefore, when the local search operation is performed, more neighborhood searches are performed on the nodes with greater influence.

[0144] S503, a new neighborhood action is designed, the selected neighborhood internal task is reversely sorted according to the importance obtained in the foregoing, and is divided into three equal-length sub-codes, the sub-codes are reversely sorted, inserted and randomly changed, and the specific action is as shown in Figure 8 .

[0145] S504, the iteration number is set, and the final scheduling result is obtained.

[0146] Embodiment 6:

[0147] Referring to Figure 9 , the application further provides an electronic device 100 based on the complex AOE network key node analysis method of complex assembly scheduling; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and capable of running 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, and the processor 102 can realize the steps of the complex assembly scheduling method based on the complex AOE network key node analysis 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, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data (such as audio data) created according to the use of the electronic device 100. 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 media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0149] The at least one processor 102 can be a central processing unit (CPU), and 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 gates or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or can also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and is connected to various parts of the entire electronic device 100 through various interfaces and lines.

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

[0151] Collecting related information of the complex assembly operation, and constructing a weighted directed acyclic network model according to the related information of the complex assembly operation;

[0152] According to the weighted directed acyclic network model, a task node influence degree matrix is constructed, and the influence degree 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 degree and ranking of all task nodes, a dynamic priority of all task nodes is obtained as a scheduling priority to form a one-stage scheduling result;

[0154] The one-stage scheduling result is locally optimized and auxiliary searched to obtain a two-stage local search optimization result.

[0155] Embodiment 7:

[0156] The modules / units integrated in the electronic device 100, if realized in the form of software function units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed 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, the computer program can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, and read-only memory (ROM).

[0157] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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 application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for performing the functions specified in one or more flows and / or blocks Figure 1 The device for performing the functions specified in one or more flows and / or blocks

[0159] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The functions specified in the flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 The functions specified in the flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0161] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limiting the same. Even though the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently, and any modification or replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.

Claims

1. A complex assembly scheduling method based on complex AOE network key node analysis, characterized in that, The method comprises the following steps: Collecting relevant information of complex assembly work, and constructing a weighted directed acyclic network model according to the relevant information of the complex assembly work; According to the weighted directed acyclic network model, a task node influence degree matrix is constructed, and the influence degree 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 degree and ranking of all task nodes, the dynamic priority of all task nodes is obtained as the scheduling priority to form a one-stage production scheduling result; the specific method is as follows: S301, the static priority of the task node is calculated to determine the scheduling priority order of each task before production scheduling starts, and the specific formula is as follows: wherein, is a successor set of node i, is a task working time; First, all task nodes are given an initial value of 0, the static priority is calculated by the above formula, and the recursive formula is repeatedly iterated until the final static priority of all task nodes is met When the iteration is completed, the static priority of all task nodes is obtained. S302, during the production scheduling process, the dynamic priority of the task is obtained, due to the limitation of human resources, part of the AO cannot be carried out according to the original plan, thereby causing delay, and the delay time will cause the path length of the AO chain to increase, so the delay time needs to be considered into the priority; S303, the dynamic priority is calculated, and the specific process is as follows: S3031, according to the initial static priority, the task with the highest priority in the unscheduled task is selected for scheduling; S3032, after the selected task is completed, the priority thereof is updated, and the formula is as follows: wherein is the task delay time; S3033, the completed task is deleted from the schedulable list, and the subsequent task is imported into the schedulable list; S3034, when the subsequent task is imported into the schedulable list, it is verified whether all the preceding tasks of the task have been completed, and if not, the task is not imported into the list; S3035, for the task successfully imported into the list, the dynamic priority thereof is updated, and the formula is as follows: wherein a priority level that has been updated for the predecessor task, a completion time for the predecessor task; S3036, the loop iteration is performed until all the tasks in the schedulable list are completed, the start time and the end time of all the tasks are obtained, and the first-stage scheduling is completed; The one-stage production scheduling result is locally optimized and auxiliary searched to obtain a two-stage local search optimization result.

2. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1, characterized in that, The relevant information of the complex assembly work includes task precedence relationship information, personnel resource use information of the task and time resource use information of the task.

3. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1, characterized in that, According to the weighted directed acyclic network model, the task node influence degree matrix is constructed, and the influence degree and ranking of all task nodes are obtained by combining the improved PageRank algorithm; the specific method is as follows: According to the weighted directed acyclic network model, the task node influence degree matrix is constructed, and the influence degree and ranking of all task nodes are obtained by combining the improved PageRank algorithm; the specific method is as follows: According to the weighted directed acyclic network model, the task node influence degree matrix is constructed, and the influence degree and ranking of all task nodes are obtained by combining the improved PageRank algorithm; the specific method is as follows: Based on the weighted directed acyclic network model and the influence degree and ranking of all task nodes, the dynamic priority of all task nodes is obtained as the scheduling priority to form a one-stage production scheduling result; the specific method is as follows:

4. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1, characterized in that, Based on the weighted directed acyclic network model, the static priority of the task node is calculated according to the influence degree and ranking of all task nodes; According to the task node static priority and the scheduling constraint, the highest static priority of the task node is selected for scheduling; During the scheduling process, the dynamic priority of all task nodes is updated according to the task delay; The dynamic priority is taken as the scheduling priority; ​ Loop iteration to all tasks complete scheduling, and obtain a stage scheduling results.

5. The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1, wherein, The specific method of the second stage local search optimization result is as follows: The improved variable neighborhood search algorithm is used for local optimization of the first stage scheduling result; An important task node auxiliary search algorithm is added in the optimization process for auxiliary search; Loop iteration to meet the stopping condition, and obtain the second stage scheduling result.

6. A complex assembly scheduling system based on complex AOE network key node analysis, characterized in that, The complex assembly scheduling method based on complex AOE network key node analysis according to claim 1 comprises: An AOE network model construction module is configured to collect relevant information of complex assembly operations, and construct a weighted directed acyclic network model according to the relevant information of the complex assembly operations; An influence degree and ranking acquisition module is configured to construct a task node influence degree matrix according to the weighted directed acyclic network model, and obtain the influence degree and ranking of all task nodes by combining an improved PageRank algorithm; A first stage scheduling result acquisition module is configured 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 degree and ranking of all task nodes, and form a first stage scheduling result; A second stage local search optimization result acquisition module is configured to perform local optimization and auxiliary search on the first stage scheduling result to obtain a second stage local search optimization result.

7. The complex assembly scheduling system based on complex AOE network key node analysis of claim 6, wherein, The function of the influence degree and ranking acquisition module is realized by the following method: According to the weighted directed acyclic network model, a task node influence degree matrix is constructed and initialized; The initialized task node influence degree matrix is imported into the improved PageRank algorithm to calculate the influence degree value; Loop iteration to meet the convergence condition, and obtain the influence degree and ranking of all task nodes.

8. The complex assembly scheduling system based on complex AOE network key node analysis of claim 6, wherein, The function of the first stage scheduling result acquisition module is realized by the following method: Based on the weighted directed acyclic network model, the static priority of the task nodes is calculated in reverse direction according to the influence degree and ranking of all task nodes by using dynamic programming; According to the static priority of the task nodes and the scheduling constraints, the highest static priority of the task nodes is selected for scheduling; During the scheduling process, the dynamic priority of all task nodes is updated; The dynamic priority is used as the scheduling priority; Loop iteration to all tasks complete scheduling, and obtain a stage scheduling results.

9. The complex assembly scheduling system based on complex AOE network key node analysis of claim 6, wherein, The function of the second stage local search optimization result acquisition module is realized by the following method: The improved variable neighborhood search algorithm is used for local optimization of the first stage scheduling result; An important task node auxiliary search algorithm is added in the optimization process for auxiliary search; Loop iteration to meet the stopping condition, and obtain the second stage scheduling result.

10. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the complex assembly scheduling method based on complex AOE network key node analysis according to any one of claims 1 to 5.

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