Multi-target ship middleware workshop welding machine mixed scheduling method based on sparrow algorithm

Abstract

The invention discloses a multi-target ship middleware workshop welding machine mixed scheduling method based on a sparrow algorithm. The method comprises the following steps: (1) acquiring data of a ship middleware workshop; (2) constructing a scheduling model; (3) carrying out initialization processing on the acquired data of the welding machine, the workpiece and the working procedure and the welding time; (4) solving by using a sparrow search algorithm to obtain a welding machine scheduling scheme; (5) solving the key path of the scheme to obtain the total time consumed by the welding task; (6) traversing all procedures of the scheme to obtain total energy consumption; (7) calculating a target function, judging whether a termination condition is met, if so, outputting a solution, otherwise, returning to the step (4); (8) the scheduling of the whole welding machine is evaluated; the problem of ship middleware workshop welding machine mixed scheduling is solved.

Description

Technical Field

[0001] The invention relates to the field of ship middleware workshop manufacturing, and in particular to a multi-objective ship middleware workshop welder hybrid scheduling method based on a sparrow search algorithm. Background Art

[0002] With the rapid development of the manufacturing industry, the ship middleware workshop is a vital link in the shipbuilding process. It undertakes the connection and coordination tasks between various systems and modules of the ship, and is the key to ensuring the perfect function and stable operation of the ship. However, with the rapid development of the shipbuilding industry and the intensification of market competition, the production efficiency and quality of the ship middleware workshop are facing higher and higher requirements, and the scheduling of workpiece processes and machines has become a key factor restricting production efficiency. The hybrid scheduling of welders in the ship middleware workshop refers to the optimization of the scheduling of welders in the welding process based on the workpiece, process and welder data, aiming at the minimum completion time and total energy consumption of the workshop. Researching and solving this problem is of great significance to reducing the maximum completion time and total energy consumption of the ship middleware workshop.

[0003] At present, the mixed scheduling problem of welders in ship middleware workshops has become one of the important challenges facing the manufacturing industry. The quality of its scheduling scheme directly affects the cost and production efficiency. As a new type of swarm intelligence optimization algorithm, the sparrow search algorithm has the characteristics of strong global search ability and fast convergence speed, which provides a new way to solve the mixed scheduling problem of welders in multi-objective ship middleware workshops. The algorithm simulates the foraging and enemy avoidance behavior of sparrows, and finds the optimal solution to the problem by continuously updating the position and speed of sparrows. In the welding machine scheduling problem, the order of workpieces and the allocation of machines are key decision variables. By applying the sparrow search algorithm, these decision variables can be optimized, thereby improving the production efficiency of the ship middleware workshop. Specifically, the algorithm can automatically adjust the processing order of workpieces and the allocation strategy of machines according to the actual situation and constraints of the workshop, with the goal of minimizing the completion time and total energy consumption. The algorithm has a strong global search ability and can avoid falling into the local optimal solution. Secondly, the algorithm converges quickly and can find a better scheduling scheme in a shorter time. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm to solve the ship middleware workshop welder hybrid scheduling problem.

[0005] Technical solution: The multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm described in the present invention comprises the following steps:

[0006] (1) Obtain data from the ship middleware workshop;

[0007] (2) Using the acquired data, a scheduling model is constructed with the goal of minimizing the completion time and total energy consumption of the welding workshop;

[0008] (3) Initializing the acquired welding machine, workpiece, process data and welding time;

[0009] (4) Use the sparrow search algorithm to solve the welding machine scheduling solution;

[0010] (5) Find the critical path of the solution and obtain the total time consumed by the welding task;

[0011] (6) Traverse all the processes of the scheme and obtain the total energy consumption;

[0012] (7) Calculate the objective function and determine whether the termination condition is met. If so, output the solution; otherwise, return to (4);

[0013] (8) Evaluate the entire welding machine scheduling.

[0014] Furthermore, the step (1) includes: the number of processes for each workpiece in the workshop, the power information of the welding machine, and the welding time.

[0015] Furthermore, in step (2), the minimum completion time and total energy consumption objective function is established as follows:

[0016] F=λ·C max +(1-λ)·E t

[0017] Among them, C max is the completion time, E t is the total energy consumption, and λ is the weight to balance the completion time and total energy consumption.

[0018] The constraints in the scheduling process include: the number of workpieces assigned to the workshop, the number of welders assigned to each workpiece at the same time, the power of welders in different processes in each workshop, the wire feeding speed of welders in each workshop, and the completion time of each workpiece.

[0019] Furthermore, the data initialization process in step (3) is as follows:

[0020] The number of workpieces in each workshop = total number of workpieces / number of workshops,

[0021] The number of workpieces is the number of workpieces allocated to each workshop; the allocation rule is the "Largest Processing Time First" (LPT) rule: first sort the workpieces in descending order according to their processing time, and then allocate each workpiece in turn to the factory with the shortest current processing time;

[0022] The initialization of the welding machine is as follows:

[0023] Set the initial number of welders on each process to 1;

[0024] This number means that each workpiece is welded by one welder before processing begins;

[0025] The welding time is initialized as follows:

[0026] Assign a set of initial values ​​to the welding time of each welding machine according to relevant rules;

[0027] Furthermore, in the welding machine scheduling plan obtained in step (4), the total time must be controlled within a minimum range while the total energy consumption must be reduced.

[0028] Furthermore, in step (5), the number of welders on the critical path and the non-critical path is increased or decreased accordingly according to the critical path.

[0029] Furthermore, in step (6), the total energy consumption is obtained based on the defined energy consumption of the welding machines and the distribution pattern of the welding machines obtained in step (5).

[0030] Furthermore, in step (8), the evaluation criteria are the total time and total energy consumption required to complete the welding tasks of all workpieces in all processes.

[0031] The multi-objective ship middleware workshop welder hybrid scheduling system based on the sparrow algorithm of the present invention comprises:

[0032] Data module: used to obtain data from the ship middleware workshop;

[0033] Scheduling module: used to build a scheduling model by using the acquired data to minimize the completion time and total energy consumption of the welding workshop;

[0034] Initialization module: used to initialize the acquired welding machine, workpiece and process data as well as welding time;

[0035] Sparrow search module: used to use the sparrow search algorithm to solve the welding machine scheduling solution;

[0036] Total time module: used to solve the critical path of the solution and obtain the total time consumed by the welding task;

[0037] Total energy consumption module: used to traverse all the processes of the solution and obtain the total energy consumption;

[0038] Judgment module: used to calculate the objective function and determine whether the termination condition is reached. If yes, the solution is output; otherwise, it returns to the sparrow search module;

[0039] Evaluation module: used to evaluate the entire welding machine scheduling.

[0040] A device described in the present invention includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, any one of the multi-objective ship middleware workshop welder hybrid scheduling methods based on the sparrow algorithm is implemented.

[0041] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: it makes full use of the information between different workpieces and welding machines, and improves the efficiency and accuracy of cross-workshop welding machine scheduling; optimizes the hybrid scheduling scheme of welding machines by utilizing a multi-objective sparrow search algorithm, and enhances the robustness and applicability of the scheduling scheme; dynamically adjusts the allocation of welding machines and the order of workpiece processing through intelligent algorithms, significantly improves the production efficiency and energy utilization efficiency of the workshop, solves the problems that are difficult to overcome by traditional scheduling methods, and thus improves the overall scheduling performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0044] like Figure 1 As shown, the embodiment of the present invention provides a multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm, comprising the following steps:

[0045] (1) Obtain data on workpieces, processes, and welders in the ship middleware workshop; the model studied in this paper is the scheduling of welders in the ship middleware workshop, so the data in a large workshop is generally selected to facilitate machine scheduling and algorithm optimization. Including: the number of processes for each workpiece in the workshop, the power information of the welder, and the welding time. Determine the decision variables as the processing order and allocation of each workpiece in each factory. Assuming there are N workpieces and M workshops, the decision variables can be represented as a two-dimensional array, which includes the factory to which each workpiece is assigned and the processing order in the factory.

[0046] (2) Through the analysis of the acquired data, the scheduling model is constructed with the goal of minimizing the maximum completion time and total energy consumption of the welding workshop, and the constraints in the scheduling process are summarized. Maximum completion time: refers to the total time required to complete all workpieces, and our goal is to minimize it. Total energy consumption: involves the energy consumed by each workpiece during the welding process. Constraints ensure the feasibility of the scheduling plan, thereby achieving efficient and energy-saving welding operation scheduling. The objective function of minimizing the maximum completion time and total energy consumption is established as follows:

[0047] Maximum completion time: the final time when all workpieces are completed in all workshops.

[0048] C max =max i∈{1,2，...，N} C i

[0049] in,

[0050]

[0051] Total energy consumption: the sum of the energy consumed by all workpieces during the processing of all workshops.

[0052]

[0053] The objective function is defined as the weighted sum of these two objectives, with trade-offs made depending on the specific situation.

[0054] F=λ·minC max +(1-λ)·E t

[0055] The constraints in the scheduling process include:

[0056] The number of workpieces assigned to the workshop, the number of welders assigned to each workpiece at the same time, the power of the welders in each workshop, the wire feeding speed of the welders in each workshop, and the completion time of each workpiece.

[0057] (3) Initialize the acquired welding machine, workpiece, process data and welding time to facilitate the subsequent algorithm operation; initially, all workpieces are evenly distributed to the workshop, and the number of initial welding machines and the welding time and energy consumption of each welding machine are set. The initial allocation rule of workpieces is LPT: first sort the workpieces in descending order according to their processing time, and then assign each workpiece to the factory with the shortest current processing time. The data initialization process is as follows:

[0058] (31) According to the LPT rule: first sort the workpieces in descending order according to their processing time, and then assign each workpiece to the factory with the shortest current processing time.

[0059] (32) Initialization of welding machines: Set the number of initial welding machines in each process to 1.

[0060] This number means that each workpiece is welded by one welder before processing begins.

[0061] (33) Initialization of welding time: Assign a set of initial values ​​to the welding time of each welding machine according to relevant rules.

[0062] (4) Using the sparrow search algorithm to solve the welding machine scheduling solution; including the following steps:

[0063] (41) Initialization: Randomly generate the positions of a group of sparrows (i.e., the initial values ​​of the decision variables). The position of each sparrow represents a possible workpiece allocation scheme and processing sequence. The initial position X0 is generated by random assignment:

[0064] X0={ 1，0 , χ 2，0 , ..., χ n，0}

[0065] (42) Foraging behavior: Update the position of the sparrow through a local search strategy to simulate the process of sparrow foraging. This can be achieved by slightly adjusting the processing order or workshop allocation of the workpieces;

[0066] X new =X current +ΔX,X current is the current location.

[0067] (43) Vigilance behavior: Some sparrows are responsible for vigilance to avoid the threat of predators. In the algorithm, this can be explored by selecting a worse solution from the current solution to avoid the local optimum;

[0068] X alert =X worst +rand()·(X best -X worst ),

[0069] Among them, X best For the best location.

[0070] (44) Flight behavior: The positions of the sparrows are updated based on the communication between the sparrows (e.g., guided search by the global optimal solution). The sparrows update their positions based on the global information. If sparrow k finds a better position, it adjusts its position through flight behavior:

[0071] X k，new =X k，current +r·(X g_best -X k，current ),

[0072] Among them, X g_best is the best global position found so far.

[0073] (45) Update and iteration:

[0074] In each iteration, according to the newly found X new Update X current , and evaluate the quality of the new position, repeating the iteration until the termination condition is met (such as reaching the maximum number of iterations or the improvement of the solution is less than a certain threshold);

[0075] (46) Decoding and application of the solution:

[0076] The optimal solution (i.e., the optimal sparrow position) found by the algorithm is decoded to obtain the optimal processing sequence and allocation plan for each workpiece.

[0077] (5) Find the critical path in the scheduling process, increase the number of welders on the critical path while reducing the number of welders on the non-critical path, and obtain the total time consumed by the welding task; wherein, according to the critical path, the number of welders on the critical path and the non-critical path is increased or decreased accordingly.

[0078] (6) Traversing all the processes of the scheme, and obtaining the total energy consumption according to the number of welders in each process and the initial energy consumption of a single welder; wherein the total energy consumption is obtained according to the defined energy consumption of the welders and the distribution of the welders obtained in step (5).

[0079] (7) Calculate the objective function and determine whether the termination condition is met. If so, output the solution; otherwise, continue with step (4).

[0080] (8) The evaluation criteria for the entire welding machine scheduling are the total time and total energy consumption required to complete the welding tasks of all workpieces in all processes.

[0081] The following takes the ship middleware workshop data as an example to give the specific steps of this implementation example:

[0082] Step 1: Collect the number of workpieces to be processed, the number of processes, the power of the welding machine in each workshop, the wire feeding speed, and the welding time of each workpiece. The welding time and energy consumption of the workpiece are related to the length of the process and the wire feeding speed, and the transfer time of the workpiece between different processes is a fixed value. In this example, the number of workpieces is 20, the number of processes is 5, and the number of workshops is 2.

[0083] Step 2: By analyzing the acquired data, determine the objective function as the minimum completion time and total energy consumption, build a scheduling model, and summarize the constraints in the scheduling process.

[0084] Step 3: Initialize the data:

[0085] The power sequence of the welding machines in each process in each workshop is defined as: powers: {{0.4, 0.6, 0.8, 0.6, 0.4}, {0.2, 0.3, 0.4, 0.3, 0.2}};

[0086] The welding time series of each workpiece in each process is defined as: {{10, 12, 13, 15, 20}, {13, 22, 30, 21, 16}, {20, 24, 26, 28, 14}, {15, 43, 22, 14, 21}, {33, 24, 26, 30, 25}, {41, 30, 28, 23, 39}, {12, 30, 26, 10, 12}, {43, 22, 14, 26, 28}, {14, 34, 15, 32, 17}, {22, 33 , 36, 40, 40}, {21, 24, 27, 30, 22}, {32, 16, 17, 21, 24}, {16, 22, 24, 25, 32}, {21, 23, 14, 34, 39}, {31, 24, 16, 25, 16}, {17, 21, 25, 29, 31}, {27, 28, 24, 12, 29}, {18, 18, 19, 19, 22}, {11, 14, 34, 39, 15}, {21, 28, 25, 29, 26}};

[0087] According to the LPT rules, workpieces are allocated to each workshop. The number of workpieces in each workshop = the total number of workpieces / number of workshops. The initial number of welders in each process is set to 1, and a set of initial values ​​are assigned to the welding time of each welder according to relevant rules.

[0088] Step 4: Use the sparrow search algorithm to solve the welding machine scheduling solution. The specific steps are as follows:

[0089] (41) Initialization: Randomly generate the positions of a group of sparrows (i.e., the initial values ​​of the decision variables). The position of each sparrow represents a possible workpiece allocation scheme and processing sequence. The initial position X0 is generated by random assignment:

[0090] X0={ 1,0 , χ 2,0 , ..., χ n，0}

[0091] (42) Foraging behavior: Update the position of the sparrow through a local search strategy to simulate the process of sparrow foraging. This can be achieved by slightly adjusting the processing order or workshop allocation of the workpieces;

[0092] X new =X current +ΔX,X current is the current location.

[0093] (43) Vigilance behavior: Some sparrows are responsible for vigilance to avoid the threat of predators. In the algorithm, this can be explored by selecting a worse solution from the current solution to avoid the local optimum;

[0094] X alert =Xworst +rand()·(X best -X worst ),

[0095] Among them, X best For the best location.

[0096] (44) Flight behavior: The positions of the sparrows are updated based on the communication between the sparrows (e.g., guided search by the global optimal solution). The sparrows update their positions based on the global information. If sparrow k finds a better position, it adjusts its position through flight behavior:

[0097] X k，new =X k，current +r·(X g_best -X k，current ),

[0098] Among them, X g_best is the best global position found so far.

[0099] (45) Update and iteration:

[0100] In each iteration, according to the newly found X new Update X current , and evaluate the quality of the new position, repeating the iteration until the termination condition is met;

[0101] Step 5: Find the critical path in the scheduling process, increase the number of welders on the critical path and reduce the number of welders on the non-critical path. Within the specified upper and lower limits of welders, the number of welders increased or decreased each time is 1. The completion time of the workpiece is obtained. The calculation method of the completion time is:

[0102] Step 6: Traverse all the processes of the scheme, and obtain the total energy consumption according to the number of welders in each process and the initial energy consumption of a single welder; the total energy consumption is calculated as follows:

[0103] Step 7: Determine the solution:

[0104] Decode the optimal solution found by the algorithm to obtain the optimal processing sequence and allocation plan for each workpiece. Decoding is divided into the following three steps:

[0105] Obtain the processing order of the workpieces in the factory according to the arrangement order of the workpieces;

[0106] Obtain the processing factory corresponding to the workpiece according to the sequence of the workpiece allocation factory;

[0107] According to the assigned machine sequence, the number of machines corresponding to the workpiece is obtained.

[0108] Some of the decoding results are as follows:

[0109] Artifact sequence: [11, 3, 6, 9, 8, 16, 10, 5, 1, 18, 7, 15, 19, 12, 13, 2, 17, 4, 14, 20],

[0110] Allocation Factory: [2,1,2,2, 2,2,1,1, 1, 2,1,2, 1, 2,1,2, 1, 1,1,2],

[0111] The machine sequence of the first factory: [[3, 3, 3, 1, 1, 1, 1, 1, 1], [4, 3, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 1, 1, 1], [3, 2, 1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 1, 1, 2, 1, 1, 3]],

[0112] Machine sequence of the second factory: [[3,3,3,3,1,1,1,1,1,1,1],[4,3,2,1,1,1,1,2,1,1],[2,2,2,1,1,1,1,1,1,2],[3,2,1,1,1,1,1,1,1,1,2],[1,1,1,1,1,1,1,1,1,1,3]]

[0113] Completion time C max :603.85

[0114] The total energy consumption is E t :81.0

[0115] Step 8: Evaluate its performance in terms of maximum completion time and total energy consumption. Assuming that the minimum completion time and total energy consumption are equally weighted, then F = λ minC max +(1-λ)·E t In the equation, λ is 0.5, that is, Apply the data decoded in step (4), and the final F is: 342.43.

[0116] From the results of the welding machine scheduling, employees can further analyze and evaluate the performance of this scheduling plan in two aspects, namely, completion time and total energy consumption. Through the specific workpiece sequence, the assigned workshop and the sequence of machines in each workshop, we can have a clear understanding of the entire welding operation process to achieve higher production efficiency and energy efficiency.

[0117] The embodiment of the present invention also provides a multi-objective ship middleware workshop welding machine hybrid scheduling system based on the sparrow algorithm, comprising:

[0118] Data module: used to obtain data from the ship middleware workshop;

[0119] Scheduling module: used to build a scheduling model by using the acquired data to minimize the completion time and total energy consumption of the welding workshop;

[0120] Initialization module: used to initialize the acquired welding machine, workpiece and process data as well as welding time;

[0121] Sparrow search module: used to use the sparrow search algorithm to solve the welding machine scheduling solution;

[0122] Total time module: used to solve the critical path of the solution and obtain the total time consumed by the welding task;

[0123] Total energy consumption module: used to traverse all the processes of the solution and obtain the total energy consumption;

[0124] Judgment module: used to calculate the objective function and determine whether the termination condition is reached. If yes, the solution is output; otherwise, it returns to the sparrow search module;

[0125] Evaluation module: used to evaluate the entire welding machine scheduling.

[0126] An embodiment of the present invention also provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the multi-objective ship middleware workshop welder hybrid scheduling methods based on the sparrow algorithm is implemented.

Claims

1. A multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm, characterized in that: The following steps are involved: (1) Obtain data from the ship middleware workshop; (2) Using the acquired data, a scheduling model is constructed with the goal of minimizing the completion time and total energy consumption of the welding workshop; (3) Initializing the acquired welding machine, workpiece, process data and welding time; (4) Use the sparrow search algorithm to solve the welding machine scheduling solution; (5) Find the critical path of the solution and obtain the total time consumed by the welding task; (6) Traverse all the processes of the scheme and obtain the total energy consumption; (7) Calculate the objective function and determine whether the termination condition is met. If so, output the solution; otherwise, return to (4); (8) Evaluate the entire welding machine scheduling.

2. According to the multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm of claim 1, it is characterized in that: The step (1) includes: the process number of each workpiece in the workshop, the power information of the welding machine, and the welding time.

3. According to the multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm of claim 1, it is characterized in that: In step (2), the minimum completion time and total energy consumption objective function are established as follows: F=λ·Cmax+(1-λ)·Et Among them, Cmax is the completion time, Et is the total energy consumption, and λ is the weight for balancing the completion time and total energy consumption. The constraints in the scheduling process include: the number of workpieces assigned to the workshop, the number of welders assigned to each workpiece at the same time, the power of welders in different processes in each workshop, the wire feeding speed of welders in each workshop, and the completion time of each workpiece.

4. According to the multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm of claim 1, it is characterized in that: The data initialization process in step (3) is as follows: The number of workpieces in each workshop = total number of workpieces / number of workshops, The number of workpieces is the number of workpieces allocated to each workshop; the allocation rule is the "Largest Processing Time First" (LPT) rule: first sort the workpieces in descending order according to their processing time, and then allocate each workpiece in turn to the factory with the shortest current processing time; The initialization of the welding machine is as follows: Set the initial number of welders on each process to 1; This number means that each workpiece is welded by one welder before processing begins; The welding time is initialized as follows: A set of initial values ​​is assigned to the welding time of each welding machine according to relevant rules.

5. According to a multi-objective ship middleware workshop ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm of claim 1, it is characterized in that: In the step (4), the welding machine scheduling plan obtained must control the total time within a minimum range while also reducing the total energy consumption.

6. According to a multi-objective ship middleware workshop ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm of claim 1, it is characterized in that: In the step (5), the number of welders on the critical path and the non-critical path is increased or decreased accordingly according to the critical path.

7. The multi-objective ship middleware workshop ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm according to claim 1 is characterized in that: In the step (6), the total energy consumption is obtained according to the defined energy consumption of the welding machines and the distribution pattern of the welding machines obtained in the step (5).

8. The multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm according to claim 1 is characterized in that: In step (8), the evaluation criteria are the total time and total energy consumption required to complete the welding tasks of all workpieces in all processes.

9. A multi-objective ship middleware workshop welding machine hybrid scheduling system based on the sparrow algorithm, characterized in that: include: Data module: used to obtain data from the ship middleware workshop; Scheduling module: used to build a scheduling model by using the acquired data to minimize the completion time and total energy consumption of the welding workshop; Initialization module: used to initialize the acquired welding machine, workpiece and process data as well as welding time; Sparrow search module: used to use the sparrow search algorithm to solve the welding machine scheduling solution; Total time module: used to solve the critical path of the solution and obtain the total time consumed by the welding task; Total energy consumption module: used to traverse all the processes of the solution and obtain the total energy consumption; Judgment module: used to calculate the objective function and determine whether the termination condition is reached. If yes, the solution is output; otherwise, it returns to the sparrow search module; Evaluation module: used to evaluate the entire welding machine scheduling.

10. A device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, a multi-objective ship middleware workshop welder hybrid scheduling method based on the sparrow algorithm as described in any one of claims 1 to 7 is implemented.

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