A batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time

Abstract

This invention discloses a batch scheduling method for hot pressing of aerospace composites that considers resource constraints and dual preparation time, specifically relating to the field of intelligent manufacturing technology for aerospace composites. It involves acquiring relevant data for the hot pressing process, including workpiece data and autoclave data, and designing an objective function for batch scheduling of the hot pressing process of aerospace composites with the goal of minimizing the maximum completion time. A two-dimensional multi-layer loading hot pressing process batch scheduling model considering resource constraints and dual preparation time is established. Using the objective function as the optimization objective and the two-dimensional multi-layer loading hot pressing process batch scheduling model as the optimization model, an improved genetic tabu algorithm based on batch processing time is used to solve the problem, resulting in a batch scheduling scheme for the hot pressing process.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology for aerospace composites, specifically to a hot-pressing batch scheduling method for aerospace composites that takes into account resource constraints and dual preparation time. Background Technology

[0002] With the continuous improvement of aerospace manufacturing technology, the application ratio of composite materials in structural components is constantly increasing, and the demand is constantly growing, which places higher demands on the scheduling scheme of its manufacturing process. The hot pressing process, due to its long process flow, large production cycle, and high resource consumption, has become a key link restricting the improvement of composite manufacturing capacity. In current actual production, the workpiece tasks are diverse, the delivery cycle is tight, and the production schedule is frequently adjusted, resulting in increasing pressure on manufacturing organization. The hot pressing process faces significant challenges in terms of scheduling and resource coordination.

[0003] The prepreg loading method in this process can be deconstructed into a two-dimensional packing process, where the spatial arrangement of workpieces is constrained by the dimensions of both the workpieces and the equipment. Furthermore, the process involves multiple preparation stages before startup, including time for clamping and loading / unloading into and out of the tank, and time for technical preparations such as heating and vacuuming of the process system. These two types of preparation times differ significantly under different batching strategies, forming a typical dual preparation time structure. Simultaneously, the shared limited thermocouple and vacuum nozzle resources among workpieces can easily lead to low resource utilization and lengthy waiting times. Summary of the Invention

[0004] To address these issues, the present invention provides a batch scheduling method for hot pressing of aerospace composite materials that considers resource constraints and dual preparation time, thereby resolving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a batch scheduling method for hot pressing of aerospace composite materials considering resource constraints and dual preparation time, comprising the following steps:

[0006] Step 1: Obtain relevant data for the hot pressing process, including workpiece data and autoclave data. Design a batch scheduling objective function for the aerospace composite hot pressing process with the goal of minimizing the maximum completion time.

[0007] Step 2: Establish a two-dimensional multi-layer loading hot pressing batch scheduling model that considers resource constraints and dual preparation time;

[0008] Step 3: Using the objective function as the optimization objective and the two-dimensional multi-layer loading hot pressing process batch scheduling model as the optimization model, the improved genetic tabu algorithm based on batch processing time is used to solve the problem and obtain the hot pressing process batch scheduling scheme.

[0009] Preferably, step one establishes the objective function for hot pressing batch scheduling:

[0010] (1);

[0011] (2);

[0012] in, express The maximum completion time for a batch of workpieces. Batch set; Batch index; :batch Processing time, ; The minimum batch quantity is determined based on the workpiece area and resource quantity.

[0013] Formulas (1) and (2) define the objective function as minimizing the maximum completion time, and the maximum completion time is equal to the sum of the processing times of each batch.

[0014] Preferably, step two involves constructing a two-dimensional multi-layer loading hot pressing process batch scheduling model that considers resource constraints and dual preparation time, including: uniqueness constraints of workpiece processing, workpiece resource carrying capacity limits and dual preparation time, workpiece batch logic and process constraints, and spatial layout constraints.

[0015] Preferably, a unique constraint on workpiece processing is applied to ensure that each workpiece can be processed exactly once, as shown in the following formula:

[0016] (3);

[0017] Autoclave hierarchy; Autoclave Hierarchical Index; : , indicating workpiece Assigned to batch of Layers, otherwise 0, indicating a workpiece. Not assigned to batch of layer; ; : Collection of workpieces; : Workpiece index.

[0018] Preferably, the workpiece resource capacity limitations and dual preparation time include:

[0019] (1) Resource carrying capacity limit: The resource required for each workpiece shall not exceed the total resource capacity of the equipment layer in which it is located, in order to avoid equipment overload and ensure processing feasibility. The formula is as follows:

[0020] (4);

[0021] : workpiece Required resource quantity; : , indicating workpiece Assigned to batch of Layer, otherwise 0, indicating a workpiece. Not assigned to batch of layer; The first in the autoclave The number of resources contained in the layer;

[0022] (2) Dual preparation time: The dual preparation time for a batch is divided into tank entry / exit preparation time and technical preparation time. The tank entry / exit preparation time is related to the batch quantity, while the technical preparation time is linearly related to the total area of ​​all workpieces in the batch. The calculation logic for preparation time is clarified, and the formula is as follows:

[0023] (5);

[0024] (6);

[0025] :batch Preparation time for entering and exiting the tank. ; : Inlet and outlet time; : ,batch Processed, otherwise 0 indicates a batch. Not processed ; :batch Technical preparation time, ; The degree of influence of workpiece area on technical preparation time; : workpiece Length and width, ; : , indicating workpiece Assigned to batch of Layer, otherwise 0, indicating a workpiece. Not assigned to batch of layer, ;

[0026] (3) Curing time: The batch curing time is determined by the maximum processing time of a single workpiece within the batch, as follows:

[0027] (7);

[0028] ;batch The curing time, ; : workpiece Processing time, ;

[0029] (4) Processing time: Total batch processing time = Curing time + Technical preparation time. The composition of the processing cycle for a single batch is defined by the following formula:

[0030] (8);

[0031] :batch Technical preparation time, ; :batch Preparation time for entering and exiting the tank. .

[0032] Preferably, the workpiece batch logic and process constraints include:

[0033] (1) Batch processing sequence: Batch processing is carried out in sequence. The next batch can only start after the previous batch is completed, to ensure the continuity of the production process and avoid parallel conflicts. The formula is as follows:

[0034] (9);

[0035] (2) Batch activation conditions: A batch will only be activated when a workpiece is assigned to it, thus preventing empty batches and optimizing resource and time costs. The formula is as follows:

[0036] (10);

[0037] (3) Relaxation solution enhances constraints. The quality of the relaxation solution of the model is enhanced by effective inequalities. Formulas (9)-(10) provide high-quality lower bounds to help improve the solution efficiency and optimization accuracy (mostly used in algorithm optimization in mathematical modeling). The workpieces need to be arranged in descending order of processing time. The formula is as follows:

[0038] (11);

[0039] (12);

[0040] (13);

[0041] The first in the autoclave The width of the layer; The first in the autoclave The length of the layer; The first in the autoclave The number of resources in the layer; : workpiece Length and width, ; : workpiece Arranged to batch of Processing is performed on a single layer; otherwise, it is 0, indicating the workpiece. Not assigned to batch of The layers are processed;

[0042] Preferably, spatial layout constraints include load-bearing capacity constraints, non-overlapping constraints, and relative position constraints.

[0043] Preferably, the load-bearing capacity is constrained, and the workpiece placement must not exceed the physical boundaries of the equipment's load-bearing platform to ensure spatial adaptability. The formula is as follows:

[0044] (14);

[0045] (15);

[0046] : workpiece In the bottom left corner of the batch coordinate, ; : workpiece In the bottom left corner of the batch coordinate, ; : , indicating workpiece The length of the workpiece is parallel to the length of the machine; otherwise, it is 0, indicating that the workpiece is parallel to the length of the machine. The length is not parallel to the length of the machine.

[0047] Preferably, there are no overlap constraints, meaning that no two workpieces can overlap in the horizontal or vertical direction to avoid spatial conflicts and ensure the independence of workpieces during processing. The formula is as follows:

[0048] (16);

[0049] (17);

[0050] : workpiece In the bottom left corner of the batch coordinate, ; : workpiece In the bottom left corner of the batch coordinate, ; , indicating workpiece Workpieces in the same batch The left side is 0; otherwise, it is 0, indicating the workpiece. Workpieces not in the same batch To the left, ; : , indicating workpiece Workpieces in the same batch Below; otherwise, 0 indicates the workpiece. Workpieces not in the same batch Below, .

[0051] Preferably, relative position constraints define the relative positional relationship between any two workpieces within the same batch and layer, providing specific coordinates or orientation rules (such as left-right or front-back order) for spatial layout. The formula is as follows:

[0052] (18);

[0053] , indicating workpiece Assigned to batch of Layers, otherwise 0, indicating a workpiece. Not assigned to batch of layer.

[0054] Preferably, the solution process of the improved genetic tabu algorithm based on batch processing time is as follows:

[0055] (1) Determine the encoding and decoding strategy: Encode based on the workpiece sequence. The length of each chromosome is the workpiece sequence. Each gene position on the chromosome represents a workpiece. Different workpiece arrangements represent different scheduling schemes. Decode using a greedy strategy to form an initial solution.

[0056] (2) Determine the termination condition and fitness function: The termination condition is that the current iteration number has reached the maximum iteration number; the fitness function is the total completion time function of the workpiece, and its calculation formula is: ;

[0057] (3) Initialize the population and evaluate its fitness;

[0058] (4) Perform roulette wheel selection, sequential crossover and reverse mutation operations on the parent chromosomes and calculate the fitness of the offspring chromosomes;

[0059] (5) Use the results of the genetic algorithm as the initial solution of the tabu search algorithm;

[0060] (6) Construct the tabu list, tabu length, and neighborhood structure for the tabu search algorithm;

[0061] (7) Perform tabu search operation and output the optimal solution.

[0062] Preferably, the neighborhood structure of the tabu search algorithm is as follows:

[0063] The purpose of neighborhood structure one is to reduce batch curing time, and the steps are as follows:

[0064] Step 1: Identify the workpieces within all batches, select the two batches with the longest curing times, and process them accordingly:

[0065] Step 2: Sort all workpieces in both batches in descending order of processing time and insert them at the beginning, then re-batch them.

[0066] Step 3: Calculate the fitness value and determine whether to accept the new solution.

[0067] The purpose of neighborhood structure 2 is to reduce preparation time, and the steps are as follows:

[0068] Step 1: Identify the workpieces and the space utilization rate (total workpiece area / total autoclave area * 100%) within all batches. If the number of batches exceeds 3, select the three batches with the lowest space utilization rate and operate on them:

[0069] Step 2: Sort all workpieces in both batches in descending order of area and insert them at the beginning, then re-batch them.

[0070] Step 3: Calculate the fitness value and determine whether to accept the new solution.

[0071] The present invention has the following advantages:

[0072] This invention addresses the current scheduling status of the hot pressing process for aerospace composite materials. It models the problem as a two-dimensional batch scheduling problem simultaneously considering resource constraints, dual preparation time, and the multi-layer loading characteristics of workpieces. A mixed-integer programming model is constructed with the objective function of minimizing the maximum completion time. To address the complexity of this model, an improved genetic-tabu hybrid optimization algorithm based on batch processing time is designed to solve it, effectively reducing the overall production time of the process and significantly improving production rate and resource utilization. The proposed method possesses good scalability and is applicable to composite material manufacturing processes with similar scheduling characteristics, providing effective theoretical support and implementation paths for scheduling optimization of related processes. Attached Figure Description

[0073] Figure 1 This is a schematic diagram of the method flow provided in an embodiment of the present invention;

[0074] Figure 2 Algorithm flowchart for a specific example provided in the embodiments of the present invention;

[0075] Figure 3 A flowchart of a neighborhood construction strategy provided in an embodiment of the present invention;

[0076] Figure 4 The flowchart for the second neighborhood construction strategy provided in the embodiment of the present invention is shown. Detailed Implementation

[0077] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] like Figure 1 As shown, this embodiment provides a batch scheduling method for hot pressing of aerospace composites that considers resource constraints and dual preparation time, including the following steps:

[0079] Step 1: Obtain relevant data for the hot pressing process, including workpiece data and autoclave data. Design a batch scheduling objective function for the aerospace composite hot pressing process with the goal of minimizing the maximum completion time.

[0080] Establish the objective function for hot pressing batch scheduling:

[0081] (1);

[0082] (2);

[0083] in, express The maximum completion time for a batch of workpieces. Batch set; Batch index; :batch Processing time, ; The minimum batch quantity is determined based on the workpiece area and resource quantity.

[0084] Formulas (1) and (2) define the objective function as minimizing the maximum completion time, and the maximum completion time is equal to the sum of the processing times of each batch.

[0085] Step 2: Establish a two-dimensional multi-layer loading hot pressing process batch scheduling model that considers resource constraints and dual preparation time, including: uniqueness constraints of workpiece processing, workpiece resource carrying capacity limits and dual preparation time, workpiece batch logic and process constraints, and spatial layout constraints.

[0086] The uniqueness constraint for workpiece machining ensures that each workpiece can be machined exactly once, as shown in the following formula:

[0087] (3);

[0088] Autoclave hierarchy; Autoclave Hierarchical Index; : , indicating workpiece Assigned to batch of Layers, otherwise 0, indicating a workpiece. Not assigned to batch of layer; ; : Collection of workpieces; : Workpiece index.

[0089] Workpiece resource constraints and dual preparation time include:

[0090] (1) Resource carrying capacity limit: The resource required for each workpiece shall not exceed the total resource capacity of the equipment layer in which it is located, in order to avoid equipment overload and ensure processing feasibility. The formula is as follows:

[0091] (4);

[0092] : workpiece Required resource quantity; : , indicating workpiece Assigned to batch of Layers, otherwise 0, indicating a workpiece. Not assigned to batch of layer; The first in the autoclave The number of resources contained in the layer;

[0093] (2) Dual preparation time: The dual preparation time for a batch is divided into tank entry / exit preparation time and technical preparation time. The tank entry / exit preparation time is related to the batch quantity, while the technical preparation time is linearly related to the total area of ​​all workpieces in the batch. The calculation logic for preparation time is clarified, and the formula is as follows:

[0094] (5);

[0095] (6);

[0096] :batch Preparation time for entering and exiting the tank. ; : Inlet and outlet time; : ,batch Processed, otherwise 0 indicates a batch. Not processed ; :batch Technical preparation time, ; The degree of influence of workpiece area on technical preparation time; : workpiece Length and width, ; : , indicating workpiece Assigned to batch of Layers, otherwise 0, indicating a workpiece. Not assigned to batch of layer, , ;

[0097] (3) Curing time: The batch curing time is determined by the maximum processing time of a single workpiece within the batch, as shown below:

[0098] (7);

[0099] ;batch The curing time, ; : workpiece Processing time, ;

[0100] (4) Processing time: Total batch processing time = Curing time + Technical preparation time. The composition of the processing cycle for a single batch is defined by the following formula:

[0101] (8);

[0102] :batch Technical preparation time, ; :batch Preparation time for entering and exiting the tank. .

[0103] Workpiece batch logic and process constraints, including:

[0104] (1) Batch processing sequence: Batch processing is carried out in sequence. The next batch can only start after the previous batch is completed, to ensure the continuity of the production process and avoid parallel conflicts. The formula is as follows:

[0105] (9);

[0106] (2) Batch activation conditions: A batch will only be activated when a workpiece is assigned to it, thus preventing empty batches and optimizing resource and time costs. The formula is as follows:

[0107] (10);

[0108] (3) Relaxation solution enhances constraints. The quality of the relaxation solution of the model is enhanced by effective inequalities. Formulas (9)-(10) provide high-quality lower bounds to help improve the solution efficiency and optimization accuracy (mostly used in algorithm optimization in mathematical modeling). The workpieces need to be arranged in descending order of processing time. The formula is as follows:

[0109] (11);

[0110] (12);

[0111] (13);

[0112] The first in the autoclave The width of the layer; The first in the autoclave The length of the layer; The first in the autoclave The number of resources in the layer; : workpiece Length and width, ; : workpiece Arranged to batch of Processing is performed on a single layer; otherwise, it is 0, indicating the workpiece. Not assigned to batch of The layers are processed;

[0113] Spatial layout constraints include load-bearing capacity constraints, non-overlapping constraints, and relative position constraints.

[0114] Due to load-bearing capacity constraints, the workpiece must not be placed beyond the physical boundaries of the equipment's support platform to ensure spatial adaptability. The formula is as follows:

[0115] (14);

[0116] (15);

[0117] : workpiece In the bottom left corner of the batch coordinate, ; : workpiece In the bottom left corner of the batch coordinate, ; : , indicating workpiece The length of the workpiece is parallel to the length of the machine; otherwise, it is 0, indicating that the workpiece is parallel to the length of the machine. The length is not parallel to the length of the machine.

[0118] Without overlap constraints, no two workpieces may overlap in the horizontal or vertical direction, avoiding spatial conflicts and ensuring the independence of workpieces during processing. The formula is as follows:

[0119] (16);

[0120] (17);

[0121] : workpiece In the bottom left corner of the batch coordinate, ; : workpiece In the bottom left corner of the batch coordinate, ; , indicating workpiece Workpieces in the same batch The left side is 0; otherwise, it is 0, indicating the workpiece. Workpieces not in the same batch To the left, ; : , indicating workpiece Workpieces in the same batch Below; otherwise, 0 indicates the workpiece. Workpieces not in the same batch Below, .

[0122] Relative position constraints define the relative positional relationship between any two workpieces within the same batch and layer, providing specific coordinates or orientation rules (such as left-right or front-back order) for spatial layout. The formula is as follows:

[0123] (18);

[0124] , indicating workpiece Assigned to batch of Layer, otherwise 0, indicating a workpiece. Not assigned to batch of layer.

[0125] Step 3: Using the objective function as the optimization objective and the two-dimensional multi-layer loading hot pressing process batch scheduling model as the optimization model, the improved genetic tabu algorithm based on batch processing time is used to solve the problem and obtain the hot pressing process batch scheduling scheme.

[0126] like Figure 2 As shown, the solution process of the improved genetic tabu algorithm based on batch processing time is as follows:

[0127] (1) Determine the encoding and decoding strategy: Encode based on the workpiece sequence. The length of each chromosome is the workpiece sequence. Each gene position on the chromosome represents a workpiece. Different workpiece arrangements represent different scheduling schemes. Decode using a greedy strategy to form an initial solution.

[0128] (2) Determine the termination condition and fitness function: The termination condition is that the current iteration number has reached the maximum iteration number; the fitness function is the total completion time function of the workpiece, and its calculation formula is: ;

[0129] (3) Initialize the population and evaluate its fitness;

[0130] (4) Perform roulette wheel selection, sequential crossover and reverse mutation operations on the parent chromosomes and calculate the fitness of the offspring chromosomes;

[0131] (5) Use the results of the genetic algorithm as the initial solution of the tabu search algorithm;

[0132] (6) Construct the tabu list, tabu length, and neighborhood structure for the tabu search algorithm;

[0133] (7) Perform tabu search operation and output the optimal solution.

[0134] Preferably, the neighborhood structure of the tabu search algorithm is as follows:

[0135] The purpose of neighborhood structure one is to reduce batch curing time, such as Figure 3 As shown, the steps are as follows:

[0136] Step 1: Identify the workpieces within all batches, select the two batches with the longest curing times, and process them accordingly:

[0137] Step 2: Sort all workpieces in both batches in descending order of processing time and insert them at the beginning, then re-batch them.

[0138] Step 3: Calculate the fitness value and determine whether to accept the new solution.

[0139] The purpose of neighborhood structure 2 is to reduce preparation time, such as Figure 4 As shown, the steps are as follows:

[0140] Step 1: Identify the workpieces and the space utilization rate (total workpiece area / total autoclave area * 100%) within all batches. If the number of batches exceeds 3, select the three batches with the lowest space utilization rate and operate on them:

[0141] Step 2: Sort all workpieces in both batches in descending order of area and insert them at the beginning, then re-batch them.

[0142] Step 3: Calculate the fitness value and determine whether to accept the new solution.

[0143] This embodiment will construct an example of hot pressing process scheduling, and solve it using the improved genetic tabu algorithm based on batch processing time of this invention. The naming rule for this scheduling case is M[number of workpieces][workpiece type], where workpieces are divided into two categories, and the required resource quantities are between [1,15] and [1,20], respectively. The dimensions of each layer of the autoclave are shown in Table 1.

[0144] Table 1 Dimensions of each layer of the autoclave

[0145]

[0146] The above processing information is input into the algorithm of this invention, and the crossover probability is set. =0.9 and mutation probability =0.1, taboo length =10, population size =100, maximum number of iterations =100. To prove the effectiveness of the proposed algorithm, it is compared with the traditional genetic algorithm ( ) and tabu search algorithm ( Compare the parameters as above.

[0147] First, a small-scale example of the proposed model is solved using gurobi, and the results are as follows: As shown. Secondly, the above three algorithms are used to solve the problem 10 times, and the average value is taken. The algorithm results are compared to... As shown. The formula for calculating the reduction ratio is: As can be seen, except for the scale of 10, Comparison and The solution quality has been improved, and the experimental results prove the effectiveness and practicality of the proposed algorithm. It can effectively reduce the processing time of the hot pressing molding process of aerospace composites and improve its production efficiency.

[0148] Table 2 shows the results of the gurobi solution.

[0149]

[0150] Table 3 Comparison of Algorithm Solution Results

[0151]

[0152] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time, characterized in that: Includes the following steps: Step 1: Obtain relevant data for the hot pressing process, including workpiece data and autoclave data. Design a batch scheduling objective function for the aerospace composite hot pressing process with the goal of minimizing the maximum completion time. Among them, the objective function for hot pressing batch scheduling is established as follows: C' = minC (1); Where C represents the maximum completion time of k batches of workpieces, B = {1,…,r}: batch set; k∈B: batch index; p k Processing time for batch k K: Minimum batch quantity obtained based on workpiece area and resource quantity; Formulas (1) and (2) define the objective function as minimizing the maximum completion time, and the maximum completion time is equal to the sum of the processing times of each batch; Step 2: Considering resource constraints and dual preparation time, establish a two-dimensional multi-layer loading hot pressing forming process batch scheduling model, including: uniqueness constraints of workpiece processing, workpiece resource carrying capacity limit and dual preparation time, workpiece batch logic and process constraints, and spatial layout constraints. Among these, the limitations on workpiece resource capacity and the dual preparation time include: (1) Resource carrying capacity limit: The resource required for each workpiece shall not exceed the total resource capacity of the equipment layer in which it is located, in order to avoid equipment overload and ensure processing feasibility. The formula is as follows: tc j : The number of resources required for workpiece j z jkr :z jkr =1 indicates that workpiece j is assigned to layer v of batch k, and 0 indicates that workpiece j is not assigned to layer v of batch k; TC v : The quantity of resources contained in the vth layer of the autoclave; K: The minimum batch quantity obtained based on the workpiece area and the quantity of resources; (2) Dual preparation time: The dual preparation time for a batch is divided into tank entry / exit preparation time and technical preparation time. The tank entry / exit preparation time is related to the batch quantity, while the technical preparation time is linearly related to the total area of ​​all workpieces in the batch. The calculation logic for preparation time is clarified, and the formula is as follows: s k Preparation time for batch k to enter or leave the tank. d: Tank entry / exit time; u k :u k =1, batch k is processed; otherwise, 0 indicates that batch k is not processed. s' k Technical preparation time for batch k a: The degree of influence of workpiece area on technical preparation time; l j ,w j : The length and width of workpiece j z jkv :z jkv =1 indicates that workpiece j is assigned to layer m of batch k, and 0 indicates that workpiece j is not assigned to layer m of batch k. (3) Curing time: The batch curing time is determined by the maximum processing time of a single workpiece within that batch, as shown below: p' k The curing time of batch k p j The processing time of workpiece j. (4) Processing time: Total batch processing time = curing time + technical preparation time. The composition of a single batch processing cycle is defined by the following formula: s' k Technical preparation time for batch k s k Preparation time for batch k to enter or leave the tank. Step 3: Using the objective function as the optimization objective and the two-dimensional multi-layer loading hot pressing process batch scheduling model as the optimization model, the improved genetic tabu algorithm based on batch processing time is used to solve the problem and obtain the hot pressing process batch scheduling scheme.

2. The batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time as described in claim 1, characterized in that: The uniqueness constraint for workpiece machining ensures that each workpiece can be machined exactly once, as shown in the following formula: V = {1, ..., g}: Autoclave hierarchy set; v ∈ V: Autoclave hierarchy index; z jkv :z jkv =1 indicates that workpiece j is assigned to layer v of batch k, and 0 indicates that workpiece j is not assigned to layer v of batch k. B = {1,…,r}: Batch set; k∈B: Batch index; J = {1,…,n}: Workpiece set; i,j∈J: Workpiece index.

3. The batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time as described in claim 1, characterized in that: Workpiece batch logic and process constraints, including: (1) Batch processing sequence: Batches are processed in chronological order. The next batch can only begin after the previous batch is completed, ensuring the continuity of the production process and avoiding parallel conflicts. The formula is as follows: (2) Batch activation conditions: A batch will only be activated when a workpiece is assigned to it, thus preventing empty batches and optimizing resource and time costs. The formula is as follows: (3) Enhance constraints by relaxing solutions. The quality of the model's relaxed solutions is enhanced through effective inequalities. Formulas (9)-(10) provide high-quality lower bounds to help improve solution efficiency and optimization accuracy. Workpieces need to be arranged in descending order of processing time. The formulas are as follows: C≥K·s k +a∑ j∈J w j l j +max{p j }+(K-1)·min{p j } (12); C: Maximum completion time; W v : Width of the vth layer in the autoclave; L v : Length of the vth layer in the autoclave; TC v : The quantity of resources in the vth layer of the autoclave; l j ,w j : The length and width of workpiece j z kkv :z kkv =1, workpiece k is assigned to layer v of batch k for processing; otherwise, 0 indicates that workpiece k is not assigned to layer v of batch k for processing.

4. The batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time as described in claim 1, characterized in that: Spatial layout constraints include load-bearing capacity constraints, non-overlapping constraints, and relative position constraints.

5. The batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time as described in claim 4, characterized in that: Due to load-bearing capacity constraints, the workpiece must not be placed beyond the physical boundaries of the equipment's support platform to ensure spatial adaptability. The formula is as follows: x j The x-coordinate of the bottom left corner of workpiece j in the batch. y j : The y-coordinate of the bottom left corner of workpiece j in the batch. θ j θ j =1 indicates that the length of workpiece j is parallel to the length of the machine; otherwise, 0 indicates that the length of workpiece j is not parallel to the length of the machine.

6. The batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time as described in claim 4, characterized in that: Without overlap constraints, no two workpieces may overlap in the horizontal or vertical direction, avoiding spatial conflicts and ensuring the independence of workpieces during processing. The formula is as follows: x i The x-coordinate of the bottom left corner of workpiece i in the batch. y i : The y-coordinate of the bottom left corner of workpiece i in the batch. lf ij :lf ij =1 indicates that workpiece i is to the left of workpiece j in the same batch; conversely, 0 indicates that workpiece i is not to the left of workpiece j in the same batch. b ij :b ij =1 indicates that workpiece i is below workpiece j in the same batch; conversely, 0 indicates that workpiece i is not below workpiece j in the same batch.

7. The batch scheduling method for hot pressing of aerospace composites considering resource constraints and dual preparation time as described in claim 4, characterized in that: Relative position constraints define the relative positional relationship between any two workpieces within the same batch and layer, providing specific coordinate or orientation rules for spatial layout. The formula is as follows: z ikv :z ikv =1 indicates that workpiece i is assigned to layer v of batch k, and 0 indicates that workpiece i is not assigned to layer v of batch k.

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