Scheduling optimization method for aviation composite material manufacturing hot press molding process

Abstract

The invention discloses a scheduling optimization method for an aviation composite material manufacturing hot-press forming process, which comprises the following steps of: 1, establishing a hot-press forming production plan scheduling objective function by taking the minimum total delay time of a workpiece as an objective according to an aviation composite material manufacturing hot-press forming production plan; 2, establishing a two-dimensional hot press molding process batch scheduling model considering a workpiece release time window and machine applicability; and step 3, taking the objective function as an optimization objective, taking the two-dimensional hot-press forming process batch scheduling model as an optimization model, and utilizing a genetic algorithm embedded with a self-adaptive domain search strategy to solve and obtain a hot-press forming production plan scheduling scheme. According to the method, the total delay time of workpieces can be remarkably shortened while the machining batch of the workpieces is minimized, the occurrence of prepreg deterioration conditions is reduced, and conditions are created for improving the hot press molding production efficiency and promoting digital and intelligent transformation and upgrading of composite material manufacturing enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent manufacturing of aviation composites, and in particular relates to a scheduling optimization method for a hot pressing forming process in manufacturing aviation composites. Background Art

[0002] As an important part of the national economy, the development and progress of the aviation equipment manufacturing industry have played an irreplaceable role in national security, economic structure optimization, and the improvement of international competitiveness. Aviation composites, or aviation composite materials, have gradually replaced metal alloys as the main raw materials for aviation equipment manufacturing with their ideal physical and chemical properties. The wide application of composite materials in aviation equipment manufacturing has led to a surge in demand. The production of aviation composite materials has the characteristics of multiple varieties, small batches, and re-entry manufacturing, which leads to a long production cycle and complex manufacturing processes, resulting in a prominent contradiction between supply and demand. The traditional composite material production management model cannot meet the needs of current aircraft production, so it puts forward higher requirements for the production planning and scheduling of composite material production systems. As an intermediate process in composite manufacturing, the hot pressing process has the longest processing time in the composite manufacturing process and has become a bottleneck process in composite manufacturing. The traditional process scheduling scheme is prone to insufficient equipment utilization and frequent delays. Therefore, establishing an effective scheduling model and algorithm to optimize the hot pressing production planning and scheduling scheme is crucial to promoting the transformation and upgrading of aviation composite manufacturing to digitalization and intelligence.

[0003] At present, there are many studies on the production planning and scheduling of aviation composite manufacturing. Most of the studies on the scheduling problem of hot press forming are one-dimensional batch scheduling studies that consider the differences in workpiece size, workpiece family and workpiece release time, with the goal of minimizing the total completion time. However, existing studies often ignore the limitations of the external time of prepreg and machine applicability. The external time of prepreg requires that the workpiece must be processed in the tank before the preform deteriorates and expires, which will result in differences in the release time window of the workpiece. Machine applicability means that not all workpieces used can be processed in any type of autoclave, and different workpieces can only be processed on machines that meet their heat treatment parameters and size requirements. Existing technologies such as patent CN118396272A-a scheduling optimization method for hot press forming processes of aviation composites based on genetic algorithms is only applicable to production and manufacturing scenarios with high requirements for curing consistency or relatively single composite standard parts products. In such production scenarios, specific workpiece curing groups can be clearly divided according to the heat treatment parameters of the workpieces. One type of autoclave is only responsible for processing one type of workpiece curing group, and the workpieces processed by different autoclaves are independent of each other. The use of a single curing group allocation scheme can ensure the product quality and production efficiency of composite standard parts, and simplify production management and operation. From the division criteria of the batch scheduling problem, it mainly solves the single-machine two-dimensional batch scheduling, that is, a batch of workpieces are processed by a batch processing machine. How to formulate a production plan, this problem belongs to the single-machine scheduling problem. And the idea of ​​algorithm improvement is to use the encoding method and repair operator designed based on the maximum time compatible workpiece set to improve the population quality, modify the crossover operator and mutation operator of the classical genetic algorithm to improve the convergence speed of the algorithm and reduce the solution time. Summary of the invention

[0004] In view of the deficiencies of the above-mentioned prior art, the present invention provides a scheduling optimization method for the hot pressing forming process in aviation composite manufacturing, which realizes the optimization of the scheduling scheme for the collaborative processing of multiple non-equivalent batch processing machines in the hot pressing forming curing process, improves the production efficiency of the hot pressing curing process, and solves the problems of frequent delays and high workpiece scrap rate in the hot pressing forming process in the prior art.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0006] A scheduling optimization method for a hot pressing forming process in aviation composite manufacturing, comprising:

[0007] Step 1: According to the hot press forming production plan of aviation composite manufacturing, the hot press forming production plan scheduling objective function is established with the goal of minimizing the total delay time of the workpiece;

[0008] Step 2: Establish a two-dimensional hot press forming process batch scheduling model considering the workpiece release time window and machine applicability;

[0009] Step 3: Taking the objective function as the optimization target and the two-dimensional hot press forming process batch scheduling model as the optimization model, a genetic algorithm embedded with an adaptive domain search strategy is used to solve and obtain a hot press forming production plan scheduling solution.

[0010] To optimize the above technical solutions, the specific measures taken also include:

[0011] The above step 1 establishes the following hot pressing production scheduling objective function:

[0012] minT(1)

[0013] Where T represents the delay time of all workpieces.

[0014] The two-dimensional hot pressing process batch scheduling model established in the above step 2 includes formula (2) to formula (27):

[0015] Formula (2) and formula (3) ensure that each workpiece can be allocated only once in the production plan;

[0016] Formula (4) and formula (5) ensure that the release time windows of any two workpieces assigned to a batch of processing are compatible;

[0017] Formula (6) ensures that any workpiece can only be processed by one batch in its applicable machine set;

[0018] Formula (7) ensures that the workpiece will not be assigned to a machine outside its applicable machine set for processing;

[0019] Formula (8) ensures that the workpieces assigned to the same batch must be processed on the same machine;

[0020] Formulas (9) and (10) ensure that any workpiece distributed in a batch of workpieces cannot exceed the edge of the operating platform inside the autoclave;

[0021] Formula (11) and formula (12) ensure that any two workpieces assigned to a batch cannot overlap each other;

[0022] Formula (13) is used to ensure the relative positions of any two workpieces assigned to the same batch;

[0023] Through formula (14) and formula (15), it is determined that a batch processing machine can only process one batch of workpieces at a time, and the order of the batches is sorted;

[0024] By using formula (16), the delay time of each workpiece is determined;

[0025] The total delay time of the allocation scheme is determined by formula (17), where T represents the delay time of all workpieces; the value range of each variable is determined by formula (18)-formula (27), where M is a positive number;

[0026]

[0027]

[0028] The meanings of the variables and parameters in the above model are as follows:

[0029] Collections and Indexes:

[0030] N = {1, 2, ..., n}: represents the set of all workpieces, n is the number of workpieces;

[0031] M = {1, 2, ..., m}: indicates the machine type, and m indicates the number of machine types;

[0032] B = {1, 2, ..., b}: represents the set of all batches, b is the number of batches;

[0033] i,i′,i″: workpiece index, i,i′,i″∈N;

[0034] j: machine index, j∈M;

[0035] parameter:

[0036] w i ,h i : The width and height of the workpiece,

[0037] p i : Processing time of workpiece,

[0038] d i : The expected delivery time of the workpiece,

[0039] est i ,lst i : represent the earliest processing time and the latest processing time of the workpiece, i∈N;

[0040] Decision variables:

[0041] u i′i : 0-1 variable, 1 if workpiece i and workpiece i' are assigned to the same batch, otherwise 0,

[0042] e ii′ : 0-1 variable, if both workpieces i and i′ are critical workpieces and i is processed before i′, then it is 1, otherwise it is 0;

[0043] o i : 0-1 variable, 1 if the height of the i-th workpiece is parallel to the width of the machine (the workpiece is rotated 90°), otherwise 0;

[0044] l i,m : 0-1 variable, if the i-th workpiece is processed on the m-th machine, it is 1, otherwise it is 0;

[0045] a ii′ : 0-1 variable, 1 if the i-th workpiece is to the left of the i′th workpiece, otherwise 0;

[0046] b ii′ : 0-1 variable, 1 if the i-th workpiece is at the bottom of the i′th workpiece, otherwise 0;

[0047] x i : integer variable, the horizontal coordinate of the lower left corner of workpiece i;

[0048] y i : integer variable, the ordinate of the lower left corner of workpiece i;

[0049] c i : integer variable, actual completion time of job i;

[0050] t i : Integer variable, delay time of job i.

[0051] The solution process of the above genetic algorithm embedded with adaptive domain search strategy is as follows:

[0052] (1) Determine the optimization objective and optimization model: take the objective function established in step 1 as the optimization objective, and take the model established in step 2 as the optimization model;

[0053] (2) Determine the encoding strategy: Use the integer encoding method. After decoding a chromosome, a solution to the problem is obtained. The number of genes on a chromosome is determined by the number of workpieces in the problem. The gene numbers on the chromosome represent different workpiece numbers. The order of the workpiece numbers on the chromosome determines the priority of the workpieces when allocating batches.

[0054] (3) Determine the decoding strategy: Use the earliest completed machine first heuristic decoding strategy EMM to decode the chromosome and obtain the corresponding job scheduling plan;

[0055] (4) Determine the termination condition and fitness function: The termination condition is to determine whether the current number of iterations I is greater than the maximum number of iterations Gen; the fitness function is the total completion time function of the workpiece, and its calculation formula is: f = ∑ i∈N t i , where t iis an integer variable, the delay time of job i;

[0056] (5) Initializing the population and evaluating the fitness: generating the initial chromosome coding gene sequence based on the maximum time-compatible artifact set segmentation;

[0057] (6) Perform selection, crossover, and mutation operations to calculate the fitness of the new individual;

[0058] (7) Perform adaptive neighborhood search operations to improve population quality.

[0059] The above EMM process is as follows:

[0060] Select a machine to initialize a batch. The machine selection follows the rule that the batch machine with the smallest completion time is given priority. If the total completion time of each machine is the same, the batch machine with the largest size is given priority. Add workpieces to the batch in coding order until no new workpieces can be added to the batch. Close the current batch, reselect the machine to initialize a new batch, and repeat the operation until all workpieces are assigned to the batch.

[0061] The crossover strategy adopted in the above crossover operation is: given a crossover probability p c , for a pair of parent chromosomes, randomly generate a probability p, if p≤p c , then select several genes at random positions in the parent chromosome 1, the positions of the genes may not be continuous, and keep the order of the selected genes in chromosome 1, recorded as gene fragment A; keep the order of all genes in chromosome 2 except gene fragment A, replace the vacant positions in chromosome 2 according to the gene order in gene fragment A, and generate daughter chromosome 1; select the genes at the same position in chromosome 2 and record them as gene fragment B, and repeat the same operation with chromosome 1 as the base to generate daughter chromosome 2;

[0062] The mutation strategy adopted by the mutation operation is: given a mutation probability p m , for each individual in the current population, randomly generate a probability p, if p≤p m , then perform the mutation operation, the specific operation is: randomly select a time-compatible artifact set K s , K s The order of all genes in the chromosome is reversed, and then inserted into the original gene positions in the chromosome in order to generate daughter chromosomes.

[0063] The above adaptive neighborhood search strategy is as follows:

[0064] During the algorithm iteration process, when the crossover and mutation operators of the nth iteration are executed, the roulette wheel is initialized or generated according to the scores of the neighborhood search operators of the previous generation. The specific operation is: the selection probability p of the search operator in each field of the current population is generated according to the following formula (ns,n) , using roulette wheel to select a neighborhood search strategy for each individual in the population, where the neighborhood search operator selects the probability p (ns,n) The calculation formula is:

[0065]

[0066] Where w (ns,n-1) represents the cumulative score weight of the neighborhood search operator ns in the n-1th generation, and the score weight calculation formula of each field search operator ns in the nth generation is w (ns,n) for:

[0067]

[0068] Among them, r is a parameter, which indicates the influence of the probability of the previous generation of neighborhood search operators being selected on the next generation; search_time ns Indicates the number of times the neighborhood search operator ns has searched for neighborhood solutions during the neighborhood search process of the nth generation population; score ns It represents the final score of each neighborhood search operator after the neighborhood search for the nth generation population; the scoring rule is as follows: each individual in the current population is regarded as the current solution. If the neighborhood solution obtained after the neighborhood search is better than the current solution, the corresponding neighborhood search operator score is increased by σ1; if the neighborhood solution is better than the global optimal solution, the corresponding neighborhood search operator score is additionally increased by σ2; if the quality of the neighborhood solution is worse than the current solution, the neighborhood search operator score remains unchanged, and σ1 is less than σ2.

[0069] The above-mentioned adaptive neighborhood search is provided with an adjustment operator MTJ for the maximum delayed workpiece, which is used to insert the workpiece with the largest total delay time into the batch of the same batch processor with better adaptability. The specific process is as follows:

[0070] Record the workpiece i with the longest delay in the entire allocation plan; filter all batches corresponding to all workpieces in the maximum time compatible workpiece set to which workpiece i belongs and filter out all batches B that meet the machine compatibility of workpiece i i ; Construct a score function f to evaluate the adaptability of workpieces and batches b ; Generate a roulette wheel based on the scores of each batch to evaluate the adaptability of the workpiece and the batch, select a batch according to probability and insert the workpiece into the batch, and adjust the encoding of the chromosome individual; the score function f for evaluating the adaptability of the workpiece and the batch b The calculation formula is as follows:

[0071]

[0072] in, Indicates the earliest start time of processing of batch b est b and the earliest start processing time est of workpiece i i The difference, The larger it is, the closer the release time of job i is to the earliest processing start time of batch b; Indicates the latest start processing time lst of batch b b and the latest processing start time lst of workpiece i i The difference, The larger it is, the closer the external time of workpiece i is to the latest processing start time of batch b; is a piecewise function, when p b -p i ≥0, that is, the batch processing time is not less than the workpiece processing time, When p b -p i <0, that is, when the processing time of the workpiece exceeds the processing time of the batch, It is used to evaluate the impact of workpiece i on the processing time of batch b before and after insertion. , it means that after inserting workpiece i into batch b, the processing time of the entire batch will not increase. , it means that after workpiece i is added to batch b, the number of processed workpieces in the batch increases to the processing time of the workpiece. At this time, the greater the difference between the processing time of the workpiece and the processing time of the batch, The smaller it is; α represents the influence of the compatibility between the release time window of the workpiece and the time window for starting processing of the batch on the batch adaptability, β represents the influence of the workpiece processing time on the batch adaptability, α+β=1.

[0073] The above adaptive neighborhood search is equipped with an adjustment operator MTB for the maximum delayed batch on the same batch processing machine, which is used to adjust the batch with the longest total delay time in the scheme with other batches in the same batch processing machine. The specific process is as follows:

[0074] Select the batch b1 with the longest total delay time, record the batch processing machine that processes batch b1 as M; select the batches in batch processing machine M that are less than the start processing time of batch b1 to form a set B, and randomly select a batch b2 from the batch set B; take out all the workpieces in batches b1 and b2 to form a workpiece set J, and then select the workpieces according to the workpiece w. i ×h i / d iThe purpose is to prioritize the processing of workpieces with earlier expected delivery time and larger size; replace the gene fragments corresponding to workpiece set J in the chromosome according to the reordered workpiece sequence, where w i ,h i is the width and height of the workpiece; d i The expected delivery time of the workpiece.

[0075] The above-mentioned adaptive neighborhood search is equipped with an adjustment operator MTM for batches between different batch processing machines, which is used to adjust the batch with the largest total delay time on a random batch processing machine in the scheme with the batch on a batch processing machine with better adaptability. The specific process is as follows:

[0076] Randomly select a batch machine M1 and record the batch b1 with the largest total delay time on batch machine M1; select the batch machine M2 that is most suitable for adjusting batch b1 according to the applicable machine set of all workpieces in batch b1; select all batches in M2 with a completion time less than batch b1 and randomly select a batch b2 as the adjusted batch; merge the workpieces in batches b1 and batch b2, delete the workpieces that cannot be applied to batch machines M1 and M2 at the same time, form a workpiece set J, and sort all the workpieces in set J according to w i ×h i / d i The values ​​of are sorted in descending order to ensure that the workpieces with more urgent delivery tasks and higher space utilization efficiency of the batch processor are processed first; the gene fragments corresponding to set J in the chromosome are replaced according to the order of the workpieces in set J, where w i ,h i is the width and height of the workpiece; d i The expected delivery time of the workpiece.

[0077] The present invention has the following beneficial effects:

[0078] Based on the existing research on the scheduling problem of hot press molding of aviation composites, the present invention studies the two-dimensional batch scheduling problem of prepreg external time and machine applicability constraints, and establishes a hot press molding process batch scheduling model with mixed integer programming that takes minimizing the total delay time of workpieces as the objective function. In addition, a genetic algorithm is called for the research problem. According to the characteristics of prepreg external time and based on the concept of maximum time compatible workpiece set, a genetic algorithm with adaptive neighborhood search strategy is designed. sets, ANSGA-MTC) is used to solve the model. By applying decoding, crossover and mutation strategies to this problem, the solution accuracy is improved, and the workpiece batching and batch scheduling can be automatically realized to minimize the total delay time of the workpiece. The total delay time of the workpiece can be significantly reduced while minimizing the workpiece processing batches, and the occurrence of prepreg deterioration can be reduced. This creates conditions for improving the production efficiency of hot pressing molding and promoting the digital and intelligent transformation and upgrading of composite manufacturing enterprises, thereby solving the problem of frequent delays or waste of workpieces due to the constraint of prepreg external time in hot pressing molding manufacturing, optimizing the scheduling plan of hot pressing molding manufacturing, and improving the comprehensive production efficiency of composite manufacturing.

[0079] The present invention is applicable to various production and manufacturing scenarios of composite standard parts products. In such production scenarios, order requirements are variable and workpieces are diverse, so there are many workpiece curing groups divided and the distinction between them is not obvious. In order to make full use of equipment resources, enterprises will use workpiece collaborative processing allocation solutions. The workpieces processed by different autoclaves are neither independent nor completely the same, and there are limitations on the applicability of the machines.

[0080] The production and processing of multiple batch processing machines solved by the present invention belongs to the parallel machine scheduling problem, and different batch processing machines have different sizes, models and production capacities. The single machine batch scheduling problem solved by the prior art is not equivalent to the two-dimensional batch scheduling problem of parallel machines. Single machine batch scheduling and parallel machine batch scheduling belong to different branches in the field of machine scheduling, and non-equivalent parallel machines are a more complicated situation in the parallel machine scheduling problem.

[0081] In addition to using the same encoding method to improve population quality, the present invention does not change the crossover mutation method of the classical genetic algorithm, but designs three types of neighborhood search actions and an adaptive neighborhood search mechanism based on the concept of the maximum time-compatible artifact set to accelerate the convergence of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0083] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention;

[0084] Figure 2 An algorithm flow chart of a specific example provided for an embodiment of the present invention;

[0085] Figure 3 Flow chart of NSGA-MTC chromosome decoding (EMM algorithm) provided by an embodiment of the present invention;

[0086] Figure 4 A chromosome encoding flow chart based on the maximum time compatible set design provided by an embodiment of the present invention;

[0087] Figure 5 NSGA-MTC chromosome crossover (OBX algorithm) flow chart provided for an embodiment of the present invention;

[0088] Figure 6 Flow chart of NSGA-MTC chromosome mutation (MTCIM algorithm) provided by an embodiment of the present invention;

[0089] Figure 7 The MTJ neighborhood search operator provided by the embodiment of the present invention;

[0090] Figure 8 The MTB neighborhood search operator provided by the embodiment of the present invention;

[0091] Fig. 9 The MTM neighborhood search operator provided by the embodiment of the present invention;

[0092] Fig.10 Genetic algorithm iteration diagram for a specific example provided for implementation of the present invention;

[0093] Fig.11 A schematic diagram of the structure of a device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0095] Although the steps in the present invention are arranged with numbers, they are not used to limit the order of the steps. Unless the order of the steps is clearly stated or the execution of a certain step requires other steps as a basis, the relative order of the steps can be adjusted. It is understood that the term "and / or" used in this article involves and covers any and all possible combinations of one or more of the associated listed items.

[0096] An embodiment of the present invention provides a scheduling optimization method for hot press forming processes in aviation composite manufacturing. An optimization model is established for the scheduling problem of hot press forming processes of aviation composites. A mixed integer programming model is established to minimize the total delay time of workpieces. The effectiveness of the model is verified using the gurobi solver. However, as the scale of the problem increases, the solution efficiency of the gurobi solver obviously cannot meet the requirements of actual production. Therefore, the present invention designs a genetic algorithm to solve the problem according to the actual problem, so as to achieve the overall optimization of large-scale workpiece batch scheduling solutions and solve the two-dimensional parallel batch scheduling problem of hot press forming of aviation composites. Figure 1 As shown, specifically including:

[0097] Step 1: According to the hot press forming production plan of aviation composite manufacturing, the hot press forming production plan scheduling objective function is established with the goal of minimizing the total delay time of the workpiece;

[0098] Step 2: Establish a two-dimensional hot press forming process batch scheduling model considering the workpiece release time window and machine applicability;

[0099] Step 3: Taking the objective function as the optimization target and the two-dimensional hot press forming process batch scheduling model as the optimization model, a genetic algorithm embedded with an adaptive domain search strategy is used to solve and obtain a hot press forming production plan scheduling solution.

[0100] In the embodiment, step 1 obtains workpiece processing information and autoclave model information of the enterprise's periodic plan order, and determines the objective function of the hot pressing molding manufacturing scheduling task according to the production plan information and plan requirements, including:

[0101] Collect the order information during the scheduling period of the hot pressing process of the composite manufacturing enterprise within a production cycle (month / week / day) and the model size and processing parameters of the hot pressing tank in the hot pressing process workshop, and determine the objective function of the hot pressing workpiece scheduling based on the enterprise's needs:

[0102] minT(1)

[0103] Where T represents the delay time of all workpieces.

[0104] The auxiliary material manufacturing production information at least includes: the size information (length and width) of the workpiece, the heat treatment processing time required for the workpiece, the expected delivery time of the order to which the workpiece belongs, the set of applicable autoclave models for the workpiece, the workpiece release time window determined by the prepreg, and the size information (length and width) of the internal operating platform of different models of autoclaves, etc.

[0105] Step 2: Establishing a two-dimensional batch scheduling model corresponding to the objective function and taking into account the workpiece release time window and machine applicability, including:

[0106] Based on the objective function, a batch scheduling model for hot pressing forming process considering the release time window of workpieces is established. The constraints of the model include: all workpieces must be scheduled for processing; the autoclave can only process one batch of workpieces at a time; workpieces belonging to the same processing batch must meet time and space constraints; workpieces can only be processed by suitable machines.

[0107] In step 2, the two-dimensional batch scheduling model considering the workpiece release time window and machine applicability requires

[0108] All workpieces can only be assigned to one batch for processing.

[0109] Constraints are imposed on the workpieces assigned to the same batch, including: the workpieces in each batch cannot be placed overlappingly; they cannot exceed the edge of the operating platform inside the autoclave; and the intersection of the release time windows of each batch of workpieces cannot be an empty set.

[0110] Each workpiece can only be processed by a machine that has the same set of machine suitability.

[0111] In step 2, the two-dimensional batch scheduling model considering the job release time window and machine applicability includes:

[0112] Formula (2) to Formula (16):

[0113] Formula (2) and formula (3) ensure that each workpiece can be allocated only once in the production plan;

[0114] Formula (4) and formula (5) ensure that the release time windows of any two workpieces assigned to a batch of processing are compatible;

[0115] Formula (6) ensures that any workpiece can only be processed by one batch in its applicable machine set;

[0116] Formula (7) ensures that the workpiece will not be assigned to a machine outside its applicable machine set for processing;

[0117] Formula (8) ensures that the workpieces assigned to the same batch must be processed on the same machine;

[0118] Formulas (9) and (10) ensure that any workpiece distributed in a batch of workpieces cannot exceed the edge of the operating platform inside the autoclave;

[0119] Formula (11) and formula (12) ensure that any two workpieces assigned to a batch cannot overlap each other;

[0120] Formula (13) is used to ensure the relative positions of any two workpieces assigned to the same batch;

[0121] Through formula (14) and formula (15), it is determined that a batch processing machine can only process one batch of workpieces at a time, and the order of the batches is sorted;

[0122] By using formula (16), the delay time of each workpiece is determined;

[0123] The total delay time of the allocation scheme is determined by formula (17).

[0124] The value range of each variable is determined by formula (18)-formula (27), where M is a sufficiently large positive number.

[0125]

[0126] The meanings of the variables and parameters in the model are as follows:

[0127] (1) Collections and Indexes

[0128] N = {1, 2, ..., n}: represents the set of all workpieces, and n is the number of workpieces.

[0129] M = {1, 2, ..., m}: indicates the machine type, and m indicates the number of machine types.

[0130] B={1,2,…,b}: represents the set of all batches, b is the number of batches.

[0131] i,i′,i″: workpiece index, i,i′,i″∈N.

[0132] j: machine index, j∈M.

[0133] (2) Parameters

[0134] w i ,h i : The width and height of the workpiece,

[0135] p i : Processing time of workpiece,

[0136] d i: The expected delivery time of the workpiece,

[0137] est i ,lst i : They represent the earliest processing start time and the latest processing start time of the workpiece, i∈N.

[0138] (3) Decision variables

[0139] u i′i : 0-1 variable, 1 if workpiece i and workpiece i' are assigned to the same batch, otherwise 0,

[0140] e ii′ : 0-1 variable, if both workpieces i and i′ are critical workpieces and i is processed before i′, then it is 1, otherwise it is 0.

[0141] o i : 0-1 variable, equal to 1 if the height of the i-th workpiece is parallel to the width of the machine (the workpiece is rotated 90°), otherwise equal to 0.

[0142] l i,m : A 0-1 variable, which is 1 if the i-th workpiece is processed on the m-th machine, otherwise it is 0.

[0143] a ii′ : 0-1 variable, which is 1 if the i-th workpiece is to the left of the i′th workpiece, otherwise it is 0.

[0144] b ii′ : 0-1 variable, which is 1 if the i-th workpiece is at the bottom of the i′th workpiece, otherwise it is 0.

[0145] x i : Integer variable, the horizontal coordinate of the lower left corner of workpiece i.

[0146] y i : Integer variable, the vertical coordinate of the lower left corner of workpiece i.

[0147] c i : Integer variable, the actual completion time of job i.

[0148] t i : Integer variable, delay time of job i.

[0149] Step three, based on the concept of maximum time-compatible job sets, a genetic algorithm with adaptive neighborhood search strategy (ANSGA-MTC) is designed to solve the batch scheduling model of the hot stamping process, and a hot stamping production plan scheduling scheme is generated according to the algorithm solution results.

[0150] In this embodiment, Python programming is used to improve the genetic algorithm, and ANSGA-MTC is used to simulate and analyze the hot pressing batch scheduling model to obtain the minimum total delay time of the workpiece. Figure 2 As shown, the solution process based on ANSGA-MTC is:

[0151] (1) Determine the optimization target and establish the optimization model;

[0152] (2) Determine the encoding method;

[0153] ANSGA-MTC uses the integer coding method. After a chromosome is decoded, a solution to the problem is obtained. The number of genes on a chromosome is determined by the number of artifacts in the problem. The gene numbers on the chromosome represent different artifact numbers. The order of the artifact numbers on the chromosome determines the priority of the artifacts when allocating batches.

[0154] (3) Determine the decoding method

[0155] After the priority order of workpieces is obtained according to the encoding strategy, a decoding process needs to be designed to decode the encoded individuals to obtain the corresponding workpiece scheduling scheme. Based on the two rules of first-come-first-served and first-delivery workpieces in the classic production planning and scheduling problem, this paper designs a heuristic decoding method of earliest makespan machine first (EMM) to decode chromosomes.

[0156] like Figure 3 As shown in the figure, the general process of EMM is to select a suitable machine to initialize an empty batch. The selection of machines follows the rule that the batch machine with the smallest completion time is given priority. If the total completion time of each machine is the same, the batch machine with the largest size is given priority. The purpose of this is to minimize the number of processing batches and the total completion time of the production plan. After the batch is initialized, the workpieces are added to the batch in the order of coding until no new workpieces can be added to the batch, the current batch is closed, and the machine is reselected to initialize a new batch. The operation is repeated until all workpieces are assigned to the batch.

[0157] (4) Determine the termination condition and fitness function: The termination condition is to determine whether the current number of iterations I is greater than the maximum number of iterations Gen. The fitness value is the total completion time of the workpiece, and its calculation formula is: f = ∑ i∈N t i , where t i is an integer variable, the delay time of job i

[0158] (5) Initialize the population and perform fitness evaluation.

[0159] Since the initial population of the classic genetic algorithm is randomly encoded, there are many infeasible individuals in the initial population and the population quality is very poor when considering the release time window of the artifact. Therefore, this method introduces the concept of the maximum time-compatible artifact set and generates the encoding gene sequence of the chromosome based on this set. Figure 4 An example of generating chromosome individuals from a maximum set of temporally compatible artifacts is shown.

[0160] (6) Perform selection, crossover and mutation operations and calculate the fitness of new individuals.

[0161] ANSGA-MTC uses the Order-Based Crossover (OBX) crossover method to ensure the global search capability of the algorithm while retaining the excellent gene fragments of the parent chromosome. Figure 5 As shown, given a crossover probability p c , for a pair of parent chromosomes, randomly generate a probability p, if p≤p c , then select several genes at random positions in the parent chromosome 1. The positions of the genes can be discontinuous. Keep the order of the selected genes in chromosome 1 and record them as gene fragment A. Keep the order of all genes in chromosome 2 except gene fragment A, replace the vacant positions in chromosome 2 according to the gene order in gene fragment A, and generate the daughter chromosome 1. Select the gene at the same position of chromosome 2 and record it as gene fragment B. Repeat the same operation with chromosome 1 as the base to generate the daughter chromosome 2.

[0162] ANSGA-MTC adopts the Max Time-compatible Job Set Inverse Mutation (MTCIM) strategy to ensure the quality of individual solutions in the population while improving the algorithm's global search capabilities. Figure 6 As shown, given a mutation probability p m For each individual in the current population, a probability p is randomly generated. If p≤p m , then perform the mutation operation. The specific operation is to randomly select a maximum time compatible artifact set K s , K sThe order of all genes in the chromosome is reversed, and then inserted into the original gene positions in the chromosome in order to generate daughter chromosomes.

[0163] (7) Perform adaptive neighborhood search operations to improve population quality. The adaptive neighborhood selection strategy is as follows:

[0164] The adaptive selection mechanism of ANSGA-MTC is that during the algorithm iteration, when the crossover and mutation operators of the nth iteration are executed, the roulette wheel is initialized or generated according to the scores of the neighborhood search operators of the previous generation. The specific operation is to generate the selection probability p of the search operator in each field of the current population according to the following formula: (ns,n) , using roulette wheel to select the neighborhood search strategy for each individual in the population, the domain search operator selects the probability p (ns,n) The calculation formula is:

[0165]

[0166] where w (ns,n-1) represents the cumulative score weight of the neighborhood search operator (ns) in the n-1th generation, and the score weight calculation formula of each field search operator (ns) in the nth generation is w (ns,n) for:

[0167]

[0168] Among them, r is a parameter, which indicates the influence of the probability of the previous generation of neighborhood search operators being selected on the next generation. When r = 0, it means that the probability of the neighborhood search operator being selected is completely determined by the score after the nth generation of neighborhood search. When r = 1, it means that the probability of the neighborhood search operator being selected is determined by the cumulative score of the previous n-1 generations. In general, let r = 0.5. By search_time ns Indicates the number of times the neighborhood search operator ns has searched for neighborhood solutions during the neighborhood search process of the nth generation population. ns It represents the final score of each neighborhood search operator after the neighborhood search for the nth generation population. The scoring rule is as follows: each individual in the current population is regarded as the current solution. If the neighborhood solution obtained after the neighborhood search is better than the current solution, the score of the corresponding neighborhood search operator is increased by σ1 (the default is 1). If the neighborhood solution is better than the global optimal solution, the score of the corresponding neighborhood search operator is additionally increased by σ2 (the default is 2). If the quality of the neighborhood solution is worse than the current solution, the score of the neighborhood search operator remains unchanged.

[0169] The neighborhood search strategies used in this algorithm include three types of operators: adjustment for the maximum delayed workpiece (MTJ), adjustment for the maximum delayed batch on the same batch processor (MTB), and adjustment for batches between different batch processors (MTM). The specific neighborhood search operators are described as follows:

[0170] like Figure 7 As shown in Figure 2, the MTJ neighborhood search operator inserts the workpiece with the largest total delay time into the batch of the same batch processor with better adaptability. The specific process is as follows:

[0171] The first step is line 1 of the code, which is responsible for recording the workpiece i with the longest delay in the entire allocation plan;

[0172] The second step is line 3 of the code, which is responsible for screening all batches corresponding to all workpieces in the maximum time compatible workpiece set to which workpiece i belongs and screening out all batches B that meet the machine compatibility of workpiece i. i ;

[0173] The third step is line 4 of the code, which is responsible for constructing a scoring function f to judge the compatibility of workpieces and batches. b ;

[0174] Finally, in lines 5-6 of the code, we are responsible for generating a roulette wheel based on the fitness scores of each batch, selecting a batch by probability and inserting the artifact into the batch, and adjusting the encoding of the chromosome individuals.

[0175] In the first step, the score function f that evaluates the suitability of artifacts and batches is b The calculation formula is as follows:

[0176]

[0177] in, Indicates the earliest start time of processing of batch b est b and the earliest start processing time est of workpiece i i The difference, The larger it is, the closer the release time of job i is to the earliest processing start time of batch b; Indicates the latest start processing time lst of batch b b and the latest processing start time lst of workpiece i i The difference, similarly, The larger it is, the closer the external time of workpiece i is to the latest processing start time of batch b; is a piecewise function, when p b -p i ≥0, that is, the batch processing time is not less than the workpiece processing time, When p b -p i <0, that is, when the processing time of the workpiece exceeds the processing time of the batch, It is used to evaluate the impact of workpiece i on the processing time of batch b before and after insertion. , it means that after inserting workpiece i into batch b, the processing time of the entire batch will not increase. , it means that after workpiece i is added to batch b, the number of processed workpieces in the batch increases to the processing time of the workpiece. At this time, the greater the difference between the processing time of the workpiece and the processing time of the batch, The smaller. α represents the degree of influence of the compatibility between the release time window of the workpiece and the start processing time window of the batch on the batch adaptability, β represents the influence of the workpiece processing time on the batch adaptability, α+β=1. When α=1, β=0, it means that only the perspective of whether the workpiece inserts into the batch affects the start processing time window of the batch is considered. When α=0, β=1, it means that only the perspective of whether the workpiece inserts into the batch affects the batch processing time is considered. In general, α=0.5, β=0.5.

[0178] like Figure 8 As shown, the MTB neighborhood search operator adjusts the solution with the longest total delay time with other batches in the same batch of processors. The specific process is as follows:

[0179] The first step is lines 2-3 of the code, which are responsible for selecting the batch b1 with the longest total delay time, and recording the batch processing machine that processes batch b1 as M;

[0180] The second step is lines 4-5 of the code, which are responsible for screening the batches in batch processing machine M that have a processing time less than the start time of batch b1 to form a set B, and randomly select a batch b2 from the batch set B;

[0181] The third step is line 6 of the code, which is responsible for taking out all the workpieces in batches b1 and b2 to form a workpiece set J. i ×h i / d i The purpose is to prioritize the processing of workpieces with earlier expected delivery time and larger size, where w i ,h i is the width and height of the workpiece; d i The expected delivery time of the workpiece.

[0182] Finally, in line 7 of the code, the gene fragment corresponding to the artifact set J in the chromosome is replaced according to the reordered artifact sequence.

[0183] like Fig. 9 As shown in the figure, the MTM neighborhood search operator adjusts the batch with the largest total delay time on a random batch processing machine in the scheme with the batch on a batch processing machine with better adaptability. The specific process is as follows:

[0184] The first step is lines 2-3 of the code, which are responsible for randomly selecting a batch processing machine M1 and recording the batch b1 with the largest total delay time on batch processing machine M1;

[0185] The second step is lines 4-5 of the code, which is responsible for selecting the batch processing machine M2 that is most suitable for adjusting batch b1 based on the applicable machine set of all workpieces in batch b1;

[0186] The third step is line 6 of the code, which is responsible for filtering out all batches in M2 whose completion time is less than batch b1 and randomly selecting a batch b2 as the adjusted batch;

[0187] The fourth step is to merge the workpieces in batch b1 and batch b2, delete the workpieces that cannot be used by batch processors M1 and M2 at the same time, and form a workpiece set J. All workpieces in set J are sorted according to w i ×h i / d i The values ​​are sorted in descending order to ensure that workpieces with more urgent delivery tasks and higher space utilization efficiency of batch processors are processed first;

[0188] Finally, the gene fragments corresponding to set J in the chromosome are replaced according to the order of the artifacts in set J.

[0189] An example of hot pressing process scheduling is constructed below and solved in combination with the ANSGA-MTC of the present invention. The production plan of the M factory for a certain day is optimized, and the size of the workpiece in the plan is shown in Table 1. It is known that the factory has three types of hot pressing tanks, and the sizes of the hot pressing tanks are shown in Table 2.

[0190] Table 1 Weekly planned workpiece information for hot pressing process of a curing group

[0191]

[0192]

[0193] Table 2 Dimensions of operating platforms for different types of autoclaves

[0194]

[0195] The above processing information is input into the ANSGA-MTC of the present invention, and the crossover probability cr=0.9 and the mutation probability mr=0.1 are set, the population size pop=100, and the maximum number of iterations maxgen=100.

[0196] ANSGA-MTC is used to repeat the solution 10 times and take the average. The algorithm iteration diagram is shown below. Fig.10 As shown in Figure 2, it can be seen that the algorithm can converge after 40 iterations, with fast convergence speed and short solution time.

[0197] The workpiece batching scheme obtained by ANSGA-MTC is shown in Table 3, and the original production plan of the factory is shown in Table 4. By comparing the two types of plans, it is found that the delay of the ANSGA-MTC solution is significantly improved, and the total delay time of the workpiece is reduced from 43 hours in the original plan to 9 hours, a decrease of 79%, which greatly improves the production efficiency of hot pressing.

[0198] Table 3 ANSGA-MTC solution schedule

[0199]

[0200]

[0201] Table 4 Factory original plan

[0202]

[0203] like Fig.11 As shown, the present invention also designs an aviation composite hot pressing molding process scheduling system, which can be embedded in the MES system of the composite manufacturing enterprise, including:

[0204] Production information collection module, which is used for workpiece processing information and autoclave model information of the enterprise's periodic planning orders;

[0205] The planning and demand analysis module is used to set the hot pressing molding scheduling objective function according to the preset production planning information and scheduling requirements;

[0206] A planning and scheduling module, used for establishing a two-dimensional parallel machine batch scheduling model associated with the objective function;

[0207] The information processing module is used to solve the two-dimensional parallel machine batch scheduling model by using ANSGA-MTC to obtain the workpiece production plan scheduling solution.

[0208] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0209] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A scheduling optimization method for hot pressing forming process in aviation composite manufacturing, characterized in that: include: Step 1: According to the hot press forming production plan of aviation composite manufacturing, the hot press forming production plan scheduling objective function is established with the goal of minimizing the total delay time of the workpiece; Step 2: Establish a two-dimensional hot press forming process batch scheduling model considering the workpiece release time window and machine applicability; Step 3: Taking the objective function as the optimization target and the two-dimensional hot pressing forming process batch scheduling model as the optimization model, a genetic algorithm embedded with an adaptive domain search strategy is used to solve and obtain a hot pressing forming production plan scheduling solution.

2. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 1 is characterized in that: The step 1 establishes the following hot pressing production scheduling objective function: minT(1) Where T represents the delay time of all workpieces.

3. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 1 is characterized in that: The two-dimensional hot pressing process batch scheduling model established in step 2 includes formula (2) to formula (27): Formula (2) and formula (3) ensure that each workpiece can be allocated only once in the production plan; Formula (4) and formula (5) ensure that the release time windows of any two workpieces assigned to a batch of processing are compatible; Formula (6) ensures that any workpiece can only be processed by one batch in its applicable machine set; Formula (7) ensures that the workpiece will not be assigned to a machine outside its applicable machine set for processing; Formula (8) ensures that the workpieces assigned to the same batch must be processed on the same machine; Formulas (9) and (10) ensure that any workpiece distributed in a batch of workpieces cannot exceed the edge of the operating platform inside the autoclave; Formula (11) and formula (12) ensure that any two workpieces assigned to a batch cannot overlap each other; Formula (13) is used to ensure the relative positions of any two workpieces assigned to the same batch; Through formula (14) and formula (15), it is determined that a batch processing machine can only process one batch of workpieces at a time, and the order of the batches is sorted; By using formula (16), the delay time of each workpiece is determined; The total delay time of the allocation scheme is determined by formula (17), where T represents the delay time of all workpieces; The value range of each variable is determined by formula (18)-formula (27), where M is a positive number; The meanings of the variables and parameters in the above model are as follows: Collections and Indexes: N = {1, 2, ..., n}: represents the set of all workpieces, n is the number of workpieces; M = {1, 2, ..., m}: indicates the machine type, and m indicates the number of machine types; B = {1, 2, ..., b}: represents the set of all batches, b is the number of batches; i,i′,i″: workpiece index, i,i′,i″∈N; j: machine index, j∈M; parameter: w i ,h i : The width and height of the workpiece, p i : Processing time of workpiece, d i : expected delivery time of the workpiece, est i ,lst i : represent the earliest processing time and the latest processing time of the workpiece, i∈N; Decision variables: u i′i : 0-1 variable, 1 if workpiece i and workpiece i' are assigned to the same batch, otherwise 0, e ii′ : 0-1 variable, if both workpieces i and i′ are critical workpieces and i is processed before i′, then it is 1, otherwise it is 0; o i : 0-1 variable, equal to 1 if the height of the i-th workpiece is parallel to the width of the machine, otherwise equal to 0; l i,m : 0-1 variable, if the i-th workpiece is processed on the m-th machine, it is 1, otherwise it is 0; a ii′ : 0-1 variable, 1 if the i-th workpiece is to the left of the i′th workpiece, otherwise 0; b ii′ : 0-1 variable, 1 if the i-th workpiece is at the bottom of the i′th workpiece, otherwise 0; x i : integer variable, the horizontal coordinate of the lower left corner of workpiece i; y i : integer variable, the ordinate of the lower left corner of workpiece i; c i : integer variable, actual completion time of job i; t i : Integer variable, delay time of job i.

4. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 1 is characterized in that: The solution process of the genetic algorithm embedded with the adaptive domain search strategy is as follows: (1) Determine the optimization objective and optimization model: take the objective function established in step 1 as the optimization objective, and take the model established in step 2 as the optimization model; (2) Determine the encoding strategy: Use the integer encoding method. After decoding a chromosome, a solution to the problem is obtained. The number of genes on a chromosome is determined by the number of workpieces in the problem. The gene numbers on the chromosome represent different workpiece numbers. The order of the workpiece numbers on the chromosome determines the priority of the workpieces when allocating batches. (3) Determine the decoding strategy: Use the earliest completed machine first heuristic decoding strategy EMM to decode the chromosome and obtain the corresponding job scheduling plan; (4) Determine the termination condition and fitness function: The termination condition is to determine whether the current number of iterations I is greater than the maximum number of iterations Gen; The fitness function is the total completion time function of the workpiece, and its calculation formula is: f = ∑ i∈N t i , where t i is an integer variable, the delay time of job i; (5) Initializing the population and evaluating the fitness: generating the initial chromosome coding gene sequence based on the maximum time-compatible artifact set segmentation; (6) Perform selection, crossover, and mutation operations to calculate the fitness of the new individual; (7) Perform adaptive neighborhood search operations to improve population quality.

5. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 4 is characterized in that: The process of the EMM is as follows: Select a machine to initialize a batch. The machine selection follows the rule that the batch machine with the smallest completion time is given priority. If the total completion time of each machine is the same, the batch machine with the largest size is given priority. Add workpieces to the batch in coding order until no new workpieces can be added to the batch. Close the current batch, reselect the machine to initialize a new batch, and repeat the operation until all workpieces are assigned to the batch.

6. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 4 is characterized in that: The crossover strategy adopted by the crossover operation is: given a crossover probability p c , for a pair of parent chromosomes, randomly generate a probability p, if p≤p c , then select several genes at random positions in the parent chromosome 1, the positions of the genes may not be continuous, and keep the order of the selected genes in chromosome 1, recorded as gene fragment A; keep the order of all genes in chromosome 2 except gene fragment A, replace the vacant positions in chromosome 2 according to the gene order in gene fragment A, and generate daughter chromosome 1; select the genes at the same position in chromosome 2 and record them as gene fragment B, and repeat the same operation with chromosome 1 as the base to generate daughter chromosome 2; The mutation strategy adopted by the mutation operation is: given a mutation probability p m , for each individual in the current population, randomly generate a probability p, if p≤p m , then perform the mutation operation, the specific operation is: randomly select a time-compatible artifact set K s , K s The order of all genes in the chromosome is reversed, and then inserted into the original gene positions in the chromosome in order to generate daughter chromosomes.

7. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 4 is characterized in that: The adaptive neighborhood search strategy is as follows: During the algorithm iteration process, when the crossover and mutation operators of the nth iteration are executed, the roulette wheel is initialized or generated according to the scores of the neighborhood search operators of the previous generation. The specific operation is: the selection probability p of the search operator in each field of the current population is generated according to the following formula (ns,n) , using roulette wheel to select a neighborhood search strategy for each individual in the population, where the neighborhood search operator selects the probability p (ns,n) The calculation formula is: Where w (ns,n-1) represents the cumulative score weight of the neighborhood search operator ns in the n-1th generation, and the score weight calculation formula of each field search operator ns in the nth generation is w (ns,n) for: Among them, r is a parameter, which indicates the influence of the probability of the previous generation of neighborhood search operators being selected on the next generation; search_time ns Indicates the number of times the neighborhood search operator ns has searched for neighborhood solutions during the neighborhood search process of the nth generation population; score ns It represents the final score of each neighborhood search operator after the neighborhood search for the nth generation population; the scoring rule is as follows: each individual in the current population is regarded as the current solution. If the neighborhood solution obtained after the neighborhood search is better than the current solution, the corresponding neighborhood search operator score is increased by σ1; if the neighborhood solution is better than the global optimal solution, the corresponding neighborhood search operator score is additionally increased by σ2; if the quality of the neighborhood solution is worse than the current solution, the neighborhood search operator score remains unchanged, and σ1 is less than σ2.

8. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 4 is characterized in that: The adaptive neighborhood search is provided with an adjustment operator MTJ for the maximum delayed workpiece, which is used to insert the workpiece with the largest total delay time into the batch of the same batch processor with better adaptability. The specific process is as follows: Record the workpiece i with the longest delay in the entire allocation plan; filter all batches corresponding to all workpieces in the maximum time compatible workpiece set to which workpiece i belongs and filter out all batches B that meet the machine compatibility of workpiece i i ; Construct a score function f to evaluate the adaptability of workpieces and batches b ; Generate a roulette wheel based on the scores of each batch to evaluate the adaptability of the workpiece and the batch, select a batch according to probability and insert the workpiece into the batch, and adjust the encoding of the chromosome individual; the score function f for evaluating the adaptability of the workpiece and the batch b The calculation formula is as follows: in, Indicates the earliest start time of processing of batch b est b and the earliest start processing time est of workpiece i i The difference, The larger it is, the closer the release time of job i is to the earliest processing start time of batch b; Indicates the latest start processing time lst of batch b b and the latest processing start time lst of workpiece i i The difference, The larger it is, the closer the external time of workpiece i is to the latest processing start time of batch b; is a piecewise function, when p b -p i ≥0, that is, the batch processing time is not less than the workpiece processing time, When p b -p i <0, that is, when the processing time of the workpiece exceeds the processing time of the batch, It is used to evaluate the impact of workpiece i on the processing time of batch b before and after insertion. , it means that after inserting workpiece i into batch b, the processing time of the entire batch will not increase. , it means that after workpiece i is added to batch b, the number of processed workpieces in the batch increases to the processing time of the workpiece. At this time, the greater the difference between the processing time of the workpiece and the processing time of the batch, The smaller it is; α represents the influence of the compatibility between the release time window of the workpiece and the time window for starting processing of the batch on the batch adaptability, β represents the influence of the workpiece processing time on the batch adaptability, α+β=1.

9. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 4 is characterized in that: The adaptive neighborhood search is provided with an adjustment operator MTB for the maximum delayed batch on the same batch processing machine, which is used to adjust the batch with the longest total delay time in the scheme with other batches in the same batch processing machine. The specific process is as follows: Select the batch b1 with the longest total delay time, record the batch processing machine that processes batch b1 as M; select the batches in batch processing machine M that are less than the start processing time of batch b1 to form a set B, and randomly select a batch b2 from the batch set B; take out all the workpieces in batches b1 and b2 to form a workpiece set J, and then select the workpieces according to the workpiece w. i ×h i / d i The purpose is to prioritize the processing of workpieces with earlier expected delivery time and larger size; replace the gene fragments corresponding to workpiece set J in the chromosome according to the reordered workpiece sequence, where w i ,h i is the width and height of the workpiece; d i The expected delivery time of the workpiece.

10. The scheduling optimization method for hot pressing forming process in aviation composite manufacturing according to claim 4, characterized in that: The adaptive neighborhood search is provided with an adjustment operator MTM for batches between different batch processing machines, which is used to adjust the batch with the largest total delay time on a random batch processing machine in the scheme with the batch on a batch processing machine with better adaptability. The specific process is as follows: Randomly select a batch machine M1 and record the batch b1 with the largest total delay time on batch machine M1; select the batch machine M2 that is most suitable for adjusting batch b1 according to the applicable machine set of all workpieces in batch b1; select all batches in M2 with a completion time less than batch b1 and randomly select a batch b2 as the adjusted batch; merge the workpieces in batches b1 and batch b2, delete the workpieces that cannot be applied to batch machines M1 and M2 at the same time, form a workpiece set J, and sort all the workpieces in set J according to w i ×h i / d i The values ​​of are sorted in descending order to ensure that the workpieces with more urgent delivery tasks and higher space utilization efficiency of the batch processor are processed first; the gene fragments corresponding to set J in the chromosome are replaced according to the order of the workpieces in set J, where w i ,h i is the width and height of the workpiece; d i The expected delivery time of the workpiece.

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