Production scheduling method applied to extrusion operation, electronic equipment and extrusion system

Abstract

The invention provides a production scheduling method applied to extrusion operation, electronic equipment and an extrusion system. The production scheduling method comprises the following steps: performing operation production scheduling modeling according to a customer order, and obtaining a production scheduling model and mathematical parameters of the model; determining an objective function of the model and setting production constraint conditions; and according to the scheduling model, obtaining an optimal solution as job scheduling information by using an artificial bee colony algorithm, and sending the job scheduling information to user equipment for extruding a job scheduling plan. According to the method, the production scheduling model is specially constructed for profile extrusion operation, reasonable resource allocation is realized in combination with the artificial bee colony algorithm, resource waste and bottleneck are avoided, and the production efficiency is improved; in addition, the defects of an artificial bee colony algorithm are made up by means of a simulated annealing algorithm; according to the whole method, the accurate production scheduling plan and the real-time scheduling capability are considered, the intelligent degree of production scheduling is improved, the order delivery punctuality rate is finally improved, and the customer satisfaction is improved.

Description

Technical Field

[0001] The present invention relates to the field of operation scheduling, and in particular to a scheduling method, electronic equipment and an extrusion system applied to extrusion operations. Background Art

[0002] With the advent of the Industrial 4.0 era, the production mode of enterprises has developed towards an order-driven, multi-variety, small-batch mixed production mode, which is characterized by a large variety of products, small batches, and segmented production. These characteristics require different production methods to be followed at different production stages, and different product processing routes are not the same.

[0003] For production enterprises, reasonable arrangement of extrusion and scheduling in extrusion operations can reduce production material consumption and costs, shorten production cycles, and improve production efficiency. Therefore, reasonable extrusion production scheduling can effectively improve delivery on-time rate, optimize resource allocation, reduce the total processing time of products, and thus increase corporate profits. However, the existing production scheduling used in extrusion operations has a relatively low level of intelligence.

[0004] At present, although there are some technologies for production scheduling methods, such as using time-delay Petri nets to graphically model complex reassembly production lines and using brainstorming optimization algorithms to complete scheduling decisions; for example, modeling the production system based on the Plant Simulation platform and using genetic algorithms to solve, etc., all of which can rely on digital tools and platforms to build models and schedule production, but there is no special production scheduling method with a high degree of intelligence for extrusion operations. This is because aluminum extrusion operations have their own particularities, including the complexity of the process route and the specificity of the equipment. These characteristics require that the production scheduling method must have an in-depth understanding of the process flow and equipment characteristics of aluminum extrusion production when used for scheduling, and the existing general production scheduling methods are difficult to meet the above-mentioned extrusion production needs. Therefore, the market urgently needs to provide a production scheduling method for extrusion operations that uses intelligent algorithms to solve scheduling to meet the needs. Summary of the invention

[0005] In response to the problems existing in the above-mentioned prior art, the present invention proposes a production scheduling method, electronic equipment and extrusion system applied to extrusion operations, so as to improve the intelligence of production scheduling applied to extrusion operations, deeply understand the process flow and equipment characteristics of the extrusion operations, ensure the accuracy and applicability of the production scheduling plan, and greatly improve the production scheduling efficiency.

[0006] In order to achieve the above technical objectives, the present invention provides the following technical solutions:

[0007] A production scheduling method applied to an extrusion operation, which specifically comprises the following steps:

[0008] S1. Modeling the production scheduling of extrusion operations according to customer orders, and obtaining the production scheduling model and mathematical parameters of the model; the mathematical parameters include workpiece batch number, extrusion production line number, process number, production batch parameters, production line parameters and production process parameters;

[0009] S2. Determine the objective function of the production scheduling model; the objective function includes minimizing the production cycle, maximizing equipment utilization, and minimizing costs;

[0010] S3. Determine the production constraints of the production scheduling model; the production constraints include production capacity constraints, material supply constraints, and process flow requirements;

[0011] S4. According to the production scheduling model described in step S1, combined with the objective function described in step S2 and the production constraints described in step S3, an artificial bee colony algorithm is used to obtain the optimal solution as the job scheduling information, and the job scheduling information is sent to the user device; the job scheduling information is used to squeeze the production scheduling plan of the job.

[0012] Further, in step S1,

[0013] The extrusion operation is specifically an aluminum profile extrusion operation, and the specific process includes: heating of aluminum bars, heating of molds, heating of extrusion barrels, cutting of aluminum bars, extrusion, cooling, stretching and straightening, profile sawing, sampling inspection and correction, aging hardening treatment and packaging; different process routes are selected for the extrusion operation according to different requirements of the order; in the specific process of the extrusion operation, the aluminum bars are cut to obtain short bars, the short bars are extruded to become long materials, and the long materials are then sawed to the required length of the order to obtain short materials, i.e., workpieces;

[0014] The mathematical parameters specifically include:

[0015] Production batch parameters: n z , l z , g z 、r z , n z ;

[0016] Where z is the batch number of the workpiece; n z is the number of batch z workpieces to be produced, l z is the length of the profile of the workpiece model in batch z, g z is the weight per meter of the profile of batch z workpiece model, r z The aluminum bar weight coefficient corresponding to the material of the workpiece model profile of batch z;

[0017] Production line parameters: m lb m ,pa m , pb m ,pc m,ta m ;

[0018] Where, m is the extrusion production line number; la m The excess length of the aluminum bar produced by the mth extrusion production line; lb m The length of the cut ends produced by the mth extrusion production line; pa m is the equipment utilization rate of the mth extrusion production line; pb m is the extrusion acceleration relaxation rate of the mth extrusion production line; pc m is the production efficiency relaxation rate of the mth extrusion production line; ta m is the fixed non-extrusion time of the mth extrusion line;

[0019] Production process parameters: Y zm , H zm , J zm , L zm , L z * m , X zm , S zmk 、T zmk 、E zmk ;

[0020] Where k is the process number; Y zm Indicates whether the workpiece model of batch z can be produced on the mth extrusion production line. If yes, it is 1, otherwise it is 0; H zm is the number of die holes of the die used for the production of the z batch of workpieces on the mth extrusion production line; J zm L is the number of single-hole extrusions of the mold used in the production of the z batch of workpieces on the mth extrusion production line; zm is the total length of the extruded part produced by the z batch of workpieces on the mth extrusion line; The short length of the aluminum bar segment to be cut on the mth extrusion production line for the z batch workpiece model; X zm Indicates whether the workpiece model of batch z is assigned to the mth extrusion production line for production, if yes, it is 1, otherwise it is 0; S zmk is the start time of production of batch z of workpieces on process k of the mth extrusion production line; T zmk E is the processing time of the profile of batch z workpiece model in the process k of the mth extrusion production line; zmk It is the end time of production of batch z of workpieces in process k of the mth extrusion production line.

[0021] Furthermore, the objective function described in step S2 is specifically expressed as:

[0022] f=min(max(E zmk ));

[0023] Where f is the minimized production completion time of all extrusion production lines, E zmk It is the end time of production of batch z of workpieces in process k of the mth extrusion production line.

[0024] Further, step S3 includes:

[0025] S31, obtaining production restriction information of aluminum profile extrusion operation;

[0026] S32. Convert the production restriction information into mathematical and logical expressions to form production constraint conditions for the production scheduling model.

[0027] More specifically, the production constraints include:

[0028] Constraint 1: For each subtask of each order, the process path specified by its model, each process is processed by at least one production line and can only be processed once; the formula is expressed as:

[0029]

[0030] Among them, whether the z batch of workpieces can be produced on the mth extrusion production line is determined by the weight per meter of the matching order model and the extrusion pressure of the extruder; that is, its judgment parameter Y zm Given by production experience;

[0031] Constraint 2: The number of extrusion processes and cooling processes that the batch of z workpieces undergoes B zm Determined by the following formula:

[0032]

[0033] Constraint 3: The number of times the z batch of workpieces undergoes the stretch straightening process C zm Determined by the following formula:

[0034] C zm =H zm ·B zm ;

[0035] Constraint 4: The same batch of long materials will be cut to length when the sawing machine has q pieces. If the sawing machine has less than q pieces, it will be cut at the current number of pieces. The number of times D that the profiles of the z batch of workpieces undergo the cut to length process is zm Determined by the following formula:

[0036]

[0037] Constraint 5: In actual production, a short bar is produced by the same type of die. The die type is not changed during the extrusion process until the short bar is extruded. The excess profiles are scrapped after the subsequent cooling, stretching, straightening, and sawing processes. The number of scrapped workpieces in batch z is F. zm Determined by the following formula:

[0038] F zm =B zm ·H zm ·J zm -n z ;

[0039] Constraint 6: Total length of extrusion L zm That is, the total length of a batch of model profiles extruded through the die. Only the number of holes in the die used, the number of single-hole bases, and the length of the profile itself are related to the sawing process. The total extrusion length of batch z of workpieces on production line m is determined by the following formula:

[0040] L zm =X zm ·Y zm ·(J zm ·l z +lb m )·H zm ;

[0041] Constraint 7: The short bar length is only related to the weight of the profile and the excess length of the aluminum bar set on the production line. The short bar length of batch z of workpieces on production line m is determined by the following formula:

[0042]

[0043] Constraint 8: The time required for the production of batch z of workpieces on production line m for short bar extrusion, i.e., one extrusion process, is determined by the following formula:

[0044]

[0045] P zm1 =T zm ;

[0046] Where v refers to the speed at which the main plunger of the extruder moves forward; P zm1 is the time of one extrusion process;

[0047] Constraint 9: Processing time T for batch z of workpieces on production line m zmk Determined by the following formula:

[0048]

[0049] Among them, the primary cooling process time P zm2 Determined according to the model process requirements, it is a constant, denoted by Pzm2 =γ z ; One stretch straightening time P zm3 The default value is constant α, that is, P zm3 =α; the time for a fixed-length sawing process is determined according to the length of the workpiece, and the default value is constant β, that is, P zm4 =β; thus:

[0050]

[0051] Constraint 10: The start and end time of production of batch z at process k on production line m is determined by the following formula:

[0052] When the initial batch is z=1, the start and end time of production at process k on production line m is:

[0053]

[0054] In the above formula, when k = 1, S 1mk 、E 1mk They represent the start time and end time of the extrusion process of the initial batch of workpieces on production line m respectively; the start time is the initial timing zero moment, and the end time is the time when the extrusion process of the batch is completed;

[0055] When k = 2, S 1mk 、E 1mk They represent the start time and end time of the cooling process of the initial batch of workpieces on production line m respectively; once the short bars are extruded, they are formed into long materials and directly enter the cooling device to start cooling. The start time of cooling of this batch of workpieces is the end time of the first extrusion process, and the end time of cooling is the end time of the last extrusion process of this batch of workpieces plus the single cooling time;

[0056] When k = 3, S 1mk 、E 1mk They represent the start time and end time of the stretching and straightening process of the initial batch of workpieces on production line m respectively: the stretching and straightening process will start once the long material is cooled. The start time of the stretching and straightening process of the batch of workpieces is the end time of the first cooling process of the batch of workpieces; the end time is the maximum value of the end time of the last cooling process of the batch of workpieces plus the single stretching and straightening time, and the end time of the second to last straightening process plus the straightening process time;

[0057] When k = 4, S 1mk 、E 1mk They represent the start time and end time of the cut-to-length sawing process of the initial batch of workpieces on production line m. If the number of long pieces in a batch of workpieces is greater than q pieces, the start time is the straightening completion time of the q pieces of long pieces, that is, the cooling completion time of the qth piece of long piece plus the straightening time. is the ordinal number of the short bar used for the qth long material in this batch of workpieces, is the cooling end time of the qth length of material, The straightening order of the qth long material in the same short bar production long material; if the long material does not exceed q, the start time is the end time of the stretch straightening process; the end time of the cut-to-length sawing process is the maximum value of the sum of the end time of the last workpiece straightening and the defined sawing process time, and the sum of the end time of the second-to-last workpiece sawing and the sawing time;

[0058] Based on the start and end time of each process of the initial batch, the start and end time of batch z under process k on production line m is determined by the following formula:

[0059]

[0060] Furthermore, step S4 specifically includes:

[0061] S41, obtaining initialization parameters and randomly generating an initial population, wherein the initialization parameters include population size, evaluation times, and a preset number of times that employed bees become scout bees;

[0062] S42, in the hired bee stage, using the PBX position-based crossover method to perform local search to generate a new solution, evaluate the quality of the new solution, and when the new solution has a higher fitness value than the current solution, use the new solution to replace the current solution and update the position of the hired bee, otherwise abandon the new solution and continue the local search until the first preset termination condition is met;

[0063] S43, in the observation bee stage, according to the fitness value of the solution, a food source is selected by a roulette strategy, and a local search is performed on the neighborhood of the selected food source using the PBX method to find a new food source; obtaining and evaluating whether the fitness value of the new food source is higher than the fitness value of the currently selected food source, if so, updating the position of the observation bee according to the new food source and applying the greedy selection mechanism; otherwise, continuing the local search until the second preset termination condition is met;

[0064] S44, in the scout bee stage, the solution that has not been improved within a predetermined number of iterations is considered abandoned, and the scout bee is sent out to randomly generate a new scheduling sequence in the search space to replace the abandoned solution;

[0065] S45, when the preset stop condition is met, the obtained solution is taken as the optimal solution; otherwise, S42 to S44 are iteratively executed;

[0066] In the process of the above-mentioned artificial bee colony algorithm, each solution corresponds to a bee, and the fitness value of each solution is calculated by the objective function; the first preset termination condition and the second preset termination condition are respectively reaching the corresponding number of iterations or the fitness value meets the preset threshold; the preset stop condition is meeting the maximum number of iterations or meeting the preset threshold of the optimal solution.

[0067] More specifically, the calculation formula of the fitness value is:

[0068] fitness i =f max -f i ;

[0069] Among them, fitness i is the calculated fitness value, f i is the objective function of the ith individual, f max is the objective function of maximizing completion time.

[0070] In addition, when a new customer order is inserted, the method proposed by the present invention further comprises: using a simulated annealing algorithm to adjust the job scheduling information to generate a new scheduling plan.

[0071] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to implement the above-mentioned production scheduling method applied to extrusion operations when executing the computer program.

[0072] The present invention also provides an extrusion system, which is an aluminum profile extrusion system, including the above-mentioned electronic equipment, and also including aluminum rod heating and shearing equipment, extrusion equipment, mold heating equipment, quenching system, roller and traction equipment, cooling bed, front saw, fixed-length sawing equipment and stretching and straightening equipment.

[0073] Based on the above technical solution, the present invention has the following beneficial effects:

[0074] The method proposed in the present invention designs a production scheduling model specifically for profile extrusion operations, and deeply understands the process flow and equipment characteristics of profile extrusion operations, so as to ensure the accuracy and applicability of the production scheduling plan; through the production scheduling model, the artificial bee colony algorithm is used to obtain the optimal solution, realize more reasonable resource allocation, avoid resource waste and bottleneck phenomena, and improve production efficiency. In addition, the simulated annealing algorithm is used to make up for the shortcomings of the artificial bee colony algorithm; the overall method takes into account accurate production scheduling and real-time scheduling capabilities, improves the intelligence of production scheduling, and ultimately improves the on-time delivery rate of customer orders and improves customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1It is a flow chart of a production scheduling method applied to extrusion operations proposed by the present invention;

[0076] Figure 2 It is a schematic diagram of the extrusion process route of aluminum profiles;

[0077] Figure 3 It is a schematic diagram of the evolution of the processing objects in each process of aluminum profile extrusion technology;

[0078] Figure 4 It is a flow chart of obtaining the optimal solution by using the artificial bee colony algorithm in the method proposed by the present invention;

[0079] Figure 5 This is a comparison chart of the running results of the artificial bee colony algorithm and the simulated annealing algorithm;

[0080] Figure 6 It is a process schematic diagram of the PBX method in the method proposed by the present invention;

[0081] Figure 7 It is a structural schematic diagram of an electronic device proposed by the present invention;

[0082] Figure 8 It is a schematic diagram of the layout structure of an extrusion system proposed by the present invention. DETAILED DESCRIPTION

[0083] 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.

[0084] 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.

[0085] like Figure 1 As shown, the present invention proposes a production scheduling method applied to extrusion operations, which specifically includes:

[0086] S1. Modeling the production scheduling of extrusion operations according to customer orders, and obtaining the production scheduling model and mathematical parameters of the model; the mathematical parameters include workpiece batch number, extrusion production line number, process number, production batch parameters, production line parameters and production process parameters;

[0087] As a preferred embodiment, in step S1: the extrusion operation is specifically an aluminum profile extrusion operation, such as Figure 2As shown in the figure, the specific process includes: heating of aluminum bars, heating of molds, heating of extrusion barrels, cutting of aluminum bars, extrusion, cooling, stretching and straightening, profile sawing, sampling inspection and correction, aging hardening treatment and packaging; according to the different requirements of the order, the extrusion operation selects different process routes; in the specific process of the extrusion operation, the evolution process of the processing object is as follows Figure 3 As shown, the aluminum rod is cut into short rods, which are then extruded into long materials, which are then cut into short materials of the required length for the order, i.e., workpieces;

[0088] In this embodiment, the processing path of each batch model of workpieces is set to be the same, and the aluminum rod material used is the same; sudden failures of molds, equipment, etc. are not considered; the quantity of raw materials meets the quantity required for the production of all current orders; the completion time of a batch of tasks is the completion time of the production of the last workpiece in the batch of tasks; the profile is pulled by a traction machine after the extrusion process, and is cooled by a cooling process at the same time. After extrusion to a certain length, it is sawed by a front saw, and the temperature is further cooled on the cooling bed and a stretching and straightening process is performed. After straightening, the material is sawed to a fixed length at the rear saw.

[0089] Based on the specific process of the aluminum profile extrusion operation mentioned above, the extrusion operation scheduling modeling in the present invention solves the problem of production line scheduling of multi-batch processing in a parallel machine environment according to the model of the customer order: taking the minimum and maximum completion time as the goal, Z batches of workpieces complete the processing plan of K processes on N machines. All machines are set to be independent of each other and do not interfere with each other; there are sequence constraints between the processes of the same workpiece, and the workpieces in each batch are produced continuously, that is, a workpiece in a batch can be sent to the next process in the batch process flow for processing after a certain process is completed; each process corresponds to a corresponding device. Limited by the extrusion process, the equipment of the entire extrusion process is generally continuous as a whole, and the machine where the workpiece is currently located and the machine of the next process are a fixed production line, and the machine process, layout order and position on each production line are the same, so the scheduling problem can be further simplified as: Z batches of workpieces complete the processing plan of K processes on M production lines.

[0090] Therefore, the mathematical parameters for modeling the extrusion operation scheduling are set as follows:

[0091] Production batch parameters: n z , l z , g z 、r z , n z ;

[0092] Where z is the batch number of the workpiece; n z is the number of batch z workpieces to be produced, l z is the length of the profile of the workpiece model in batch z, g zis the weight per meter of the profile of batch z workpiece model, r z The aluminum bar weight coefficient corresponding to the material of the workpiece model profile of batch z;

[0093] Production line parameters: m lb m ,pa m , pb m ,pc m ,ta m ;

[0094] Where, m is the extrusion production line number; la m The excess length of the aluminum bar produced by the mth extrusion production line; lb m The length of the cut ends produced by the mth extrusion production line; pa m is the equipment utilization rate of the mth extrusion production line; pb m is the extrusion acceleration relaxation rate of the mth extrusion production line; pc m is the production efficiency relaxation rate of the mth extrusion production line; ta m is the fixed non-extrusion time of the mth extrusion line;

[0095] Production process parameters: Y zm , H zm , J zm , L zm , X zm , S zmk 、T zmk 、E zmk ;

[0096] Where k is the process number; Y zm Indicates whether the workpiece model of batch z can be produced on the mth extrusion production line. If yes, it is 1, otherwise it is 0; H zm is the number of die holes of the die used for the production of the z batch of workpieces on the mth extrusion production line; J zm L is the number of single-hole extrusions of the mold used in the production of the z batch of workpieces on the mth extrusion production line; zm is the total length of the extruded part produced by the z batch of workpieces on the mth extrusion line; The short length of the aluminum bar segment to be cut on the mth extrusion production line for the z batch workpiece model; X zm Indicates whether the workpiece model of batch z is assigned to the mth extrusion production line for production, if yes, it is 1, otherwise it is 0; S zmk is the start time of production of batch z of workpieces on process k of the mth extrusion production line; T zmk E is the processing time of the profile of batch z workpiece model in the process k of the mth extrusion production line; zmkIt is the end time of production of batch z of workpieces in process k of the mth extrusion production line.

[0097] In this embodiment, "z batch workpiece model" means "the model of the workpiece as a workpiece in the zth batch". The workpiece models of the same batch are the same, that is, only one model is produced in each batch; although this article does not take into account the factors of order and delivery time, in actual production, workpieces of the same model can be divided into different batches.

[0098] S2. Determine the objective function of the production scheduling model; the objective function includes minimizing the production cycle, maximizing equipment utilization, and minimizing costs;

[0099] In this embodiment, the objective function is set to:

[0100] f=min(max(E zmk ));

[0101] Where f is the minimized production completion time of all extrusion production lines, E zmk It is the end time of production of batch z of workpieces in process k of the mth extrusion production line.

[0102] The objective function is defined as minimizing the production completion time of all extrusion production lines. By optimizing the production schedule, the processing and waiting time of each workpiece or batch on the production line can be reduced, thereby shortening the entire production cycle. By minimizing the completion time, the operating efficiency of the production line can be improved, the production equipment can be more fully utilized, and the idle time of the equipment can be reduced. The objective function takes into account the reasonable allocation of production resources, ensures that each step in the production process can efficiently utilize resources, avoid resource waste and bottlenecks, and provides a quantitative basis for the production scheduling method.

[0103] S3. Determine the production constraints of the production scheduling model; the production constraints include production capacity constraints, material supply constraints, and process flow requirements;

[0104] As a preferred implementation mode, step S3 is specifically as follows:

[0105] S31, obtaining production restriction information of aluminum profile extrusion operation;

[0106] S32. Based on the production restriction information, obtain the production constraint conditions of the production scheduling model of the aluminum profile extrusion operation.

[0107] Production constraints include, for example, production capacity constraints, material supply constraints, process flow requirements, etc., which are used to ensure the feasibility of the production schedule. Specifically, the production constraint information can be converted into mathematical or logical expressions in advance to form the production constraints of the production schedule model. Production constraints are used to ensure that the production schedule model can simulate the actual production environment, such as equipment cannot be overloaded, raw material supply cannot be interrupted, and process flow cannot be violated. Integrating production constraints into the production schedule model allows the above constraints to be automatically considered when searching for the optimal solution for the production schedule model. The generated production schedule is not only optimized, but also feasible and can be directly applied to actual production.

[0108] In this embodiment, the specific setting of the production constraints is as follows:

[0109] Constraint 1: For each subtask of each order, the process path specified by its model, each process is processed by at least one production line and can only be processed once; the formula is expressed as:

[0110]

[0111] Among them, whether the z batch of workpieces can be produced on the mth extrusion production line is determined by the weight per meter of the matching order model and the extrusion pressure of the extruder; that is, its judgment parameter Y zm Given by production experience;

[0112] Constraint 2: The number of extrusion processes and cooling processes that the batch of z workpieces undergoes B zm Determined by the following formula:

[0113]

[0114] Constraint 3: The number of times the z batch of workpieces undergoes the stretch straightening process C zm Determined by the following formula:

[0115] C zm =H zm ·B zm ;

[0116] Constraint 4: The same batch of long materials will be cut to length when the sawing machine has q pieces. If the sawing machine has less than q pieces, it will be cut at the current number of pieces. The number of times D that the profiles of the z batch of workpieces undergo the cut to length process is zm Determined by the following formula:

[0117]

[0118] Constraint 5: In actual production, a short bar is produced by the same type of die. The die type is not changed during the extrusion process until the short bar is extruded. The excess profiles are scrapped after the subsequent cooling, stretching, straightening, and sawing processes. The number of scrapped workpieces in batch z is F. zm Determined by the following formula:

[0119] F zm =B zm ·H zm ·J zm -n z ;

[0120] Constraint 6: Total length of extrusion L zm That is, the total length of a batch of model profiles extruded through the die. Only the number of holes in the die used, the number of single-hole bases, and the length of the profile itself are related to the sawing process. The total extrusion length of batch z of workpieces on production line m is determined by the following formula:

[0121] L zm =X zm ·Y zm ·(J zm ·l z +lb m )·H zm ;

[0122] Constraint 7: The short bar length is only related to the weight of the profile and the excess length of the aluminum bar set on the production line. The short bar length of batch z of workpieces on production line m is determined by the following formula:

[0123]

[0124] Constraint 8: The time required for the production of batch z of workpieces on production line m for short bar extrusion, i.e., one extrusion process, is determined by the following formula:

[0125]

[0126] P zm1 =T zm ;

[0127] Where v refers to the speed at which the main plunger of the extruder moves forward; P zm1 is the time of one extrusion process;

[0128] Constraint 9: Processing time T for batch z of workpieces on production line m zmk Determined by the following formula:

[0129]

[0130] Among them, the primary cooling process time P zm2 Determined according to the model process requirements, it is a constant, denoted by Pzm2 =γ z ; One stretch straightening time P zm3 The default value is constant α, that is, P zm3 =α; the time for a fixed-length sawing process is determined according to the length of the workpiece, and the default value is constant β, that is, P zm4 =β; thus:

[0131]

[0132] In addition, in this embodiment, the processes k=1, 2, 3, 4 correspond to the extrusion process, the cooling process, the stretching and straightening process, and the cut-to-length sawing process, respectively;

[0133] Constraint 10: The start and end time of production of batch z at process k on production line m is determined by the following formula:

[0134] When the initial batch is z=1, the start and end time of production at process k on production line m is:

[0135]

[0136] In the above formula, when k = 1, S 1mk 、E 1mk They represent the start time and end time of the extrusion process of the initial batch of workpieces on production line m respectively; the start time is the initial timing zero moment, and the end time is the time when the extrusion process of the batch is completed;

[0137] When k = 2, S 1mk 、E 1mk They represent the start time and end time of the cooling process of the initial batch of workpieces on production line m respectively; once the short bars are extruded, they are formed into long materials and directly enter the cooling device to start cooling. The start time of cooling of this batch of workpieces is the end time of the first extrusion process, and the end time of cooling is the end time of the last extrusion process of this batch of workpieces plus the single cooling time;

[0138] When k = 3, S 1mk 、E 1mk They represent the start time and end time of the stretching and straightening process of the initial batch of workpieces on production line m respectively: the stretching and straightening process will start once the long material is cooled. The start time of the stretching and straightening process of the batch of workpieces is the end time of the first cooling process of the batch of workpieces; the end time is the maximum value of the end time of the last cooling process of the batch of workpieces plus the single stretching and straightening time, and the end time of the second to last straightening process plus the straightening process time;

[0139] When k = 4, S 1mk 、E 1mkThey represent the start time and end time of the cut-to-length sawing process of the initial batch of workpieces on production line m. If the number of long pieces in a batch of workpieces is greater than q pieces, the start time is the straightening completion time of the q pieces of long pieces, that is, the cooling completion time of the qth piece of long piece plus the straightening time. is the ordinal number of the short bar used for the qth long material in this batch of workpieces, is the cooling end time of the qth length of material, It is the straightening order of the qth long material in the same short bar production. If the long material does not exceed q, the start time is the end time of the stretch straightening process. The end time of the cut-to-length sawing process is the maximum value of the sum of the end time of the last workpiece straightening and the cut-to-length sawing process time, and the sum of the end time of the penultimate workpiece sawing and the sawing time.

[0140] More specifically, this embodiment uses an example to calculate the start time and end time when k=4:

[0141] In C zm >q, here we discuss two cases, one is q≤H 1m , and secondly, q>H 1m :

[0142] Assuming that two long pieces are sawn together and the mold is a 3-hole mold, then The long material produced by one extrusion can be sawed for the first time.

[0143] Assuming 10 pieces of material are sawn together, the mold is a 3-hole mold. Then four squeezes are required to make one sawing, and the time after the fourth squeeze is After the cooling time P zm2 A total of 12 long materials are cooled and stretched and straightened. When the first long material extruded in the fourth time is stretched and straightened, it can enter the next cut-to-length sawing process. The stretching and straightening time is

[0144] Based on the start and end time of each process of the initial batch, the start and end time of batch z under process k on production line m is determined by the following formula:

[0145]

[0146]

[0147] In a specific application, the production constraint condition includes, for example, a combination of all conditional constraints of the above production constraint conditions.

[0148] S4. According to the production scheduling model described in step S1, combined with the objective function described in step S2 and the production constraint conditions described in step S3, the artificial bee colony algorithm is used to obtain the optimal solution as the job production scheduling information, and the job production scheduling information is sent to the user device; the job production scheduling information is used to squeeze the production schedule of the job. The artificial bee colony algorithm solves multi-dimensional and multi-mode optimization problems by simulating the foraging behavior of bees. It has the characteristics of few parameters, easy implementation, strong robustness, etc., and is suitable for global optimization problems of continuous functions.

[0149] As a preferred embodiment, Figure 4 As shown, step S4 specifically includes:

[0150] S41, obtaining initialization parameters and randomly generating an initial population, wherein the initialization parameters include population size, evaluation times, and a preset number of times that employed bees become scout bees;

[0151] S42, in the hired bee stage, using the PBX position-based crossover method to perform local search to generate a new solution, evaluate the quality of the new solution, and when the new solution has a higher fitness value than the current solution, use the new solution to replace the current solution and update the position of the hired bee, otherwise abandon the new solution and continue the local search until the first preset termination condition is met;

[0152] S43. In the observation bee stage, a food source is selected through a roulette strategy according to the fitness value of the solution, and a local search is performed on the neighborhood of the selected food source using the PBX method to find a new food source; whether the fitness value of the new food source is higher than the fitness value of the currently selected food source is obtained and evaluated. If so, the position of the observation bee is updated according to the new food source and the greedy selection mechanism is applied; otherwise, the local search is continued until the second preset termination condition is met; in solving optimization problems, especially in scheduling problems, the PBX method is used to generate new solutions, and the basic principle of the roulette strategy is to determine the probability of being selected based on the individual's fitness. Individuals with high fitness have a higher chance of being selected, but individuals with low fitness also have a certain chance of being selected to maintain the diversity of the population.

[0153] This embodiment provides a specific process of the PBX method, such as Figure 6 As shown in the figure, the artificial bees randomly select two individuals from the population and use the Position-based Crossover (PBX) method to learn and generate new individuals. For individuals 1 and 2, new individuals are generated. The batch numbers z are 1 to 8, and the results of whether they are produced on production line m are 1 or 0. The corresponding specific crossover process is as follows:

[0154] In the first step, several genes are randomly selected from a pair of chromosomes. The positions may not be continuous, but the selected positions on the two chromosomes are the same.

[0155] The second step is to generate an offspring and ensure that the position of the selected gene in the offspring is the same as that of the parent (individual 1 in the figure).

[0156] Step 3: First find the genes at the corresponding positions of the genes selected in the first step in the other parent generation (individual 2 in the figure), and then put these genes into the offspring generated in the previous step in order to obtain new individuals.

[0157] S44, in the scout bee stage, the solution that has not been improved within a predetermined number of iterations is considered abandoned, and the scout bee is sent out to randomly generate a new scheduling sequence in the search space to replace the abandoned solution;

[0158] S45, when the preset stop condition is met, the obtained solution is taken as the optimal solution; otherwise, S42 to S44 are iteratively executed;

[0159] In this embodiment, the artificial bee colony algorithm is used to optimize the production scheduling of the job. During execution, the chromosome encoding uses the scheduling plan as an individual, and the scheduling plan is encoded in a matrix encoding manner. The matrix size is (|M|+1)×|Z|, the chromosome length is the total number of batches, and one column of the chromosome represents a gene, representing the processing order and allocation machine of the batch. The first row of the matrix can be regarded as an encoding method based on the full arrangement of the process code, and the remaining rows of the matrix are X zm Taking 6 batches of workpieces and 2 extrusion production lines as an example, the matrix obtained is as follows:

[0160]

[0161] In the above matrix code, the first row represents the order in which the workpieces are processed. For example, if the fourth column is 3, workpiece 4 is the third one to be processed. The second row represents whether it is processed on production line 1. For example, if the first, fourth and fifth columns are 1, workpieces 1, 4 and 5 are processed on production line 1. The third row represents whether it is processed on production line 2. For example, if the second, third and sixth columns are 1, workpieces 2, 3 and 6 are processed on production line 2 (the processing order is 3, 2 and 6). Therefore, the code shown in the above matrix indicates that the workpieces are processed in the order of 1, 3, 4, 2, 6 and 5, among which 1, 4 and 5 are produced on production line 1 and 2, 3 and 6 are produced on production line 2. The production order of different production lines does not conflict.

[0162] Correspondingly, during decoding, the processing order of each production line is calculated based on the processing order of the batches and the production line allocation represented by the genes of each chromosome; then the extrusion time is determined based on the mold parameters of the workpiece and the allocation machine, and finally the start and completion time of the batch, as well as the maximum completion time of the entire production line, are determined.

[0163] In the process of the artificial bee colony algorithm, each solution corresponds to a bee, and the fitness value of each solution is calculated by the objective function; the first preset termination condition and the second preset termination condition are respectively reaching the corresponding number of iterations or the fitness value meets the preset threshold; the preset stop condition is meeting the maximum number of iterations or meeting the preset threshold of the optimal solution;

[0164] The formula for calculating the fitness value of a new solution or current solution is:

[0165] fitness i =f max -f i

[0166] Among them, fitness i is the calculated fitness value, f i is the objective function of the ith individual, f max is the objective function of the maximum completion time. In this way, the algorithm takes minimizing the maximum completion time as the objective function. The smaller the objective function, the greater the fitness value, and thus the optimal solution can be output.

[0167] In addition, when a new customer order is inserted, the method proposed in the present invention also uses a simulated annealing algorithm to adjust the job scheduling information to generate a new scheduling plan to avoid ignoring local changes in the process of global optimization. Specifically, when a new customer order arrives, the initial parameters of the simulated annealing algorithm (for example, including initial temperature, cooling rate, stop temperature, etc.) can be obtained or set. Generate candidate solutions, that is, randomly select a solution from the current job scheduling information as the starting point, and randomly perturb the neighborhood of the solution to generate new candidate solutions. Use the objective function to evaluate the quality of the current solution and the newly generated candidate solution. If the quality of the candidate solution is better than the current solution, accept the candidate solution as the new current solution. If the quality of the candidate solution is not better than the current solution, accept the candidate solution with a certain probability, and this probability decreases as the temperature decreases. After a certain number of iterations, the temperature is lowered according to the cooling plan. Repeat the generation of candidate solutions and subsequent steps until the temperature drops to the set minimum value or reaches the preset number of iterations and other stop conditions. Once the stop condition is met, the iteration is stopped and the final job scheduling information is output as a new scheduling plan.

[0168] In this way, the dynamic changes of the insertion of urgent orders in the production process can be adapted to ensure the flexibility and responsiveness of the production plan. The production plan can be quickly adjusted to adapt to new orders, reducing production delays and potential delivery delays caused by inserted orders, and further improving the intelligence of the production scheduling method. So far, the overall technical solution of the method proposed by the present invention has been described, and the following example will verify the effect:

[0169] An aluminum profile manufacturer was used as the verification background, and the experiment was conducted on its actual order data. Within the given parameter range, an initial population of N was randomly generated. The number of initial populations was set to 30, the maximum number of iterations was set to 100, the number of employed bees, follower bees, and nectar sources was the same as the population size, and the count of nectar sources abandoned by scout bees was set to 50.

[0170] Different models of orders have different extrusion times on different production lines, while the cooling, stretching and straightening, cut-to-length sawing, and correction process times are not affected by the production line. According to the above extrusion process time calculation method, the enterprise order data is processed to obtain the specific predicted extrusion time;

[0171] Some order batch parameters are shown in Table 1 below:

[0172] Table 1 Part of the order batch parameters

[0173]

[0174] The parameters of different extrusion machines are shown in Table 2:

[0175] Table 2 Parameters of different extrusion machines

[0176] serial number <![CDATA[la m ]]> <![CDATA[lb m ]]> <![CDATA[pa m ]]> <![CDATA[pb m ]]> <![CDATA[pc m ]]> <![CDATA[ta m ]]> 1 100 1.5 0.125 0.03 0.06 23 2 135 1.5 0.208 0.03 0.06 27 3 135 1.5 0.153 0.03 0.06 27 4 178 1.5 0.153 0.05 0.06 16 5 178 1.5 0.208 0.05 0.06 16 6 229 2.0 0.208 0.05 0.06 19

[0177] The artificial bee colony algorithm proposed in this application is used for solution, and compared with the scenario of simulated annealing algorithm, it is proved that the algorithm can give a reasonable and effective production scheduling plan. The simulated annealing algorithm setting parameters are: initial temperature 1000, end temperature 1, temperature attenuation coefficient 0.95, and new solutions are generated by exchanging the production scheduling order of the solution and updating the allocation machine, and the new solution is accepted when it is better. Figure 5 (a) Figure 5 (b) Figure 5 (c) Comparison of the results when the order batch |Z| is set to 10, 20, and 100 during the optimization iteration of the two algorithms. ABC is the operating curve of the artificial bee colony algorithm, and SA is the operating curve of the simulated annealing algorithm.

[0178] from Figure 5 (a) Figure 5 (b) Figure 5 (c) It can be seen that in the technical solution of the production scheduling method provided by this application, the simulated annealing algorithm converges faster, but from the optimization results, the artificial bee colony algorithm can obtain a better solution and effectively avoid falling into the local optimal solution. The running time comparison of the two algorithms is shown in Table 3 below.

[0179] Table 3 Comparison of running time of two algorithms

[0180] Z Artificial Bee Colony Algorithm Simulated annealing algorithm 10 16.89s 0.26s 20 38.97s 0.51s 50 59.72s 0.84s 100 105.29s 1.28s 200 216.57s 3.66s

[0181] When the number of order batches increases, the computation time of the artificial bee colony algorithm increases exponentially, while the running time of the simulated annealing algorithm does not increase significantly. Compared with the artificial bee colony algorithm, the simulated annealing algorithm can obtain calculation results faster. Therefore, the two methods are complementary to each other, and their combined use can achieve the search for the global optimal solution without ignoring local changes.

[0182] Therefore, the method proposed in the present invention is based on the actual production scheduling needs and problems of aluminum extrusion operations. After the operation scheduling model is built according to the customer order, the artificial bee colony algorithm is used to obtain the optimal solution as the operation scheduling information, and the simulated annealing algorithm is used to regenerate the scheduling plan in the subsequent scenario of urgent orders, thereby improving the intelligence of production scheduling.

[0183] In addition, this embodiment provides Figure 7 An electronic device is shown, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to implement the method proposed by the present invention when executing the computer program.

[0184] The electronic device 200 includes, for example, at least one memory 210 , at least one processor 220 , and a bus 230 connecting different platform systems.

[0185] The memory 210 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 211 and / or a cache memory 212 , and may further include a read-only memory (ROM) 213 .

[0186] Among them, the memory 210 also stores a computer program, which can be executed by the processor 220, so that the processor 220 executes the steps of the method in the method embodiment of the present application. Its specific implementation method is consistent with the implementation method and the technical effect achieved in the embodiment of the above method, and some contents will not be repeated here.

[0187] The memory 210 may also include a utility 214 having at least one program module 215, such program module 215 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0188] Accordingly, the processor 220 may execute the above-mentioned computer program, and may execute the utility 214 .

[0189] Bus 230 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or local bus using any of a variety of bus architectures.

[0190] The electronic device 200 may also communicate with one or more external devices 240 such as keyboards, pointing devices, Bluetooth devices, etc., and may also communicate with one or more devices that can interact with the electronic device 200, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. Such communication may be performed through the input and output interface 250. In addition, the electronic device 200 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 through the bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0191] This embodiment also provides an extrusion system, which is an aluminum profile extrusion system, including the above-mentioned electronic equipment, and Figure 8 The aluminum rod heating and shearing equipment 1, the extrusion equipment 2, the mold heating equipment 3, the quenching system 4, the roller and traction equipment 5, the cooling bed 6, the front saw 7, the cut-to-length sawing equipment 8 and the stretching and straightening equipment 9 are shown.

[0192] In summary, the method, electronic equipment and extrusion system proposed in the present invention realize reasonable production scheduling and dispatching during the extrusion operation, which plays a role in reducing production material consumption and costs, shortening production cycles and improving production efficiency.

[0193] It should be noted that it is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-restrictive from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any reference numerals in the claims should not be regarded as limiting the claims involved.

[0194] 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 production scheduling method for extrusion operation, characterized in that: The specific steps include: S1. Modeling the production scheduling of extrusion operations according to customer orders, and obtaining the production scheduling model and mathematical parameters of the model; the mathematical parameters include workpiece batch number, extrusion production line number, process number, production batch parameters, production line parameters and production process parameters; S2. Determine the objective function of the production scheduling model; the objective function includes minimizing the production cycle, maximizing equipment utilization, and minimizing costs; S3. Determine the production constraints of the production scheduling model; the production constraints include production capacity constraints, material supply constraints, and process flow requirements; S4. According to the production scheduling model described in step S1, combined with the objective function described in step S2 and the production constraints described in step S3, an artificial bee colony algorithm is used to obtain the optimal solution as the job scheduling information, and the job scheduling information is sent to the user device; the job scheduling information is used to squeeze the production scheduling plan of the job.

2. A production scheduling method for extrusion operations according to claim 1, characterized in that: In step S1, The extrusion operation is specifically an aluminum profile extrusion operation, and the specific process includes: heating of aluminum bars, heating of molds, heating of extrusion barrels, cutting of aluminum bars, extrusion, cooling, stretching and straightening, profile sawing, sampling inspection and correction, aging hardening treatment and packaging; different process routes are selected for the extrusion operation according to different requirements of the order; in the specific process of the extrusion operation, the aluminum bars are cut to obtain short bars, the short bars are extruded to become long materials, and the long materials are then sawed to the required length of the order to obtain short materials, i.e., workpieces; The mathematical parameters specifically include: Production batch parameters: n z , l z , g z 、r z , n z ; Where z is the batch number of the workpiece; n z is the number of batch z workpieces to be produced, l z is the length of the profile of the workpiece model in batch z, g z is the weight per meter of the profile of batch z workpiece model, r z The aluminum bar weight coefficient corresponding to the material of the workpiece model profile of batch z; Production line parameters: m lb m ,pa m , pb m ,pc m ,ta m ; Where, m is the extrusion production line number; la m The excess length of the aluminum bar produced by the mth extrusion production line; lb m The length of the cut ends produced by the mth extrusion production line; pa m is the equipment utilization rate of the mth extrusion production line; pb m is the extrusion acceleration relaxation rate of the mth extrusion production line; pc m is the production efficiency relaxation rate of the mth extrusion production line; ta m is the fixed non-extrusion time of the mth extrusion line; Production process parameters: Y zm , H zm , J zm , L zm , X zm , S zmk , T zmk 、E zmk ; Where k is the process number; Y zm Indicates whether the model of the workpiece in batch z can be produced on the mth extrusion production line. If yes, it is 1, otherwise it is 0; H zm is the number of die holes of the die used for the production of the z batch of workpieces on the mth extrusion production line; J zm L is the number of single-hole extrusions of the mold used in the production of the z batch of workpieces on the mth extrusion production line; zm is the total length of the extruded part produced by the z batch of workpieces on the mth extrusion line; The short length of the aluminum bar segment that needs to be cut on the mth extrusion production line for the z batch workpiece model; X zm Indicates whether the workpiece model of batch z is assigned to the mth extrusion production line for production, if yes, it is 1, otherwise it is 0; S zmk is the start time of production of batch z of workpieces on process k of the mth extrusion production line; T zmk E is the processing time of the profile of batch z of workpieces in process k of the mth extrusion production line; zmk It is the end time of production of batch z of workpieces in process k of the mth extrusion production line.

3. A production scheduling method for extrusion operations according to claim 2, characterized in that: The objective function described in step S2 is specifically expressed as: f=min(max(E zmk )); Where f is the minimized production completion time of all extrusion production lines, E zmk It is the end time of production of batch z of workpieces in process k of the mth extrusion production line.

4. A production scheduling method for extrusion operations according to claim 2, characterized in that: Step S3 includes: S31, obtaining production restriction information of aluminum profile extrusion operation; S32. Convert the production restriction information into mathematical and logical expressions to form production constraint conditions for the production scheduling model.

5. A production scheduling method for extrusion operations according to claim 4, characterized in that: The production constraints specifically include: Constraint 1: For each subtask of each order, each process of the process path specified by its model must be processed by at least one production line and can only be processed once; the formula is: Among them, whether the z batch of workpieces can be produced on the mth extrusion production line is determined by the weight per meter of the matching order model and the extrusion pressure of the extruder; that is, its judgment parameter Y zm Given by production experience; Constraint 2: The number of extrusion processes and cooling processes that the batch of z workpieces undergoes B zm Determined by the following formula: Constraint 3: The number of times the z batch of workpieces undergoes the stretch straightening process C zm Determined by the following formula: C zm =H zm ·B zm ; Constraint 4: The same batch of long materials will be cut to length when the sawing machine has q pieces. If the sawing machine has less than q pieces, it will be cut at the current number of pieces. The number of times D that the profiles of the z batch of workpieces undergo the cut to length process is zm Determined by the following formula: Constraint 5: In actual production, a short bar is produced by the same type of die. The die type is not changed during the extrusion process until the short bar is extruded. The excess profiles are scrapped after the subsequent cooling, stretching, straightening, and sawing processes. The number of scrapped workpieces in batch z is F. zm Determined by the following formula: F zm =B zm ·H zm ·J zm -n z ; Constraint 6: Total length of extrusion L zm That is, the total length of a batch of model profiles extruded through the die. Only the number of holes in the die used, the number of single-hole bases, and the length of the profile itself are related to the sawing process. The total extrusion length of batch z of workpieces on production line m is determined by the following formula: L zm =X zm ·Y zm ·(J zm ·l z +lb m )·H zm ; Constraint 7: The short bar length is only related to the weight of the profile and the excess length of the aluminum bar set on the production line. The short bar length of batch z of workpieces on production line m is determined by the following formula: Constraint 8: Batch z of workpieces are produced on production line m, and short rods are extruded, that is, the one-time extrusion process time is determined by the following formula: P zm1 =T zm ; Where v refers to the speed at which the main plunger of the extruder moves forward; P zm1 is the time of one extrusion process; Constraint 9: Processing time T for batch z of workpieces on production line m zmk Determined by the following formula: Among them, the primary cooling process time P zm2 Determined according to the model process requirements, it is a constant, denoted by P zm2 =γ z ; One stretch straightening time P zm3 The default value is constant α, that is, P zm3 =α; the time for a fixed-length sawing process is determined according to the length of the workpiece, and the default value is constant β, that is, P zm4 =β; thus: Constraint 10: The start and end time of production of batch z at process k on production line m is determined by the following formula: When the initial batch is z=1, the start and end time of production at process k on production line m is: In the above formula, when k = 1, S 1mk 、E 1mk They represent the start time and end time of the extrusion process of the initial batch of workpieces on production line m respectively; the start time is the initial timing zero moment, and the end time is the time when the extrusion process of the batch is completed; When k = 2, S 1mk 、E 1mk They represent the start time and end time of the cooling process of the initial batch of workpieces on production line m respectively; once the short bars are extruded, they are formed into long materials and directly enter the cooling device to start cooling. The start time of cooling of this batch of workpieces is the end time of the first extrusion process, and the end time of cooling is the end time of the last extrusion process of this batch of workpieces plus the single cooling time; When k = 3, S 1mk 、E 1mk They represent the start time and end time of the stretching and straightening process of the initial batch of workpieces on production line m respectively: the stretching and straightening process will start once the long material is cooled. The start time of the stretching and straightening process of the batch of workpieces is the end time of the first cooling process of the batch of workpieces; the end time is the maximum value of the end time of the last cooling process of the batch of workpieces plus the single stretching and straightening time, and the end time of the second to last straightening process plus the straightening process time; When k = 4, S 1mk 、E 1mk They represent the start time and end time of the cut-to-length sawing process of the initial batch of workpieces on production line m. If the number of long pieces in a batch of workpieces is greater than q pieces, the start time is the straightening completion time of the q pieces of long pieces, that is, the cooling completion time of the qth piece of long piece plus the straightening time. is the ordinal number of the short bar used for the qth long material in this batch of workpieces, is the cooling end time of the qth length of material, It is the straightening order of the qth long material in the same short bar production. If the long material does not exceed q, the start time is the end time of the stretch straightening process. The end time of the cut-to-length sawing process is the maximum value of the sum of the end time of the last workpiece straightening and the cut-to-length sawing process time, and the sum of the end time of the penultimate workpiece sawing and the sawing time. Based on the start and end time of each process of the initial batch, the start and end time of batch z under process k on production line m is determined by the following formula:

6. A production scheduling method for extrusion operations according to claim 1, characterized in that: Step S4 specifically includes: S41, obtaining initialization parameters and randomly generating an initial population, wherein the initialization parameters include population size, evaluation times, and a preset number of times that employed bees become scout bees; S42, in the hired bee stage, using the PBX position-based crossover method to perform local search to generate a new solution, evaluate the quality of the new solution, and when the new solution has a higher fitness value than the current solution, use the new solution to replace the current solution and update the position of the hired bee, otherwise abandon the new solution and continue the local search until the first preset termination condition is met; S43, in the observation bee stage, according to the fitness value of the solution, a food source is selected by a roulette strategy, and a local search is performed on the neighborhood of the selected food source using the PBX method to find a new food source; obtaining and evaluating whether the fitness value of the new food source is higher than the fitness value of the currently selected food source, if so, updating the position of the observation bee according to the new food source and applying the greedy selection mechanism; otherwise, continuing the local search until the second preset termination condition is met; S44, in the scout bee stage, the solution that has not been improved within a predetermined number of iterations is considered abandoned, and the scout bee is sent out to randomly generate a new scheduling sequence in the search space to replace the abandoned solution; S45, when the preset stop condition is met, the obtained solution is taken as the optimal solution; otherwise, S42 to S44 are iteratively executed; In the process of the above-mentioned artificial bee colony algorithm, each solution corresponds to a bee, and the fitness value of each solution is calculated by the objective function; the first preset termination condition and the second preset termination condition are respectively reaching the corresponding number of iterations or the fitness value meets the preset threshold; the preset stop condition is meeting the maximum number of iterations or meeting the preset threshold of the optimal solution.

7. A production scheduling method for extrusion operations according to claim 6, characterized in that: The calculation formula of the fitness value is: fitness i =f max -f i ; Among them, fitness i is the calculated fitness value, f i is the objective function of the ith individual, f max is the objective function of maximizing completion time.

8. A production scheduling method for extrusion operations according to claim 1, characterized in that: When a new customer order is inserted, the method further comprises: using a simulated annealing algorithm to adjust the job scheduling information to generate a new scheduling plan.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to implement the production scheduling method for extrusion operations described in any one of claims 1 to 8 when executing the computer program.

10. An extrusion system, characterized in that: The extrusion system is an aluminum profile extrusion system, including the electronic device described in claim 9, and also includes aluminum rod heating and shearing equipment, extrusion equipment, mold heating equipment, quenching system, roller and traction equipment, cooling bed, front saw, cut-to-length sawing equipment and stretching and straightening equipment.

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