Dynamic production scheduling method of workshop, medium and equipment

Abstract

The invention relates to a workshop dynamic production scheduling method, device and equipment, and the method comprises the steps: building a process time matrix and a transfer time matrix, obtaining a flexible workshop production scheduling optimization model, sorting the numbers of workpieces to be processed, building an initial population, enabling the coding sequences of a plurality of individuals to be crossed in pairs, obtaining a crossed population, and obtaining a flexible workshop production scheduling optimization model; and performing mutation operation to obtain a mutated population, retaining the updated population according to a roulette mechanism, designing a preset scheduling scheme according to the flexible workshop production scheduling optimization model, and executing a task based on the preset scheduling scheme until the to-be-processed workpiece is processed. The problems of long calculation time, difficulty in adapting to rapid change in the production process and the like in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling, and in particular to a method, device and equipment for dynamic production scheduling of a workshop. Background Art

[0002] In modern manufacturing, production scheduling of flexible workshops in factories is a key link to ensure production efficiency and product quality. Traditional production scheduling methods mainly rely on operations research methods, simulation technology and heuristic rules. Operations research methods transform production scheduling problems into mathematical programming models and use branch and bound methods or dynamic programming algorithms to seek optimal or approximately optimal production scheduling plans. Simulation methods evaluate and analyze different production scheduling plans by simulating the logical relationships in the production process to evaluate the dynamic performance of the system. Heuristic rules have been widely used in actual production scheduling systems because of their simplicity, ease of implementation and low computational complexity.

[0003] However, these traditional methods have certain limitations in practical applications. First, manual scheduling is inefficient and cannot meet the needs of modern manufacturing for rapid response and flexibility. Second, although traditional operations research methods can provide optimal solutions in theory, they take a long time to calculate and cannot adapt to rapid changes in the production process.

[0004] Therefore, how to improve it is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] Based on this, the purpose of this application is to provide a dynamic production scheduling method, medium and equipment for a workshop to solve at least one technical problem mentioned in the above background technology.

[0006] In a first aspect, the present application provides a dynamic production scheduling method for a workshop, comprising:

[0007] Obtain the processing equipment information, transfer equipment information and workpiece information to be processed in the workshop, and build the process time matrix and transfer time matrix; the workpiece information to be processed, including the workpiece number to be processed;

[0008] According to the workshop's processing equipment information, transfer equipment information, workpiece information to be processed, process time matrix and transfer time matrix, a flexible workshop production scheduling optimization model is constructed, including: the objective function that maximizes the average utilization of equipment and the corresponding constraints;

[0009] According to the preset random strategy, the shortest processing time optimization strategy and the longest processing time optimization strategy, the numbers of the workpieces to be processed are sorted to obtain a number of individuals to construct an initial population;

[0010] Select several individuals from the initial population, cross their coding sequences two by two, and obtain the population after the crossover;

[0011] Perform mutation operation on the individuals in the population after crossover to obtain the mutated population;

[0012] The fitness values ​​of individuals in the population after crossover and the population after mutation are calculated according to the preset target evaluation function, so as to retain the updated population according to the roulette mechanism;

[0013] Design a preset scheduling plan based on the flexible workshop production scheduling optimization model, and execute tasks based on the preset scheduling plan until the workpiece to be processed is completed.

[0014] Furthermore, the steps of constructing the process time matrix are as follows:

[0015] According to the process flow of the workpiece to be processed, the workstations that need to be processed are determined, and the processing speed of each processing equipment in the workstation is combined to obtain the processing time of the workpiece to be processed on each processing equipment in each workstation, so as to construct a process time matrix, including: the workpiece number and the corresponding workstation time and the total processing time.

[0016] Furthermore, the steps of constructing the transit time matrix are as follows:

[0017] According to the distance between each processing equipment, as well as the operating speed and acceleration of the transfer equipment, the transfer time between each workstation is obtained to construct a transfer time matrix.

[0018] Furthermore, the step of constructing the transit time matrix also includes:

[0019] Obtain the distance between each processing equipment, as well as the operating speed and acceleration of the transfer equipment, to construct the transfer time matrix according to equations 1-3, 1-4, and 1-5:

[0020] T a =(V f -V i ) / a 1-3

[0021] T c =D / V f 1-4

[0022] T d =(V f -V i ) / b 1-5

[0023] Among them, T a is the acceleration phase time, V f is the final speed of the transfer equipment, V i is the initial velocity of the transfer equipment, a is the acceleration rate, T cis the uniform speed stage time, D is the distance between processing equipment, T d is the deceleration stage time, b is the deceleration rate, according to T a、 T c and T d The transit time between each workstation is calculated to construct a transit time matrix. The transit time between each workstation is calculated to construct a transit time matrix.

[0024] Furthermore, the steps of constructing a flexible workshop production scheduling optimization model include:

[0025] Obtain the information of the workshop's processing equipment, transfer equipment, and workpieces to be processed, and build an initial model for flexible workshop production scheduling;

[0026] Decision variables and intermediate variables are defined to construct the objective function and constraints that maximize the average equipment utilization, and obtain the flexible workshop production scheduling optimization model.

[0027] Furthermore, the objective function and constraints are as follows:

[0028] Objective function formula 2-1:

[0029]

[0030] Constraints 2-2, 2-3, 2-4, 2-5:

[0031]

[0032] The decision variables are: ijk ∈{0,1}, indicating that the kth process of steel plate i is processed on equipment j, y ijv ∈{0,1}, indicating that the steel plate i is transferred by the transverse vehicle v after being processed on the equipment j. The specific intermediate variables are: s ikj represents the start time of processing k of steel plate i in equipment j, e ikj represents the processing completion time of process k of steel plate i in equipment j, a jv It indicates the time when the transverse vehicle v arrives at the equipment j station, l jv represents the time when the transverse vehicle v leaves the equipment j station, T max represents the maximum completion time, T j represents the total processing time of equipment j.

[0033] Furthermore, the steps of constructing the initial population include:

[0034] Randomly sort the numbers of the workpieces to be processed to obtain a number of individuals;

[0035] Arrange the numbers of the workpieces to be processed in order from shortest to longest according to the total processing time to obtain a number of individuals;

[0036] Arrange the numbers of the workpieces to be processed in order from longest to shortest according to the total processing time to obtain a number of individuals;

[0037] Construct the initial population with all individuals.

[0038] Furthermore, the steps of obtaining the population after crossover include:

[0039] S41: Take the initial population as the current population;

[0040] S42: Select any two individuals from the current population, calculate the crossover probability between the two individuals, and determine whether it is greater than a set threshold. If so, cross the encoding sequences of the two individuals to obtain an updated population. If not, keep the population unchanged.

[0041] S43: Determine whether the number of selected individuals is greater than the set number threshold. If so, take the updated population as the population after crossover. If not, take the updated population as the current population and return to step S42.

[0042] Furthermore, the step of executing the task based on the preset scheduling scheme also includes

[0043] Determine whether an abnormal situation occurs; if no abnormal situation occurs and the workpiece to be processed is completed, the method ends; if no abnormal situation occurs and the workpiece to be processed is not completed, continue to execute the task based on the preset scheduling plan, if an abnormal situation occurs, then:

[0044] According to the type of abnormal situation, a processing method and correction strategy corresponding to the type of abnormal situation are selected. Based on the processing method and correction strategy, the respective positions and earliest available time of the processing equipment in the current workshop, the respective positions and earliest available time of the transfer equipment, the size, quantity and number of the workpieces to be processed that have not yet started processing, and the respective positions of the workpieces that have not completed all processing procedures are obtained as new information of the workpieces to be processed, which is used as the new initial state of the workshop. The initial population is reconstructed with the new information of the workpieces to be processed and the new initial state of the workshop, and step S2 is entered.

[0045] Furthermore, the specific processing methods and correction strategies are as follows:

[0046] When a processing equipment fails, the processes in the processing process window and the unprocessed process window affected by the failure are completely rescheduled, and then the faulty equipment is deleted from the optional machine set. The processing processes and the unprocessed processes affected by the failure are used as scheduling task inputs of the genetic algorithm, and the genetic algorithm is called to re-optimize the production schedule.

[0047] When an emergency order event occurs, the set of unprocessed processes is updated, and the highest priority is assigned to all steel plates in the emergency order. The updated unprocessed processes are used as scheduling tasks of the genetic algorithm, and the genetic algorithm is called to re-schedule production.

[0048] When the loading sequence is manually intervened, priorities are assigned to all workpieces to be processed in the order according to the manually set loading sequence, and the production tasks after the loading priorities are set are input as scheduling tasks. The equipment allocation strategy and transfer equipment transfer scheduling strategy are called according to the manually set loading sequence to generate processing equipment allocation and transfer equipment scheduling plans.

[0049] In a second aspect, the present application further provides a computer storage medium storing an executable program code; the executable program code is used to execute the dynamic production scheduling method for a workshop as described in any one of the first aspects.

[0050] In a third aspect, the present application also provides a terminal device, including a memory and a processor; the memory stores program code that can be executed by the processor; the program code is used to execute the dynamic production scheduling method for a workshop as described in any one of the first aspects.

[0051] The present invention provides a workshop dynamic production scheduling method, device and equipment, which constructs a process time matrix and a transfer time matrix by acquiring the processing equipment information, transfer equipment information and workpiece information to be processed in the workshop; the workpiece information to be processed includes the workpiece number to be processed, and then constructs a flexible workshop production scheduling optimization model according to the workshop processing equipment information, transfer equipment information, workpiece information to be processed and the process time matrix and the transfer time matrix, including: an objective function that maximizes the average utilization rate of the equipment and corresponding constraints, so that the optimal or approximately optimal production scheduling plan can be calculated, and then according to the preset random strategy, the shortest processing time The optimization strategy and the longest processing time optimization strategy are to sort the numbers of the workpieces to be processed to obtain several individuals to construct the initial population, select several individuals from the initial population, cross their coding sequences in pairs, obtain the crossover population, perform mutation operations on the individuals in the crossover population, obtain the mutated population, calculate the fitness values ​​of the individuals in the crossover population and the mutated population according to the preset target evaluation function, and retain the updated population according to the roulette mechanism, and then design the preset scheduling plan according to the flexible workshop production scheduling optimization model, and execute the task based on the preset scheduling plan until the workpiece to be processed is completed. It solves the problems of long calculation time in the existing technology and difficulty in adapting to rapid changes in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The present invention is a flowchart of a dynamic production scheduling method for a workshop according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] It should be noted that if the embodiments of the present invention involve directional indications, such as up, down, left, right, front, back, etc., then the directional indication is only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly. In addition, if the embodiments of the present invention involve descriptions of "first, second", "S1, S2", "step one, step two", etc., then such descriptions are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of indicated technical features or indicating the execution order of the method, etc. Those skilled in the art can understand that anything that does not violate the gist of the invention under the technical concept of the invention should be included in the protection scope of the present invention.

[0055] like Figure 1 As shown, the present invention provides a dynamic production scheduling method for a workshop:

[0056] S1: Obtain the processing equipment information, transfer equipment information and workpiece information to be processed in the workshop, and construct the process time matrix and transfer time matrix;

[0057] Specifically, the processing equipment information may optionally include the number, location, distance between each processing equipment, processing speed and processing steps of the processing equipment; the transfer equipment information may optionally include the number, transfer speed and acceleration of the transfer equipment; the workpiece information to be processed may optionally include the size, quantity and number of each workpiece to be processed.

[0058] S11: The steps of constructing a process time matrix are as follows: determining each workstation to be processed according to the process flow of the workpiece to be processed, and combining the processing speed of each processing equipment in the workstation to obtain the processing time of the workpiece to be processed on each processing equipment in each workstation, so as to construct a process time matrix, including: the workpiece number and the corresponding workstation time and the total processing time.

[0059] For example, taking the steel plate as the workpiece to be processed, the optional positions include cutting, drilling, and the other three positions. Taking cutting and drilling as examples, it is optional but not limited to obtaining the length of each steel plate, the processing method of the processing equipment, and the corresponding processing speed, so as to construct the process time matrix according to formula 1-1:

[0060] T c =L / V+T p 1-1

[0061] T d =N×t+T p 1-2

[0062] Among them, T c is the workpiece cutting time, L is the workpiece length, V is the cutting speed, T p is the processing preparation time, T d is the workpiece drilling time, N is the number of workpiece holes, and t is the single hole processing time. Thus, the time required for each processing equipment to process the workpiece to be processed is calculated to construct a process time matrix. The process time matrix can optionally include the workpiece number and the corresponding station time and total processing time, as shown in Table 1:

[0063] Table 1: Process time matrix

[0064] Steel plate number Station 1 (cutting) Station 2 (drilling) Station 3 (Other) Total processing time Steel Plate 1 Tc1 Td1 To1 Ttotal1 Steel Plate 2 Tc2 Td2 To2 Ttotal2 Steel Plate 3 Tc3 Td3 To3 Ttotal3

[0065] S12: The step of constructing a transfer time matrix, specifically, obtaining the transfer time between each station according to the distance between each processing equipment, and the running speed and acceleration of the transfer equipment, so as to construct the transfer time matrix;

[0066] For example, the distance between each processing equipment, as well as equipment parameters such as the operating speed and acceleration of the transfer equipment are obtained to construct a transfer time matrix according to equations 1-3, 1-4, and 1-5:

[0067] T a =(V f -V i ) / a 1-3

[0068] T c =D / V f 1-4

[0069] T d =(V f -V i ) / b 1-5

[0070] Among them, T a is the acceleration phase time, V f is the final speed of the transfer equipment, V i is the initial velocity of the transfer equipment, a is the acceleration rate, T c is the uniform speed stage time, D is the distance between processing equipment, T d is the deceleration stage time, and b is the deceleration rate. Thus, the transit time between each station is calculated to construct the transit time matrix.

[0071] For example, assuming there are n processing equipment, we can construct an n×n transit time matrix T[n][n], where T[i][j] represents the transit time from the i-th processing equipment to the j-th processing equipment, <i≤n,0<j≤n。

[0072] S2: Based on the processing equipment information, transfer equipment information, workpiece information to be processed, process time matrix and transfer time matrix of the workshop, a flexible workshop production scheduling optimization model is constructed, including: the objective function that maximizes the average utilization rate of the equipment and the corresponding constraints;

[0073] Specifically, it is optional but not limited to constructing a flexible workshop production scheduling optimization model based on the processing equipment information, transfer equipment information, workpiece information to be processed, process time matrix and transfer time matrix of the workshop obtained in step S1, and defining decision variables and intermediate variables to construct an objective function and corresponding constraints that maximize the average equipment utilization.

[0074] Preferably, step S2 may optionally include but is not limited to:

[0075] S21: Obtain the processing equipment information, transfer equipment information and workpiece information to be processed in the workshop, and build an initial model for flexible workshop production scheduling;

[0076] Specifically, it is optional but not limited to obtaining the processing equipment information of the workshop, including the number of processing equipment, the type of processing equipment and the processing procedures of various types of processing equipment; the transfer equipment information, including the number of transfer equipment and the transfer time; the workpiece information to be processed, including the number of workpieces to be processed, etc., as well as the process time matrix and transfer time matrix constructed in step S1, to construct an initial model for flexible workshop production scheduling.

[0077] For example, the initial model of the flexible workshop production scheduling is:

[0078] The workshop includes n workpieces to be processed i = {i1, i2, i3, ..., in}; p processable processes k = {k1, k2, k3, ..., kp}, each process has several processing equipment with corresponding processing functions; m processing equipment j = {j1, j2, j3, ..., jm}, each processing equipment is responsible for processing one process of the workpiece to be processed; u transfer equipment V = {V1, V2, V3, ..., Vu}, responsible for the transfer task of the workpiece to be processed between various processing equipment; M k is the set of equipment that can process process k; T ij is the processing time of steel plate i in equipment j; is the transit time of the transverse vehicle v between equipment j and j'.

[0079] S22: Define decision variables and intermediate variables to construct the objective function and constraints that maximize the average equipment utilization and obtain a flexible workshop production scheduling optimization model.

[0080] Specific, optional but not limited to defining decision variables specifically: x ijk ∈{0,1},x ijk =1 means that the kth process of steel plate i is processed on equipment j; ijv ∈{0,1},y ijv =1 means that after the steel plate i is processed on the equipment j, it is transferred by the transverse vehicle v. The intermediate variables are defined as follows: ikj represents the start time of processing step k of steel plate i in equipment j; e ikj represents the processing completion time of process k of steel plate i in equipment j; a jv Indicates the time when the transverse vehicle v arrives at the equipment j station; l jv represents the time when the transverse vehicle v leaves the equipment j station; T max represents the maximum completion time; T j represents the total processing time of equipment j.

[0081] Thus, the objective function 2-1 is constructed to maximize the average utilization of the equipment:

[0082]

[0083] At the same time, constraint 2-2 is established so that each workpiece to be processed can only be processed on one processing device at a time:

[0084]

[0085] Establish constraints 2-3 so that each processing equipment can only process one workpiece at a time:

[0086]

[0087] Establish constraints 2-4 so that each transfer device can only perform the same transfer task at the same time:

[0088]

[0089] Constraints 2-5 are established so that before each process of each workpiece to be processed begins, the previous process of the process must have been completed.

[0090]

[0091] S3: According to the preset random strategy, the shortest processing time optimization strategy and the longest processing time optimization strategy, the numbers of the workpieces to be processed are sorted to obtain a number of individuals to construct an initial population;

[0092] Specifically, it is optional but not limited to using a preset random strategy, a shortest processing time optimization strategy and a longest processing time optimization strategy to sort the numbers of all workpieces to be processed, so as to obtain several groups of codes consisting of the numbers of the workpieces to be processed, each group of codes is an individual, and all the individuals obtained constitute the initial population.

[0093] Preferably, the step of constructing the initial population includes:

[0094] S31: Randomly sort the numbers of the workpieces to be processed to obtain a number of individuals;

[0095] S32: Arrange the numbers of the workpieces to be processed in order from shortest to longest according to the total processing time to obtain a plurality of individuals;

[0096] S33: Arrange the numbers of the workpieces to be processed in order from longest to shortest according to the total processing time to obtain a plurality of individuals;

[0097] S34: Construct the initial population with all individuals.

[0098] Specifically, it is optional but not limited to adopting a random strategy to sort the numbers of the workpieces to be processed and constructing a number of individuals, that is, the loading order of the workpieces to be processed is random; adopting the shortest processing time optimization strategy to sort the numbers of the workpieces to be processed and constructing a number of individuals, that is, arranging the workpieces to be processed in order from short to long according to the total processing time; adopting the longest processing time optimization strategy to sort the numbers of the workpieces to be processed and constructing a number of individuals, that is, arranging the workpieces to be processed in order from long to short according to the total processing time, the arrangement order of the corresponding numbers of the workpieces to be processed in the individuals is their codes, and all constructed individuals are the initial population.

[0099] Preferably, the ratio of individuals constructed according to the random strategy, the shortest processing time optimization strategy and the longest processing time optimization strategy is 4:3:3.

[0100] For example, suppose there are 5 steel plates, numbered 1 to 5, and the processing times are 1, 2, 3, 3, and 4 minutes respectively.

[0101] Random strategy: Generate 4 random loading orders and obtain 4 individuals.

[0102] Shortest processing time optimization strategy: Generate 3 loading sequences with the shortest processing time as the priority, such as:

[0103] Individual 1: [1,2,3,4,5] (sorted completely by shortest processing time);

[0104] Individual 2: [2,1,3,4,5] (slightly shuffle the order of individual 1);

[0105] Individual 3: [1,3,2,4,5] (further disrupt the order of individual 2 to obtain new individuals).

[0106] Longest processing time optimization strategy: Generate 3 loading sequences with the longest processing time first, such as:

[0107] Individual 1: [5, 4, 3, 2, 1] (sorted completely by longest processing time);

[0108] Individual 2: [4,5,3,2,1] (slightly shuffle the order of individual 1);

[0109] Individual 3: [5,3,4,2,1] (further disrupt the order of individual 2 to obtain a new individual).

[0110] S4: Select a number of individuals from the initial population, cross their coding sequences in pairs, and obtain a population after crossover;

[0111] Specifically, it is optional but not limited to selecting two individuals from the initial population, then crossing their coding sequences two by two to obtain two new individuals, repeating the above steps until the number selected reaches a preset number threshold, and then obtaining a population after crossing.

[0112] Preferably, the specific steps of S4 may include, but are not limited to:

[0113] S41: Take the initial population as the current population;

[0114] S42: Select any two individuals from the current population, calculate the crossover probability between the two individuals, and determine whether it is greater than a set threshold. If so, cross the encoding sequences of the two individuals to obtain an updated population. If not, keep the population unchanged.

[0115] Specifically, it is optional but not limited to calculating the maximum fitness, average fitness and minimum fitness of the current population according to the fitness evaluation method, and then selecting two individuals from the current population, calculating the crossover probability at this time according to the crossover probability calculation formula, and judging whether it is greater than the set threshold. If so, the two coding sequences are crossed, if not, they remain unchanged.

[0116] Preferably, the crossover probability calculation formula may be as shown in Formula 4-1:

[0117]

[0118] Among them, p c is the crossover probability, and Respectively represent the maximum and minimum crossover probability, obj max 、obj avg 、obj min They represent the maximum fitness, average fitness and minimum fitness in the current population respectively.

[0119] S43: Determine whether the number of selected individuals is greater than the set number threshold. If so, take the updated population as the population after crossover. If not, take the updated population as the current population and return to step S42.

[0120] Preferably, the quantity threshold is set to the number of individuals in the initial population.

[0121] S5: Perform mutation operation on the individuals in the population after crossover to obtain the mutated population;

[0122] Specifically, it is optional but not limited to sequentially selecting individuals in the population after crossover, and calculating the current mutation probability according to the mutation probability calculation formula to determine whether it is less than the set mutation probability. If so, any two sequence values ​​in the individual coding sequence are exchanged, if not, it remains unchanged.

[0123] Preferably, the mutation probability calculation formula can be optionally expressed as Formula 5-1:

[0124]

[0125] Among them, p m is the current mutation probability, and Respectively represent the preset maximum and minimum mutation probabilities, obj max 、obj avg 、obj min They represent the maximum fitness, average fitness and minimum fitness in the current population respectively.

[0126] S6: Calculate the fitness values ​​of individuals in the population after crossover and the population after mutation according to a preset target evaluation function, so as to retain the updated population according to the roulette mechanism;

[0127] Specifically, it is optional but not limited to pre-building a target evaluation function to evaluate the fitness of each individual, so as to decide which individuals to retain based on the fitness of each individual to construct the next generation population. The higher the fitness value, the better the performance of the individual scheduling plan, and the higher the probability of being selected to form the next generation population.

[0128] Preferably, the objective evaluation function can be optionally expressed as the average equipment utilization of the current individual scheduling scheme, such as formula 6-1:

[0129]

[0130] in, represents the maximum completion time of the scheduling plan of individual i, represents the total processing time of equipment j in the scheduling plan of individual i, and m represents the number of equipment.

[0131] S7: Design a preset scheduling plan according to the flexible workshop production scheduling optimization model, and execute tasks based on the preset scheduling plan until the workpiece to be processed is completed.

[0132] Specifically, it is optional but not limited to designing a preset scheduling plan based on the flexible workshop production scheduling optimization model, which may include preset scheduling plans such as the loading sequence plan of the workpieces to be processed, the processing equipment allocation plan and the transfer equipment scheduling plan, and then processing the workpieces to be processed according to the preset scheduling plan until all the workpieces to be processed are processed.

[0133] The preset scheduling plan executes the task, processes the workpiece to be processed, and continuously determines whether an abnormal situation occurs during the execution of the task. If no abnormal situation occurs, the task is continuously executed according to the preset scheduling plan until the workpiece to be processed is completed. If an abnormal situation occurs, it is necessary to determine the corresponding processing method and correction strategy according to the type of abnormal situation.

[0134] Preferably, since, when the workpiece to be processed is processed, due to factors such as environmental influences or process conditions, abnormal situations may occur during the process of processing the workpiece to be processed according to the preset scheduling scheme, when step S7 executes the task based on the preset scheduling scheme, the method further includes:

[0135] S8: Determine whether an abnormal situation occurs; if no abnormal situation occurs and the workpiece to be processed is completed, the method ends; if no abnormal situation occurs and the workpiece to be processed is not completed, continue to execute the task based on the preset scheduling plan, if an abnormal situation occurs, execute step S9;

[0136] S9: According to the type of abnormal situation, select the processing method and correction strategy corresponding to the type of abnormal situation. Based on the processing method and correction strategy, obtain the respective positions and earliest available time of the processing equipment in the current workshop, the respective positions and earliest available time of the transfer equipment, the size, quantity and number of the workpieces to be processed that have not yet started processing, and the respective positions of the workpieces that have not completed all the processing procedures. This is the new information of the workpieces to be processed and the new initial state of the workshop. Reconstruct the initial population with the new information of the workpieces to be processed and the new initial state of the workshop, and enter step S2.

[0137] Preferably, the processing methods and correction strategies corresponding to the abnormal situation types include:

[0138] S81: When a processing equipment fails, the processes in the processing process window and the unprocessed process window affected by the failure are completely rescheduled, and then the faulty equipment is deleted from the optional machine set, and the processing process and the unprocessed process affected by the failure are used as scheduling task inputs of the genetic algorithm, and the genetic algorithm is called to re-optimize the production schedule.

[0139] S82: When an emergency order event occurs, the unprocessed process set is updated, and the highest priority is assigned to all steel plates in the emergency order. The updated unprocessed process is used as a scheduling task input of the genetic algorithm, and the genetic algorithm is called to re-schedule production.

[0140] S83: After manual intervention in the loading sequence, priorities are assigned to all workpieces to be processed in the order according to the manually set loading sequence, and the production tasks after setting the loading priorities are input as scheduling tasks. The equipment allocation strategy and transfer equipment transfer scheduling strategy are called according to the manually set loading sequence to generate processing equipment allocation and transfer equipment scheduling plans.

[0141] In this embodiment, a dynamic production scheduling method for a workshop of the present invention is provided. By acquiring the processing equipment information, transfer equipment information and workpiece information to be processed in the workshop, a process time matrix and a transfer time matrix are constructed; the workpiece information to be processed includes the workpiece number to be processed, and then according to the processing equipment information, transfer equipment information, workpiece information to be processed in the workshop and the process time matrix and the transfer time matrix, a flexible workshop production scheduling optimization model is constructed, including: an objective function that maximizes the average utilization rate of the equipment and corresponding constraints, so that the optimal or approximately optimal production scheduling plan can be calculated, and then according to the preset random strategy, the shortest processing time, the optimal production scheduling plan can be obtained. The optimization strategy for the longest processing time and the optimization strategy for the longest processing time are used. The numbers of the workpieces to be processed are sorted to obtain several individuals to construct the initial population. Several individuals are selected from the initial population, and their coding sequences are crossed in pairs to obtain the crossed population. The individuals in the crossed population are mutated to obtain the mutated population. The fitness values ​​of the individuals in the crossed population and the mutated population are calculated according to the preset target evaluation function to retain the updated population according to the roulette mechanism. Then, the preset scheduling plan can be designed according to the flexible workshop production scheduling optimization model, and the task is executed based on the preset scheduling plan until the workpiece to be processed is completed. The problem of long calculation time and difficulty in adapting to rapid changes in the production process in the existing technology is solved.

[0142] On the other hand, the present invention also provides a computer storage medium storing executable program code; the executable program code is used to execute the dynamic production scheduling method for any of the above-mentioned workshops.

[0143] On the other hand, the present invention also provides a terminal device, including a memory and a processor; the memory stores program codes that can be executed by the processor; the program codes are used to execute the dynamic production scheduling method of any of the above-mentioned workshops.

[0144] Exemplarily, the program code may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the program code in the terminal device.

[0145] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the terminal device may also include an input / output device, a network access device, a bus, etc.

[0146] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0147] The memory may be an internal storage unit of the terminal device, such as a hard disk or a memory. The memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device. Further, the memory may also include both an internal storage unit of the terminal device and an external storage device. The memory is used to store the program code and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.

[0148] The above-mentioned computer storage medium and terminal equipment are created based on the dynamic production scheduling method of the above-mentioned workshop. Its technical role and beneficial effects are not repeated here. The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the various technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present invention, several deformations and improvements can be made, which all belong to the protection scope of the present invention. Therefore, the scope of protection of the patent of the present invention shall be based on the attached claims.

Claims

1. A dynamic production scheduling method for a workshop, characterized in that: include: Obtain the processing equipment information, transfer equipment information and workpiece information to be processed in the workshop, and build the process time matrix and transfer time matrix; Information of the workpiece to be processed, including the number of the workpiece to be processed; According to the workshop's processing equipment information, transfer equipment information, workpiece information to be processed, process time matrix and transfer time matrix, a flexible workshop production scheduling optimization model is constructed, including: the objective function that maximizes the average utilization of equipment and the corresponding constraints; According to the preset random strategy, the shortest processing time optimization strategy and the longest processing time optimization strategy, the numbers of the workpieces to be processed are sorted to obtain a number of individuals to construct an initial population; Select several individuals from the initial population, cross their coding sequences two by two, and obtain the population after the crossover; Perform mutation operation on the individuals in the population after crossover to obtain the mutated population; The fitness values ​​of individuals in the population after crossover and the population after mutation are calculated according to the preset target evaluation function, so as to retain the updated population according to the roulette mechanism; Design a preset scheduling plan based on the flexible workshop production scheduling optimization model, and execute tasks based on the preset scheduling plan until the workpiece to be processed is completed.

2. The method according to claim 1, characterized in that The steps to construct the process time matrix are as follows: According to the process flow of the workpiece to be processed, the workstations that need to be processed are determined, and the processing speed of each processing equipment in the workstation is combined to obtain the processing time of the workpiece to be processed on each processing equipment in each workstation, so as to construct a process time matrix, including: the workpiece number and the corresponding workstation time and the total processing time.

3. The method according to claim 1, characterized in that The steps to construct the transit time matrix are as follows: According to the distance between each processing equipment, as well as the operating speed and acceleration of the transfer equipment, the transfer time between each workstation is obtained to construct a transfer time matrix.

4. The method according to claim 3, characterized in that The steps of constructing the transit time matrix also include: Obtain the distance between each processing equipment, as well as the operating speed and acceleration of the transfer equipment, to construct the transfer time matrix according to equations 1-3, 1-4, and 1-5: T a =(V f -V i ) / a 1-3 T c =D / V f 1-4 T d =(V f -V i ) / b 1-5 Among them, T a is the acceleration phase time, V f is the final speed of the transfer equipment, V i is the initial velocity of the transfer equipment, a is the acceleration rate, T c is the uniform speed stage time, D is the distance between processing equipment, T d is the deceleration stage time, b is the deceleration rate, according to T a、 T c and T d The transit time between each workstation is calculated to construct a transit time matrix.

5. The method according to claim 1, characterized in that The steps to build a flexible workshop production scheduling optimization model include: Obtain the information of the workshop's processing equipment, transfer equipment, and workpieces to be processed, and build an initial model for flexible workshop production scheduling; Decision variables and intermediate variables are defined to construct the objective function and constraints that maximize the average equipment utilization, and obtain the flexible workshop production scheduling optimization model.

6. The method according to claim 5, characterized in that The objective function and constraints are as follows: Objective function formula 2-1: Constraints 2-2, 2-3, 2-4, 2-5: The decision variables are: ijk ∈{0,1}, indicating that the kth process of steel plate i is processed on equipment j, y ijv ∈{0,1}, indicating that the steel plate i is transferred by the transverse vehicle v after being processed on the equipment j. The specific intermediate variables are: s ikj represents the start time of processing k of steel plate i in equipment j, e ikj represents the processing completion time of process k of steel plate i in equipment j, a jv It indicates the time when the transverse vehicle v arrives at the equipment j station, l jv represents the time when the transverse vehicle v leaves the equipment j station, T max represents the maximum completion time, T j represents the total processing time of equipment j.

7. The method according to claim 1, characterized in that The steps to construct the initial population include: Randomly sort the numbers of the workpieces to be processed to obtain a number of individuals; Arrange the numbers of the workpieces to be processed in order from shortest to longest according to the total processing time to obtain a number of individuals; Arrange the numbers of the workpieces to be processed in order from longest to shortest according to the total processing time to obtain a number of individuals; Construct the initial population with all individuals.

8. The method according to claim 1, characterized in that: The steps to obtain the population after crossover include: S41: Take the initial population as the current population; S42: Select any two individuals from the current population, calculate the crossover probability between the two individuals, and determine whether it is greater than a set threshold. If so, cross the encoding sequences of the two individuals to obtain an updated population. If not, keep the population unchanged. S43: Determine whether the number of selected individuals is greater than the set number threshold. If so, take the updated population as the population after crossover. If not, take the updated population as the current population and return to step S42.

9. The method according to claim 1, characterized in that: The step of executing the task based on the preset scheduling scheme also includes Determine whether an abnormal situation occurs; if no abnormal situation occurs and the workpiece to be processed is completed, the method ends; if no abnormal situation occurs and the workpiece to be processed is not completed, continue to execute the task based on the preset scheduling plan, if an abnormal situation occurs, then: According to the type of abnormal situation, the processing method and correction strategy corresponding to the type of abnormal situation are selected. Based on the processing method and correction strategy, the respective positions and earliest available time of the processing equipment in the current workshop, the respective positions and earliest available time of the transfer equipment, the size, quantity and number of the workpieces to be processed that have not yet started processing, and the respective positions of the workpieces that have not completed all the processing procedures are obtained. This is the new information of the workpieces to be processed and the new initial state of the workshop. The initial population is reconstructed with the new information of the workpieces to be processed and the new initial state of the workshop, and the process goes to step S2.

10. The method according to claim 9, characterized in that The specific processing methods and correction strategies are as follows: When a processing equipment fails, the processes in the processing process window and the unprocessed process window affected by the failure are completely rescheduled, and then the faulty equipment is deleted from the optional machine set. The processing processes and the unprocessed processes affected by the failure are used as scheduling task inputs of the genetic algorithm, and the genetic algorithm is called to re-optimize the production schedule. When an emergency order event occurs, the set of unprocessed processes is updated, and the highest priority is assigned to all steel plates in the emergency order. The updated unprocessed processes are used as scheduling tasks of the genetic algorithm, and the genetic algorithm is called to re-schedule production. When the loading sequence is manually intervened, priorities are assigned to all workpieces to be processed in the order according to the manually set loading sequence, and the production tasks after the loading priorities are set are input as scheduling tasks. The equipment allocation strategy and transfer equipment transfer scheduling strategy are called according to the manually set loading sequence to generate processing equipment allocation and transfer equipment scheduling plans.

11. A computer storage medium, characterized in that: An executable program code is stored; the executable program code is used to execute the dynamic production scheduling method for a workshop as described in any one of claims 1-10.

12. A terminal device, characterized in that: It comprises a memory and a processor; the memory stores program code executable by the processor; the program code is used to execute the dynamic production scheduling method for a workshop as described in any one of claims 1-10.

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