Method, device, storage medium and processor for determining production scheduling

Abstract

This application provides a method, apparatus, storage medium, and processor for determining production scheduling plans. The method includes: acquiring pending orders, each pending order including multiple parts to be produced and production processes for each part; determining multiple candidate production scheduling groups for all pending orders based on the production processes of all parts to be produced and the corresponding work equipment for each production process, each candidate production scheduling group including production parameters corresponding to each pending order; for each candidate production scheduling group, determining scheduling plan parameters for the candidate production scheduling group based on preset scheduling targets and production parameters; inputting the scheduling plan parameters of each candidate production scheduling group into a fitness function to determine the fitness of each candidate production scheduling group; and determining a target production scheduling group from the multiple candidate production scheduling groups based on the fitness, so as to produce all pending orders according to the target production scheduling group, thereby improving the efficiency of determining production scheduling plans and the production benefits of pending orders.

Description

Technical Field

[0001] This application relates to the field of industrial manufacturing, and more specifically to a method, apparatus, storage medium, and processor for determining production scheduling plans. Background Technology

[0002] In discrete manufacturing, the production sequence of products on various machines is flexible and varied, making production scheduling more complex. Therefore, when developing corresponding production scheduling plans for production orders based on different scheduling objectives, it is necessary to consider issues such as the priority relationship between processes, the capacity constraints of manufacturing equipment, order priority, and resource constraints.

[0003] Currently, manual production scheduling in discrete manufacturing fails to coordinate overall resource utilization efficiency, leading to uneven equipment capacity allocation and unnecessary resource waste, and hindering the improvement of production scheduling efficiency. Furthermore, when order volumes increase, manual scheduling requires cumbersome operations, increasing both time and labor costs, making it difficult to rationally schedule production for each component of an order, and reducing the production efficiency and effectiveness of each pending order. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, storage medium, and processor for determining production scheduling plans.

[0005] To achieve the above objectives, the first aspect of this application provides a method for determining a production schedule, comprising:

[0006] Obtain pending orders, each of which includes multiple parts to be produced and the production process for each part;

[0007] Based on the production processes of all components to be produced and the work equipment corresponding to each production process, multiple candidate production scheduling groups are determined for all orders to be processed. Each candidate production scheduling group includes production parameters corresponding to each order to be processed, wherein the production parameters include the execution order of the production processes of each order to be produced and the work equipment corresponding to each production process.

[0008] For each candidate production group, determine the production plan parameters of the candidate production group based on the preset production targets and production parameters;

[0009] The production planning parameters of each candidate production group are input into the fitness function to determine the fitness of each candidate production group.

[0010] The target production group is determined from multiple candidate production groups based on fitness, and production is carried out for all pending orders based on the target production group.

[0011] A second aspect of this application provides an apparatus for determining a production schedule, comprising:

[0012] The order acquisition module is used to acquire orders to be processed. Each order to be processed includes multiple parts to be produced and the production process for each part.

[0013] The first production scheduling module is used to determine multiple candidate production scheduling groups for all pending orders based on the production processes of all parts to be produced and the work equipment corresponding to each production process. Each candidate production scheduling group includes production parameters corresponding to each pending order, wherein the production parameters include the execution order of the production processes of each pending order and the work equipment corresponding to each production process.

[0014] The production scheduling parameter determination module is used to determine the production scheduling parameters for each candidate production group based on preset production targets and production parameters.

[0015] The fitness determination module is used to input the production planning parameters of each candidate production group into the fitness function to determine the fitness of each candidate production group.

[0016] The second scheduling module is used to determine the target scheduling group from multiple candidate scheduling groups based on fitness, so as to produce all pending orders according to the target scheduling group.

[0017] A third aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned method for determining a production schedule.

[0018] A fourth aspect of this application provides a processor configured to perform the above-described method for determining a production schedule.

[0019] The above technical solution can determine the production plan parameters of the candidate production groups based on the preset production targets and production parameters, and determine the fitness of each candidate production group based on the production plan parameters. Then, the target production group can be determined based on the fitness. No manual intervention is required for production scheduling, which reduces the labor and time costs required for production scheduling, greatly improves the efficiency of determining the production plan, and can reasonably schedule each part to be produced in the order to be processed, which greatly improves the production efficiency and effectiveness of the order to be processed.

[0020] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0022] Figure 1 The illustration shows a flowchart of a method for determining a production schedule according to an embodiment of this application;

[0023] Figure 2 An example diagram illustrating time conversion according to an embodiment of this application is shown schematically;

[0024] Figure 3 An example diagram illustrating a first set to be cross-processed according to an embodiment of this application is shown schematically;

[0025] Figure 4 An example diagram illustrating a second set to be mutated according to an embodiment of this application is shown schematically;

[0026] Figure 5 An example diagram of a production schedule according to an embodiment of this application is shown schematically;

[0027] Figure 6 The illustration shows a flowchart of a method for determining a production schedule according to yet another embodiment of this application;

[0028] Figure 7 This schematic diagram illustrates a structural block diagram of an apparatus for determining a production schedule according to an embodiment of the present application;

[0029] Figure 8 This schematic diagram illustrates the structural block diagram of the production scheduling parameter determination module according to an embodiment of this application;

[0030] Figure 9 The diagram schematically illustrates a structural block diagram of an apparatus for determining a production schedule according to yet another embodiment of this application;

[0031] Figure 10 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0033] Figure 1 A schematic flowchart illustrating a method for determining a production schedule according to an embodiment of this application is shown. Figure 1As shown in one embodiment of this application, a method for determining a production schedule is provided, comprising the following steps:

[0034] Step 101: Obtain pending orders. Each pending order includes multiple parts to be produced and the production process for each part.

[0035] Step 102: Determine multiple candidate production scheduling groups for all pending orders based on the production processes of all parts to be produced and the work equipment corresponding to each production process. Each candidate production scheduling group includes production parameters corresponding to each pending order. The production parameters include the execution order of the production processes for each pending order and the work equipment corresponding to each production process.

[0036] Step 103: For each candidate production group, determine the production plan parameters of the candidate production group based on the preset production target and production parameters.

[0037] Step 104: Input the production planning parameters of each candidate production group into the fitness function to determine the fitness of each candidate production group.

[0038] Step 105: Determine the target production group from multiple candidate production groups based on fitness, and produce all pending orders according to the target production group.

[0039] Production scheduling refers to assigning execution equipment to each process according to the technological route of the ordered products, and arranging the execution sequence of the processes under the constraints of the process. When determining the production schedule, the processor can first obtain pending orders. A pending order can include multiple orders. Each pending order includes multiple parts to be produced and production processes for each part. For each part to be produced, any two production processes can be discontinuous, but the order of all production processes is fixed. For example, for part A to be produced, the corresponding production processes are a1-a2-a3, and for part B to be produced, the corresponding production processes are b1-b2-b3. Therefore, when producing parts A and B, the corresponding production processes can be: a1-b1-b2-a2-b3-a3. It can be seen that when parts A and B are processed simultaneously, the production processes for parts A and B are still performed according to a1-a2-a3 and b1-b2-b3 respectively. However, for a single part to be produced, any two production processes can be discontinuous. For example, after processing production step a1 of component A to be produced, production steps b1 and b2 of component B to be produced can be processed in sequence, and then production step a2 of component A to be produced can be processed.

[0040] Upon receiving pending orders, the processor can determine multiple candidate production scheduling groups for all pending orders based on the production processes of all components to be produced and the corresponding work equipment for each production process. Each candidate production scheduling group includes production parameters corresponding to each pending order, including the execution order of the production processes for each order and the work equipment corresponding to each production process. In one embodiment, the processor can randomly combine all production processes according to the production processes of each component to be produced, and can separately encode each production process and the corresponding work equipment to generate multiple candidate production scheduling groups for all pending orders. Each production process corresponds to one work equipment. This work equipment is selected from multiple optional work equipment corresponding to the production process. Optional work equipment refers to equipment capable of processing the corresponding production process.

[0041] For example, Table 1 shows the execution sequence of production processes for the orders to be produced included in the production scheduling group A. Where, O ij This refers to the j-th production process of the i-th component to be produced. Table 2 shows the equipment corresponding to each production process included in the production scheduling group A. The numbers represent equipment numbers. For example, processing process O... 11 The corresponding optional equipment includes equipment numbered 1, 2, and 3. Table 2 selects optional equipment numbered 1 as the equipment for processing step O. 11 Corresponding work equipment. Having determined the production execution sequence for each order to be produced and the work equipment corresponding to each production step, multiple candidate scheduling groups can be further identified for all orders to be processed. Table 3 shows candidate scheduling group A. Here, JS refers to the execution sequence of the production steps for each order to be produced, and MS refers to the work equipment corresponding to each production step.

[0042] Table 1 shows the execution sequence of production processes for the orders to be produced included in the production scheduling group A.

[0043] 1 1 2 3 1 3 2 3 <![CDATA[O 11 ]]> <![CDATA[O 12 ]]> <![CDATA[O 21 ]]> <![CDATA[O 31 ]]> <![CDATA[O 13 ]]> <![CDATA[O 31 ]]> <![CDATA[O 22 ]]> <![CDATA[O 33 ]]>

[0044] Table 2 shows the equipment corresponding to each production process in the candidate production group A.

[0045] 1 3 4 2 1 5 2 4 1、2、3 3、4 1、4 2、3 1、5 4、5 2、4 3、4、5

[0046] Table 3. Production Group A (to be selected)

[0047]

[0048] After identifying multiple candidate production groups, the processor can determine the production plan parameters for each candidate group based on preset production targets and production parameters. The preset production targets can be set according to the actual needs of each pending order. Once the production plan parameters for each candidate group are determined, the processor can input these parameters into a fitness function to determine the fitness of each candidate group. Fitness reflects the production efficiency after production is carried out according to each production process and its corresponding equipment within each candidate group. The processor can further determine a target production group from the multiple candidate groups based on the fitness, and then produce all pending orders according to the target production group.

[0049] The above technical solution can determine the production plan parameters of the candidate production groups based on the preset production targets and production parameters, and determine the fitness of each candidate production group based on the production plan parameters. Then, the target production group can be determined based on the fitness. No manual intervention is required for production scheduling, which reduces the labor and time costs required for production scheduling, greatly improves the efficiency of determining the production plan, and can reasonably schedule each part to be produced in the order to be processed, which greatly improves the production efficiency and effectiveness of the order to be processed.

[0050] In one embodiment, for each candidate production group, determining the production plan parameters of the candidate production group based on preset production targets and production parameters includes: determining the idle time period of the work equipment corresponding to each production process of each component to be produced in the candidate production group; determining the balanced production parameters of the candidate production group based on the total number of work equipment and the idle time period of each work equipment in the candidate production group; determining the completion time point for completing the last production process in the candidate production group for each candidate production group; determining the manufacturing cycle parameters of each candidate production group based on the completion time point of each candidate production group; determining the completion time difference between pending orders of different order levels in the candidate production group for each candidate production group; determining the production decision parameters for all candidate production groups based on preset production targets; and determining the production plan parameters for each candidate production group based on the production decision parameters, balanced production parameters, manufacturing cycle parameters, and completion time differences.

[0051] For each candidate production scheduling group, the processor can determine the completion time of the last production process in that group and determine the manufacturing cycle parameters of that group based on the completion time. For example, if a candidate production scheduling group includes production processes for parts to be manufactured in two pending orders, and its last process is the last process for a part to be manufactured in one of the pending orders, the manufacturing cycle parameters of the candidate production scheduling group can be determined based on the completion time of the last process of that part. In one embodiment, the manufacturing cycle parameters are determined by formula (3):

[0052] l1 = ends last (3)

[0053] Where l1 refers to the manufacturing cycle parameter, and ends last This refers to the completion time of each candidate production group.

[0054] The processor can determine the idle time period of the work equipment corresponding to each production process of each component to be produced in the candidate production scheduling group. Then, the processor can determine the balanced production parameters for each candidate production scheduling group based on the total number of work equipment in each group and the idle time period of each work equipment. By determining the balanced production parameters, it is possible to avoid a situation where one work equipment in each candidate production scheduling group is fully loaded while another work equipment is idle, which can significantly improve the utilization rate of work equipment.

[0055] In one embodiment, for each candidate production scheduling group, determining the balanced production parameters of the candidate production scheduling group based on the total number of working devices in the candidate production scheduling group and the idle time period of each working device includes: converting the idle time period of each working device according to preset conditions to obtain the idle duration of each working device; determining the average idle duration of all working devices in each candidate production scheduling group; and determining the balanced production parameters based on the total number of working devices, idle duration, and average idle duration of the candidate production scheduling group.

[0056] To avoid frequent changes of parts to be produced during production, the processor can first obtain the capacity information of each piece of equipment and determine its idle time period based on this information. The idle time period can include multiple non-contiguous sub-time periods. After determining the idle time period for each piece of equipment, the processor can transform it according to preset conditions to obtain the idle duration for each piece of equipment. The preset conditions refer to transforming the idle time period according to a preset unit of time. For example, ... Figure 2 As shown, an example diagram of time transformation is presented. Figure 2 The boxes below represent the idle time periods for each operating device, i.e., normal natural time. Figure 2 The upper line segment represents the timeline converted to minutes. If the idle time period for a work equipment is from 8:00 to 16:00 on September 1st, then that time period can be converted to a timeline of 0-480, meaning the idle time can be 480 minutes. By uniformly converting the idle time periods for each work equipment, it becomes easier to determine the suitability of each candidate production scheduling group.

[0057] Specifically, since the capacity status of each work unit in each candidate production group may differ, time conversion can be performed on a per-work unit basis. The time conversion formula for each work unit is: unavailable time ={(start1,end1),…,(start1,end1),…,(start1,end1)} n end n )};available time ={(total-start1,total-end1),…,(total*n-start n ,total*n-end n )}. Among them, unavailable time This refers to the non-idle time period for each operating device. A non-idle time period can include n non-idle sub-time periods, each of which includes a start time and an end time. For example, (start1, end1) refers to the first non-idle sub-time period, where start1 is the start time and end1 is the end time. time This refers to the idle time period of each piece of equipment, which can include n idle sub-time periods. `total` refers to the effective production time for each production day.

[0058] Having determined the idle time of each work unit in each production scheduling group, the processor can further determine the average idle time of all work units in each candidate scheduling group. Then, the processor can determine the balanced production parameters of the candidate scheduling group based on the total number of work units in the candidate scheduling group, the idle time of each work unit, and the average idle time of all work units.

[0059] In one embodiment, the equilibrium production parameters are determined by formula (4):

[0060]

[0061] Where l2 refers to the balanced production parameter, n refers to the total number of operating equipment in each candidate production group, and M refers to the average idle time of all operating equipment in each candidate production group.n This refers to the idle time of the nth operating device in each candidate production scheduling group.

[0062] For each candidate production scheduling group, the processor can determine the completion time difference between pending orders of different order levels within that group. In one embodiment, the processor can first determine the order level of each pending order in each candidate production scheduling group. The order level can include a first level and a second level. Pending orders with a first level order can be priority orders. Pending orders with a second level order can be non-priority orders. Based on this, the processor can determine the first total completion time for all pending orders with a first level order and the second total completion time for all pending orders with a second level order. Then, the processor can determine the difference between the first total completion time and the second total completion time, which is the completion time difference.

[0063] In one embodiment, the time difference for completion is determined by formula (5):

[0064]

[0065] Where l3 refers to the completion time difference, N refers to the total number of all pending orders at order level 1, L refers to the total number of all pending orders at order level 2, and order i This refers to the completion time of the i-th pending order with a first-level order status. other This refers to the completion time of the pending order with the second order level.

[0066] The processor can first obtain preset production scheduling targets and then determine the scheduling decision parameters for all candidate production groups based on these targets. These scheduling decision parameters are those that can influence the production schedule. Multiple scheduling decision parameters can be included. For example, they may include decision parameters related to balanced production, manufacturing cycle time, and order priority. The processor can determine the scheduling plan parameters for each candidate production group based on the scheduling decision parameters, balanced production parameters, manufacturing cycle time parameters, and completion time differences. For example, if one of the preset production scheduling targets is to ensure balanced production processes, the processor can set the decision parameter related to balanced production to 1. Therefore, it can determine the scheduling plan parameters for balanced production for each candidate production group based on this decision parameter and the balanced production parameters.

[0067] In one embodiment, the fitness of each candidate production group is determined by formula (6):

[0068]

[0069] Where f refers to the fitness of each candidate production group, r1, r2 and r3 refer to different production scheduling decision parameters, r1, r2 and r3 are all 0 or 1, l1 refers to the manufacturing cycle parameter of each candidate production group, l2 refers to the equilibrium production parameter of each candidate production group, and l3 refers to the completion time difference.

[0070] In one embodiment, determining the target production group from multiple candidate production groups based on fitness includes: forming a first set by any two candidate production groups; performing crossover processing on the first candidate production groups in the first set to obtain a second set; performing mutation processing on the second candidate production groups in the second set to obtain a third set; determining the fitness of the third candidate production groups in each third set; and determining the third candidate production group with the highest fitness in the third set as the target production group.

[0071] When determining the target production group from multiple candidate production groups based on fitness, the processor can first randomly select two candidate production groups and designate both as first candidate production groups, thus combining the two first candidate production groups to form a corresponding first set. Next, the processor can perform cross-processing on the first candidate production groups in the first set, and designate both cross-processed first candidate production groups as second candidate production groups, thus combining the two second candidate production groups to obtain a second set. After determining the second set, the processor can perform mutation processing on the second candidate production groups in the second set, and designate both mutated second candidate production groups as third candidate production groups, thus combining the two third candidate production groups to obtain a third set. The processor can determine the fitness of the third candidate production groups in each third set and designate the third candidate production group with the highest fitness in the third set as the target production group, so that all pending orders can be produced according to the target production group.

[0072] In one embodiment, cross-processing the first candidate production groups in the first set to obtain the second set includes: determining the maximum fitness and the average fitness of all candidate production groups; determining the cross-processing probability of the first candidate production groups in the first set based on the maximum fitness, the average fitness, and the average fitness of the first candidate production groups in the first set; and for any first set, cross-processing the first candidate production groups in the first set according to the cross-processing probability to obtain the second set.

[0073] The processor can determine the maximum and average fitness of all candidate production groups. For any given first set, the processor can determine the maximum fitness of the first candidate production group within that first set. Then, the processor can determine the crossover probability of the first candidate production group in the first set based on the maximum fitness of all candidate production groups, the maximum fitness of the first candidate production group in the first set, and the average fitness of all candidate production groups. The crossover probability reflects the proportion of production processes to be crossovered in the first set; a higher crossover probability allows for a larger global search. For any first set, the processor can perform crossover processing on the first candidate production groups in the first set according to the crossover probability of that first set to obtain a second set.

[0074] In one embodiment, for any first set, performing cross-processing on the first candidate production scheduling groups in the first set according to the cross-processing probability includes: for any first candidate production scheduling group in the first set, determining the segment to be crossed in the first candidate production scheduling group according to the cross-processing probability, the segment to be crossed includes a process segment and an equipment segment, the process segment includes at least one production process, and the equipment segment includes the operating equipment corresponding to each production process in the process segment; traversing another first candidate production scheduling group in the first set to determine a target cross-process segment from the other first candidate production scheduling group, the target cross-process segment including a target process segment with the same process as the segment to be crossed but a different order, and a target equipment segment corresponding to the target process segment; and swapping the segment to be crossed with the target cross-process segment to complete the cross-processing for the first candidate production scheduling group in the first set.

[0075] For any first candidate production scheduling group in the first set, the processor can determine the segments to be crossed within the first candidate production scheduling group based on the crossover probability. These segments include process segments and equipment segments. A process segment includes at least one production process, and an equipment segment includes the work equipment corresponding to each production process within the process segment. Then, the processor can iterate through another first candidate production scheduling group in the first set to determine the target crossover segment from that group. The target crossover segment includes a target process segment with the same processes as the segment to be crossed but in a different order, and a target equipment segment corresponding to the target process segment. The processor can then swap the segment to be crossed with the target crossover segment to complete the crossover processing for the first candidate production scheduling groups in the first set.

[0076] For example, such as Figure 3The diagram shows an example of a first set to be cross-processed. Here, JS refers to a process segment, and MS refers to a device segment. For the segments to be cross-processed {(1,2,3), (3,4,2)}, (1,2,3) refers to a process segment, and (3,4,2) refers to a device segment. In the process segment, 1 refers to the second production process of the first component to be produced, 2 refers to the first production process of the second component to be produced, and 3 refers to the first production process of the third component to be produced. In the device segment, 3 refers to the machine with device number 3 capable of processing the second production process of the first component to be produced, 4 refers to the machine with device number 4 capable of processing the first production process of the second component to be produced, and 2 refers to the machine with device number 2 capable of processing the first production process of the third component to be produced. Traversal... Figure 3 The first candidate production group in the lower middle section has a target process segment (2,3,1) that has the same type of parts to be produced as the target process segment but a different order. That is, this target process segment contains the same processes as the target process segment, but the order of each process is different. The target equipment segment is (4,3,4). The processor can swap process segment (1,2,3) with target process segment (2,3,1) and equipment segment (3,4,2) with target equipment segment (4,3,4) to complete the cross-processing of this first set.

[0077] In one embodiment, the crossover probability is determined by formula (1):

[0078]

[0079] Among them, P c This refers to the crossover probability, f m This refers to the maximum fitness value of all candidate production scheduling groups, f a f' refers to the mean fitness of all candidate production groups, f' refers to the maximum fitness of the first candidate production group in the first set, and α is a constant, α = 0.7.

[0080] In one embodiment, the method further includes: setting an initial value for the number of iterations of the crossover process; incrementing the number of iterations after crossover processing of the first candidate production groups in the first set; determining whether the number of iterations is greater than or equal to a preset value after determining the fitness of the third candidate production groups in each third set; if the number of iterations is less than the preset value, using the third set as a new first set and returning to the step of crossover processing of the first candidate production groups in the first set to obtain a second set, until the number of iterations is greater than or equal to the preset value; if the number of iterations is greater than or equal to the preset value, determining the third candidate production group with the highest fitness in the last obtained third set as the target production group.

[0081] The processor can obtain an initial value for the number of iterations in the crossover process. After crossover processing the first candidate scheduling group in the first set, the iteration count is incremented. The increment value can be 1. After determining the fitness of the third candidate scheduling group in each third set, the processor can determine if the iteration count is greater than a preset value. The preset value can refer to the total number of crossover iterations. The preset value can be customized according to actual needs. If the iteration count is less than the preset value, the processor can treat the third set as the new first set and return to the step of crossover processing the first candidate scheduling group in the first set to obtain the second set, until the iteration count is greater than or equal to the preset value. If the iteration count is greater than or equal to the preset value, the processor can determine the third candidate scheduling group with the highest fitness in the last obtained third set as the target scheduling group.

[0082] In one embodiment, mutating a second candidate production scheduling group in a second set to obtain a third set includes: obtaining an initial value for the mutation probability of the second candidate production scheduling group in the obtained second set; determining the mutation probability for each second candidate production scheduling group in the second set based on the initial value of the mutation probability, the number of iterations after numerical increment, and a preset value; for any second candidate production scheduling group in the second set, determining the segment to be mutated in the second candidate production scheduling group based on the mutation probability, the segment to be mutated including the operating equipment of each production process in the second candidate production scheduling group; for each production process in the segment to be mutated, determining all available operating equipment for the production process; for any production process in the segment to be mutated, replacing the operating equipment corresponding to the production process with equipment other than the operating equipment among all available operating equipment for the production process, so as to mutate the second candidate production scheduling group in the second set.

[0083] The processor can obtain an initial value for the mutation probability of the second candidate production group in the second set. As the number of iterations increases, the algorithm is prone to getting trapped in local optima. To avoid this, a dynamically changing mutation rate can be used during the iteration process. Therefore, the processor can determine the mutation probability for each second candidate production group in the second set based on the initial mutation probability, the number of iterations after numerical increment, and a preset value. For any second candidate production group in the second set, the processor can determine the segment to be mutated within the second candidate production group based on the mutation probability. This segment includes the operating equipment for each production process in the second candidate production group. For each production process in the segment to be mutated, the processor can determine all available operating equipment for that production process. During mutation processing, for any production process in the segment to be mutated, the processor can replace the operating equipment corresponding to that production process with any other available operating equipment from all available operating equipment in that production process, thus performing mutation processing on the second candidate production group in the second set.

[0084] For example, such as Figure 4 As shown, an example diagram of a second set to be mutated is presented. The segment to be mutated, determined by the mutation probability, is (3) in MS, where (3) refers to the work equipment with equipment number 3 capable of processing the second production step of the first part to be produced. If all available work equipment for processing the second production step of the first part to be produced also includes equipment with equipment number 4, then (3) in MS can be replaced with (4) to complete the mutation processing of the second candidate production group in the second set.

[0085] In one embodiment, the mutation probability is determined by formula (2):

[0086]

[0087] Among them, P g P0 refers to the initial value of the mutation probability, m refers to the number of iterations after the value is incremented, and M refers to the preset value.

[0088] In one embodiment, such as Figure 5The diagram illustrates another flowchart for determining a production scheduling plan. When determining the production scheduling plan, the processor can first encode the scheduling scheme to obtain an initial population, i.e., multiple candidate production scheduling groups. Then, the processor can determine the fitness of each individual in the initial population, i.e., determine the fitness of each candidate production scheduling group. If the number of iterations after crossover is less than the preset total number of iterations, the processor can determine that the termination condition has not been met and can determine the adaptive probability. The adaptive probability can include the crossover probability for any two individuals and the mutation probability for any individual. In this case, the processor can perform crossover on each individual according to the crossover probability, and then perform mutation on each crossover individual according to the mutation probability, thereby generating a new generation population. The new generation population includes multiple individuals that have undergone crossover and mutation. Further, the processor can determine the fitness of each individual after crossover and mutation, and again determine whether the current number of iterations after crossover is greater than or equal to the preset total number of iterations. If the current number of iterations after crossover is greater than or equal to the preset total number of iterations, the individual with the highest fitness among the individuals after the last mutation can be selected as the target individual. If the current number of iterations after crossover is less than the preset total number of iterations, continue to determine new adaptive probabilities, and perform crossover and mutation on individuals according to the new adaptive probabilities until the current number of iterations after crossover is greater than or equal to the preset total number of iterations.

[0089] In one embodiment, after determining the target production group from multiple candidate production groups based on fitness, the target production group can be transformed into a visual production schedule. During the transformation, it is necessary to ensure that the completion time of the (j-1)th production step of the i-th component to be produced is before the completion time of the j-th production step of the i-th component to be produced, and also to ensure that the work equipment corresponding to each production step of the i-th component to be produced is in an idle period. For example... Figure 6 The diagram illustrates an example of a production scheduling table. M1 to M5 are the equipment numbers of the work equipment corresponding to the processing steps. For example, the first production step (O) of the first part to be produced... 11 The first production process of the third component to be produced (O) 31 The first production process of the second component to be produced (O) 21 Processing can be performed using M1, M3, and M4 respectively at the same starting time point. 22 The space preceding this cell represents the non-idle time period of work equipment M2. 12 The space preceding the current cell represents the non-idle time period of the work device M3.

[0090] In one embodiment, after converting the target production group into a visual production schedule, the processor can send the production schedule to a display device for display and / or to a storage device for storage.

[0091] In one embodiment, after converting the target production group into a visualized production schedule, the processor can acquire material information and determine whether the total reserve of each material in the material information meets the material usage of each production process in the production schedule. In the event of insufficient warehouse materials, the processor can determine the material requirement for each production process in the production schedule based on the material usage of each production process in the production schedule and the material warehouse data. Only after the total reserve of each material in the material information meets the material usage of each production process in the production schedule can production be carried out for each component to be produced in the production schedule, ensuring full utilization of materials and guaranteeing the production capacity efficiency of the schedule.

[0092] The above technical solution enables the determination of production plan parameters for candidate production groups based on preset production targets and production parameters. The fitness of each candidate production group is then determined based on these parameters, leading to the identification of the target production group. This eliminates the need for manual intervention in production scheduling, reducing labor and time costs and significantly improving the efficiency of production plan determination. Furthermore, by determining the production decision parameters through preset production targets, the fitness function can be freely configured based on these parameters, resulting in strong adaptability to different operating conditions and more flexible production plan determination. By determining manufacturing cycle parameters, balanced production parameters, and completion time differences, discrete manufacturing scheduling can be performed rapidly based on actual production needs, significantly improving scheduling efficiency.

[0093] Figure 1 This is a flowchart illustrating a method for determining a production schedule in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0094] In one embodiment, such as Figure 7As shown, an apparatus 700 for determining a production schedule is provided, including an order acquisition module 710, a first production scheduling module 720, a production schedule parameter determination module 730, a fitness determination module 740, and a second production scheduling module 750, wherein:

[0095] The order acquisition module 710 is used to acquire orders to be processed. Each order to be processed includes multiple parts to be produced and the production process of each part.

[0096] The first production scheduling module 720 is used to determine multiple candidate production scheduling groups for all pending orders based on the production processes of all parts to be produced and the work equipment corresponding to each production process. Each candidate production scheduling group includes production parameters corresponding to each pending order, wherein the production parameters include the execution order of the production processes of each pending order and the work equipment corresponding to each production process.

[0097] The production scheduling parameter determination module 730 is used to determine the production scheduling parameters of each candidate production group based on preset production targets and production parameters.

[0098] The fitness determination module 740 is used to input the production planning parameters of each candidate production group into the fitness function to determine the fitness of each candidate production group.

[0099] The second scheduling module 750 is used to determine the target scheduling group from multiple candidate scheduling groups based on fitness, so as to produce all pending orders according to the target scheduling group.

[0100] Production scheduling refers to arranging the production plan for the components required to be processed in an order. When determining the production scheduling plan, the order acquisition module 710 can first acquire pending orders. Pending orders can include multiple items. Each pending order includes multiple components to be produced and production steps for each component. For each component, any two production steps can be discontinuous, but the order of all production steps is fixed.

[0101] Upon receiving pending orders, the first scheduling module 720 can determine multiple candidate scheduling groups for all pending orders based on the production processes of all components to be produced and the corresponding work equipment for each production process. Each candidate scheduling group includes production parameters corresponding to each pending order, including the execution order of the production processes for each order and the work equipment corresponding to each production process. In one embodiment, the first scheduling module 720 can randomly combine all production processes according to the production processes of each component to be produced, and can separately encode each production process and the corresponding work equipment to generate multiple candidate scheduling groups for all pending orders. Each production process corresponds to one work equipment. This work equipment is selected from multiple optional work equipment corresponding to the production process. Optional work equipment refers to equipment capable of processing the corresponding production process.

[0102] After identifying multiple candidate production groups, the production planning parameter determination module 730 can determine the production planning parameters for each candidate production group based on preset production targets and production parameters. The preset production targets can be set according to the actual needs of each pending order. Once the production planning parameters for each candidate production group are determined, the fitness determination module 740 can input these parameters into a fitness function to determine the fitness of each candidate production group. Fitness reflects the production efficiency after production is carried out according to each production process and the corresponding equipment in each candidate production group. The second production scheduling module 750 can further determine a target production group from the multiple candidate production groups based on the fitness, and then produce all pending orders according to the target production group.

[0103] In one embodiment, such as Figure 8 As shown, the production scheduling parameter determination module 730 includes:

[0104] The balanced production module 7301 is used to determine the idle time period of the work equipment corresponding to each production process of each component to be produced in the candidate production scheduling group. For each candidate production scheduling group, the balanced production parameters of the candidate production scheduling group are determined based on the total number of work equipment in the candidate production scheduling group and the idle time period of each work equipment.

[0105] The manufacturing cycle module 7302 is used to determine the completion time of the last production process in each candidate production group, and to determine the manufacturing cycle parameters of each candidate production group based on the completion time of each candidate production group.

[0106] The priority order module 7303 is used to determine the difference in completion time between pending orders of different order levels in each candidate scheduling group.

[0107] The production scheduling parameter determination module 7304 is used to obtain the production scheduling decision parameters for all candidate production scheduling groups, and to determine the production scheduling plan parameters for each candidate production scheduling group based on the production scheduling decision parameters, balanced production parameters, manufacturing cycle parameters, and completion time difference.

[0108] In one embodiment, the production balancing module 7301 further includes:

[0109] The time conversion module is used to convert the idle time period of each working device according to preset conditions to obtain the idle duration of each working device;

[0110] The balance parameter determination module is used to determine the average idle time of all working equipment in each candidate production group. The balance production parameters are determined based on the total number of working equipment, idle time, and average idle time in the candidate production group.

[0111] The device for determining the production schedule includes a processor and a memory. The order acquisition module, the first production scheduling module, the production schedule parameter determination module, the fitness determination module, and the second production scheduling module are all stored in the memory as program units. The processor executes the program modules stored in the memory to implement the corresponding functions.

[0112] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters allows for different methods used to determine production scheduling.

[0113] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0114] In one embodiment, such as Figure 9 As shown, another device 900 for determining production scheduling is provided, including a resource management module 910, an intelligent scheduling module 920, a data parsing module 930, a data storage module 940, and a display adjustment module 950, wherein:

[0115] Resource management module 910 is used to provide equipment capacity information, material consumption in production processes, and the number of personnel required to operate the equipment;

[0116] The intelligent scheduling module 920 is used to encode each production process and the corresponding equipment to identify multiple candidate scheduling groups. It is used to determine manufacturing cycle parameters based on equipment capacity information, to determine the balanced production parameters for each candidate scheduling group, to determine the completion time difference between pending orders of different order levels in each candidate scheduling group to prioritize the production of key orders, to determine process priority to ensure that the processing time of the previous production process of each component to be produced is scheduled before the processing time of the next production process, to determine the fitness of each candidate scheduling group based on manufacturing cycle parameters, balanced production parameters, completion time difference, and scheduling decision parameters, and to determine the target scheduling group based on the fitness.

[0117] The data parsing module 930 is used to determine the idle time period of each working equipment based on the capacity information, and to convert the idle time period of each working equipment into a timeline according to preset conditions to obtain the idle duration of each working equipment. It is used to convert the target production scheduling group into a visual production scheduling table and to determine the production scheduling decision parameters based on the preset production scheduling target.

[0118] The data storage module 940 is used to store the visualized production schedule and production scheduling system resource information. The production scheduling system resource information includes equipment capacity information, material consumption of production processes, number of personnel required to operate the equipment, and material requirements for each production process.

[0119] Display adjustment module 950 is used to display a visual production schedule and to manually fine-tune the visual production schedule.

[0120] In one embodiment, a storage medium is provided on which a program is stored, which, when executed by a processor, implements the method described above for determining a production schedule.

[0121] In one embodiment, a processor is provided for running a program, wherein the program executes the method described above for determining a production schedule.

[0122] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10As shown. The computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores data such as target production scheduling groups. The network interface A02 communicates with external terminals via a network connection. When executed by the processor A01, the computer program B02 implements a method for determining a production scheduling plan.

[0123] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0124] This application provides an apparatus including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of a method for determining a production schedule.

[0125] This application also provides a computer program product that, when executed on a data processing device, is adapted to execute a program having method steps for determining a production schedule.

[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0131] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0132] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0133] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0134] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining a production scheduling plan, characterized in that, The method includes: Obtain pending orders, each of which includes multiple parts to be produced and the production process for each part; Based on the production processes of all components to be produced and the work equipment corresponding to each production process, multiple candidate production scheduling groups are determined for all orders to be processed. Each candidate production scheduling group includes production parameters corresponding to each order to be processed, wherein the production parameters include the execution order of the production processes of each order to be produced and the work equipment corresponding to each production process. For each candidate production group, the production plan parameters of the candidate production group are determined according to the preset production target and production parameters; The production planning parameters of each candidate production group are input into the fitness function to determine the fitness of each candidate production group. A target production group is determined from the plurality of candidate production groups based on the fitness, so as to produce all pending orders according to the target production group; The step of determining the target production group from the plurality of candidate production groups based on the fitness includes: forming a first set by any two candidate production groups from the plurality of candidate production groups; performing crossover processing on the first candidate production groups in the first set to obtain a second set; performing mutation processing on the second candidate production groups in the second set to obtain a third set; determining the fitness of the third candidate production groups in each third set; and determining the third candidate production group with the highest fitness in the third set as the target production group. The process of cross-processing the first candidate production scheduling group in the first set to obtain the second set includes: determining the cross-processing probability of the first candidate production scheduling group in the first set based on the fitness of the first candidate production scheduling group in the first set; for any first set, cross-processing the first candidate production scheduling group in the first set according to the cross-processing probability to obtain the second set; wherein, for any first set, cross-processing the first candidate production scheduling group in the first set according to the cross-processing probability includes: for any first candidate production scheduling group in the first set, determining the segments to be cross-processed in the first candidate production scheduling group according to the cross-processing probability, the segments to be cross-processed including process segments and equipment segments; Specifically, determining the production planning parameters for each candidate production group based on preset production targets and production parameters includes: determining the idle time period of the equipment corresponding to each production process of each component to be produced in the candidate production group; determining the balanced production parameters for each candidate production group based on the total number of equipment and the idle time period of each equipment; determining the completion time of the last production process in each candidate production group; determining the manufacturing cycle parameters for each candidate production group based on the completion time; determining the completion time difference between pending orders of different order levels in each candidate production group; determining the production decision parameters for all candidate production groups based on the preset production targets; and determining the production planning parameters for each candidate production group based on the production decision parameters, the balanced production parameters, the manufacturing cycle parameters, and the completion time difference. The determination of the crossover probability of the first candidate production group in the first set based on the fitness of the first candidate production group in the first set includes: Determine the maximum and mean fitness of all candidate production scheduling groups; The crossover probability of the first candidate production group in the first set is determined based on the maximum fitness of the first candidate production group in the first set, the maximum fitness of all candidate production groups, and the average fitness of all candidate production groups.

2. The method for determining a production schedule according to claim 1, characterized in that, The process segment includes at least one production process, and the equipment segment includes operating equipment corresponding to each production process in the process segment; the step of performing cross-processing on the first candidate production scheduling group in the first set according to the cross-probability for any first set further includes: Traverse another first candidate production group in the first set to determine a target cross segment from the other first candidate production group. The target cross segment includes a target process segment with the same process as the cross segment but a different order, and a target equipment segment corresponding to the target process segment. The segment to be crossed is replaced with the target segment to complete the cross-processing for the first candidate production group in the first set.

3. The method for determining a production schedule according to claim 1, characterized in that, The crossover probability is determined by formula (1): （1） in, This refers to the crossover probability. This refers to the maximum fitness value of all candidate production scheduling groups. This refers to the average fitness of all candidate production scheduling groups. This refers to the maximum fitness value of the first candidate production group in the first set. It is a constant. .

4. The method for determining a production schedule according to claim 1, characterized in that, The method further includes: Set the initial value for the number of iterations for cross-processing; After cross-processing the first candidate production scheduling group in the first set, the number of iterations is incremented. After determining the fitness of the third candidate production group in each third set, it is determined whether the number of iterations is greater than or equal to a preset value. If the number of iterations is less than the preset value, the third set is used as the new first set, and the process of cross-processing the first candidate production group in the first set to obtain the second set is repeated until the number of iterations is greater than or equal to the preset value. If the number of iterations is greater than or equal to the preset number, the third candidate production group with the highest fitness in the last obtained third set is determined as the target production group.

5. The method for determining a production schedule according to claim 4, characterized in that, The process of performing mutation processing on the second candidate production scheduling group in the second set to obtain the third set includes: Obtain initial values ​​for the mutation probability of the second candidate production group in the second set; The mutation probability for each second candidate production group in the second set is determined based on the initial value of the mutation probability, the number of iterations after numerical increment, and the preset value. For any second candidate production group in the second set, determine the segment to be mutated in the second candidate production group according to the mutation probability, the segment to be mutated includes the operating equipment of each production process in the second candidate production group; For each production step in the segment to be mutated, determine all available operating equipment for that production step; For any production process in the segment to be mutated, the work equipment corresponding to the production process is replaced with a work equipment other than the work equipment corresponding to the production process from all available work equipment of the production process, so as to perform mutation processing on the second candidate production scheduling group in the second set.

6. The method for determining a production schedule according to claim 5, characterized in that, The mutation probability is determined by formula (2): （2） in, This refers to the probability of mutation. is the initial value of the mutation probability, m is the number of iterations after the value is incremented, and M is the preset value.

7. The method for determining a production schedule according to claim 1, characterized in that, The process of determining the balanced production parameters for each candidate production scheduling group, based on the total number of operating equipment in the candidate production scheduling group and the idle time period of each operating equipment, includes: The idle time period of each working device is converted according to preset conditions to obtain the idle duration of each working device; Determine the average idle time of all operating equipment in each candidate production group; The balanced production parameters are determined based on the total number of operating equipment in the candidate production scheduling group, the idle time, and the average idle time.

8. The method for determining a production schedule according to claim 1, characterized in that, The manufacturing cycle parameters are determined by formula (3): （3） in, This refers to the manufacturing cycle parameters. This refers to the completion time of each candidate production scheduling group; The balanced production parameters are determined by formula (4): （4） in, This refers to the balanced production parameters, where n refers to the total number of operating devices in each candidate production scheduling group. This refers to the average idle time of all operating equipment in each candidate production scheduling group. This refers to the idle time of the nth working device in each candidate production scheduling group; The time difference between completion and completion is determined by formula (5): （5） in, This refers to the difference in completion time, where N is the total number of all pending orders at order level 1, and L is the total number of all pending orders at order level 2. This refers to the completion time of the i-th pending order with a first-level order status. It refers to the first Completion time for pending orders at the second order level.

9. The method for determining a production schedule according to claim 8, characterized in that, The fitness of each candidate production scheduling group is determined by formula (6): （6） Where f refers to the fitness of each candidate production scheduling group. , as well as These refer to different production scheduling decision parameters. , as well as Both are 0 or 1. This refers to the manufacturing cycle parameters for each candidate production scheduling group. This refers to the equilibrium production parameters of each candidate production scheduling group. This refers to the difference in completion time.

10. A processor, characterized in that, It is configured to perform the method for determining a production schedule as described in any one of claims 1 to 9.

11. An apparatus for determining a production schedule, characterized in that, The device includes the processor according to claim 10.

12. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the method for determining a production schedule according to any one of claims 1 to 9.

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