Scheduling task determination method and device, equipment, medium and product

Abstract

The invention relates to a production scheduling task determination method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a plurality of initial production scheduling tasks; the plurality of initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the to-be-processed product; constructing a simulation model based on a preset discrete event simulation strategy, the parameter information and the processing information; the simulation model is used for simulating a performance quantized value of the initial production scheduling task under the production line; and inputting the plurality of initial production scheduling tasks into the simulation model, and determining a target production scheduling task from the plurality of initial production scheduling tasks. By adopting the method, the accuracy and reliability of determining the target production scheduling task can be improved, so that reasonable configuration of production resources and improvement of production efficiency are facilitated.

Description

Technical Field

[0001] The present application relates to the technical field of production line simulation, and in particular to a method, device, equipment, medium and product for determining production scheduling tasks. Background Art

[0002] In the garment manufacturing industry, traditional production management and planning methods are no longer able to meet the increasingly complex production demands. With the diversification of garment orders, the expansion of production scale, and the increasing demands for production efficiency and quality, more scientific and precise methods are needed to manage the production process of garment hanging production lines to adapt to market competition.

[0003] Traditionally, production scheduling for clothing production lines has relied heavily on manual experience. However, this approach can lead to low production efficiency. Summary of the Invention

[0004] Based on this, it is necessary to provide a production scheduling task determination method, device, equipment, medium and product to address the above technical problems, which can improve the production efficiency of products.

[0005] In a first aspect, the present application provides a method for determining a production scheduling task, the method comprising:

[0006] Acquire multiple initial production scheduling tasks; the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed;

[0007] A simulation model is constructed based on preset discrete event simulation strategies, parameter information, and processing information. The simulation model is used to simulate the performance quantification value of the initial production scheduling task on the production line.

[0008] A plurality of initial production scheduling tasks are input into the simulation model respectively, and a target production scheduling task is determined from the plurality of initial production scheduling tasks.

[0009] In one embodiment, a plurality of initial production scheduling tasks are input into a simulation model, and a target production scheduling task is determined from the plurality of initial production scheduling tasks, including:

[0010] For each initial production scheduling task, the performance quantification value corresponding to the initial production scheduling task is obtained according to the initial production scheduling task and the simulation model;

[0011] Based on the performance quantification value of each initial production scheduling task, a target production scheduling task is determined from the multiple initial production scheduling tasks.

[0012] In one embodiment, determining a target production scheduling task from a plurality of initial production scheduling tasks based on the performance quantified value of each initial production scheduling task includes:

[0013] The initial production scheduling task corresponding to the maximum performance quantization value is determined as the target production scheduling task.

[0014] In one embodiment, a simulation model is constructed based on a preset discrete event simulation strategy, parameter information, and processing information, including:

[0015] According to the parameter information and processing information, determine the main track information table, processing site information table and storage location information table of the production line;

[0016] A simulation model is constructed based on the preset discrete event simulation strategy, main track information table, processing site information table and storage location information table.

[0017] In one embodiment, the simulation model includes a main track sub-model, a buffer area sub-model, and a production process sub-model; based on the initial production scheduling task and the simulation model, a performance quantification value corresponding to the initial production scheduling task is obtained, including:

[0018] Obtain the average number of main tracks on track corresponding to the initial production scheduling task through the main track sub-model;

[0019] Through the buffer zone sub-model, the buffer rod utilization rate and product return times corresponding to the initial production scheduling task are obtained;

[0020] Obtain the production cycle corresponding to the initial production scheduling task through the production process sub-model;

[0021] The average number of on-track main rails, cache rod utilization, product reflow times, and production cycle are weighted according to preset weights to obtain a performance quantification value.

[0022] In one embodiment, the method further comprises:

[0023] Based on the performance quantification value of each initial production scheduling task, the target production scheduling task is optimized.

[0024] In a second aspect, the present application further provides a production scheduling task determination device, the device comprising:

[0025] A production information acquisition module is used to obtain multiple initial production scheduling tasks; the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed;

[0026] The simulation model building module is used to build a simulation model based on the preset discrete event simulation strategy, parameter information and processing information; the simulation model is used to simulate the performance quantification value of the initial production scheduling task on the production line;

[0027] The production scheduling task simulation module is used to input multiple initial production scheduling tasks into the simulation model respectively, and determine the target production scheduling task from the multiple initial production scheduling tasks.

[0028] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] Acquire multiple initial production scheduling tasks; the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed;

[0030] A simulation model is constructed based on preset discrete event simulation strategies, parameter information, and processing information. The simulation model is used to simulate the performance quantification value of the initial production scheduling task on the production line.

[0031] A plurality of initial production scheduling tasks are input into the simulation model respectively, and a target production scheduling task is determined from the plurality of initial production scheduling tasks.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0033] Acquire multiple initial production scheduling tasks; the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed;

[0034] A simulation model is constructed based on preset discrete event simulation strategies, parameter information, and processing information. The simulation model is used to simulate the performance quantification value of the initial production scheduling task on the production line.

[0035] A plurality of initial production scheduling tasks are input into the simulation model respectively, and a target production scheduling task is determined from the plurality of initial production scheduling tasks.

[0036] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the following steps:

[0037] Acquire multiple initial production scheduling tasks; the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed;

[0038] A simulation model is constructed based on preset discrete event simulation strategies, parameter information, and processing information. The simulation model is used to simulate the performance quantification value of the initial production scheduling task on the production line.

[0039] A plurality of initial production scheduling tasks are input into the simulation model respectively, and a target production scheduling task is determined from the plurality of initial production scheduling tasks.

[0040] The above-mentioned production scheduling task determination method, device, equipment, and medium product first obtains multiple initial production scheduling tasks. Since the multiple initial production scheduling tasks are different production scheduling tasks generated based on the parameter information of each processing node on the production line and the processing information of the product to be processed, they have certain reference value; then, based on the preset discrete event simulation strategy, parameter information and processing information, a simulation model is constructed, which can simulate the production process in a virtual environment, avoiding the time and cost consumption brought about by large-scale experiments in actual production; finally, the multiple initial production scheduling tasks are input into the simulation model respectively to determine the target production scheduling task based on the performance quantitative value of the initial production scheduling task output by the simulation model on the production line. This decision-making method based on data and model reduces the influence of human subjective factors, thereby helping to improve the accuracy and reliability of the target production scheduling task determination, and thus helps to achieve the rational allocation of production resources and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A diagram illustrating an application environment of the production scheduling task determination method provided in some embodiments of the present application;

[0042] Figure 2 A flowchart of a method for determining production scheduling tasks provided in some embodiments of the present application;

[0043] Figure 3 A flowchart of determining target production scheduling tasks provided in some embodiments of the present application;

[0044] Figure 4 A flowchart of constructing a simulation model provided in some embodiments of the present application;

[0045] Figure 5 A schematic diagram of a simulation model provided for some embodiments of the present application;

[0046] Figure 6 A flowchart for determining performance quantification values ​​provided for some embodiments of the present application;

[0047] Figure 7 Schematic diagram of a clothing hanging production line simulation model provided in some other embodiments of the present application;

[0048] Figure 8 Schematic diagram of a roadmap and task list provided for some embodiments of the present application;

[0049] Figure 9 A schematic diagram illustrating the flow data and utilization rates of each main rail and cache bin provided in some embodiments of the present application;

[0050] Figure 10 A structural block diagram of a production scheduling task determination device provided in some embodiments of the present application;

[0051] Figure 11 An internal structural diagram of a computer device provided for some embodiments of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0053] The production scheduling task determination method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 can be, but is not limited to, various computer devices such as personal computers, laptops, smartphones, and tablet computers. The terminal 102 can connect to each node facility in the garment hanging production line, i.e., the processing equipment 104, via wired or wireless means to obtain parameter information of each processing equipment 104, and execute the production scheduling task determination method of the present application to help the production line determine the final feasible production scheduling task.

[0054] In one embodiment, Figure 2 As shown, this method is applied to Figure 1 The computer device in the embodiment is used as an example to illustrate. In this embodiment, the method includes the following steps:

[0055] Step 202: Acquire multiple initial production scheduling tasks.

[0056] Among them, the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed.

[0057] For example, these initial production scheduling tasks specify how to distribute and circulate products between various processing nodes within a certain timeframe, such as when products enter each processing node and the processing order of each processing node. Different initial production scheduling tasks may result in different production efficiency and resource utilization.

[0058] Specifically, the initial production schedule includes processing task information and processing route information for each processing node on the production line. The processing task information is used to define the production schedule and production sequence that each processing node needs to execute. The processing route information is used to define the processing route of the product on the production line.

[0059] Optionally, experienced production planners can schedule production based on past production experience and actual conditions. For example, they can determine multiple different initial production scheduling tasks with certain feasibility based on factors such as the performance of each production equipment, the processing volume of the product, and the required time of the order.

[0060] Step 204 : constructing a simulation model based on the preset discrete event simulation strategy, parameter information, and processing information.

[0061] Parameter information includes specific information related to each processing step on the production line, such as the performance parameters, status information, layout information, and staffing information of the processing equipment. Processing information includes process data and order data of the products to be processed.

[0062] A preset discrete event simulation strategy is a set of predefined rules and methods that guide the discrete event simulation process. Discrete event simulation is a method for modeling and analyzing the progression of discrete events in a system over time. In scenarios where production scheduling tasks are determined, a preset discrete event simulation strategy includes event definitions, event triggering rules, a time advancement mechanism, and data collection and statistical methods. Specifically, event definitions specify various events that may occur during the production process, such as product arrival at a processing node, processing completion, and equipment failure. Event triggering rules specify the conditions and sequence for event occurrence. For example, when a product arrives at a processing node and the equipment at that node is idle, a processing event is triggered. The time advancement mechanism determines how to advance simulation time, typically using event scheduling. This involves processing events sequentially in the order in which they occur, and updating the system state and time. Data collection and statistical methods define what data to collect during the simulation and how to perform statistical analysis on this data to evaluate the performance of the initial production scheduling task.

[0063] The simulation model is a computer model constructed based on the above-mentioned preset discrete event simulation strategy, the parameter information of each processing node and the processing information of the product to be processed. It is used to simulate the execution of the initial production scheduling task on the production line. The simulation model is also used to simulate the performance quantification value of the initial production scheduling task under the production line. The simulation model in this application is used to predict various performance indicators in the production process by simulating different initial production scheduling tasks before actual production, providing a reference for decision makers to avoid trial and error in actual production, thereby saving time and cost. The performance quantification value is a specific numerical indicator used to measure the execution effect of the initial production scheduling task on the production line. The larger the performance quantification value, the better the performance of the corresponding initial production scheduling task and the more valuable it is for reference. The performance quantification value is related to the flow data and utilization rate of each main track and cache bin under the initial production scheduling task.

[0064] As you can see, simulation models can simulate the production process in a virtual environment, avoiding the time and cost associated with large-scale trials in actual production. Furthermore, by adjusting and optimizing the model, the effectiveness of different initial production schedules can be quickly evaluated, improving the scientific nature and accuracy of decision-making.

[0065] Step 206: Input the multiple initial production scheduling tasks into the simulation model respectively, and determine the target production scheduling task from the multiple initial production scheduling tasks.

[0066] Among them, the target production scheduling task is the optimal production scheduling plan selected from multiple initial production scheduling tasks, which can help decision makers achieve goals such as maximizing production efficiency and minimizing costs.

[0067] As you can see, by evaluating and comparing multiple initial production schedules using a simulation model, we can intuitively understand the quantitative performance of each schedule, thereby selecting the schedule that best meets the company's production goals and constraints. This data- and model-based decision-making approach can reduce the influence of subjective factors, thereby improving the accuracy and reliability of target production schedule determination.

[0068] In the above-mentioned production scheduling task determination method, multiple initial production scheduling tasks are first obtained. Since the multiple initial production scheduling tasks are different production scheduling tasks generated based on the parameter information of each processing node on the production line and the processing information of the product to be processed, they have certain reference value; then, based on the preset discrete event simulation strategy, parameter information and processing information, a simulation model is constructed, which can simulate the production process in a virtual environment, avoiding the time and cost consumption brought about by large-scale experiments in actual production; finally, the multiple initial production scheduling tasks are input into the simulation model respectively to determine the target production scheduling task according to the performance quantitative value of the initial production scheduling task output by the simulation model on the production line. This decision-making method based on data and models reduces the influence of human subjective factors, thereby helping to improve the accuracy and reliability of the target production scheduling task determination, and thus helps to achieve the rational allocation of production resources and improve production efficiency.

[0069] In one embodiment, Figure 3 As shown, multiple initial production scheduling tasks are input into the simulation model, and target production scheduling tasks are determined from the multiple initial production scheduling tasks, including:

[0070] Step 302 : For each initial production scheduling task, obtain a performance quantization value corresponding to the initial production scheduling task according to the initial production scheduling task and the simulation model.

[0071] Alternatively, you can first input specific information about the initial production schedule, such as order quantity, delivery time, and the processing sequence and time requirements for each process, into a pre-built simulation model. The model can then be run, simulating the entire garment production process according to discrete event simulation logic, including transportation on the main track, storage and warehousing in and out of the buffer area, and processing at each production site. Simultaneously, the actual processing time for each product at each processing site is recorded and accumulated to obtain the total processing time for the entire order. This reflects the efficiency of the processing sites under the production schedule. The transportation time of products on the main track, including waiting time and actual transportation time, is calculated to assess the impact of main track transportation efficiency on overall production. The ratio of actual operating time to total operating time for each processing machine during the simulation period is calculated to understand equipment utilization efficiency and determine whether any equipment is idle or overloaded. Inventory levels in each warehouse area at different time points are monitored, and the maximum, minimum, and average inventory values ​​are calculated to assess the rationality of inventory management and avoid inventory backlogs or stock-outs. The number of product inbound and outbound shipments is recorded to analyze the busyness and smoothness of logistics. Furthermore, the ratio of on-time completed orders to total orders can be calculated to gauge the effectiveness of the production schedule in delivering products on time. Based on these key indicators—production efficiency, logistics and inventory, and order delivery—the performance quantification corresponding to the initial production schedule can be determined.

[0072] Step 304 : determining a target production scheduling task from the multiple initial production scheduling tasks based on the performance quantification value of each initial production scheduling task.

[0073] Optionally, the collected key indicator data can be quantified. For example, processing time and transportation time can be converted into specific numerical values, and equipment utilization and on-time delivery rates can be expressed as percentages. Weights can be assigned to each indicator based on actual production needs and key areas of focus. Multiple indicators can then be combined into one or more performance metrics using methods such as weighted summation. For example, on-time delivery rates can be given a higher weight, while equipment utilization and inventory levels can be given appropriate weights. This comprehensive performance metric can then be calculated to assess the quality of the initial production schedule.

[0074] In this embodiment, the final target production scheduling task is determined by the level of the performance quantification value, which has high accuracy and reliability, thereby helping to ensure the production efficiency of the products to be processed.

[0075] In one embodiment, based on the performance quantization values ​​of the initial production scheduling tasks, a target production scheduling task is determined from the multiple initial production scheduling tasks, including: determining the initial production scheduling task corresponding to the maximum performance quantization value as the target production scheduling task.

[0076] It's understandable that performance quantification values ​​comprehensively reflect key indicators in the production process, such as equipment utilization, processing time, and transportation time. The production scheduling task corresponding to the maximum performance quantification value often means that within existing resource conditions, resources such as manpower, equipment, and materials can be more rationally allocated. For example, this production scheduling task may minimize the idle time of processing equipment and maximize the efficiency of main rail transportation, thereby increasing the output speed of the entire production system and shortening the product production cycle. In addition, by quantitatively evaluating the performance of multiple initial production scheduling tasks, the target production scheduling task is selected based on objective data, avoiding the limitations of subjective judgment and human experience. This data-driven decision-making method is more scientific and accurate, and can improve the reliability and effectiveness of decision-making.

[0077] In one embodiment, Figure 4 As shown, based on the preset discrete event simulation strategy, parameter information and processing information, a simulation model is constructed, including:

[0078] Step 402: Determine the main track information table, processing site information table, and storage location information table of the production line based on the parameter information and processing information.

[0079] The production line in this application uses a garment hanging production line as an example. This production line typically includes three parts: a hanging station, a sewing line, and a back-end process. Therefore, the parameter information in this embodiment specifically includes positional parameters such as the relative position and length of each main rail, as well as the type and quantity of processing equipment used in the hanging station, sewing line, and back-end process, as well as information about the corresponding operators. It also includes information such as the number of buffer bins and the number and capacity of storage rods in each buffer bin. Based on this parameter information, the main rail information table, processing station information table, and storage location information table of the production line can be obtained.

[0080] The main track information table records the relevant attributes and parameters of the production line's main track and is used to describe the characteristics of the primary channel for product transportation on the production line. The main track is a key component connecting various processing stations and storage locations, and products flow between different areas along the main track. As shown in Table 1, the main track information table includes information such as the main track number, the total number of stations, push rod spacing, chain speed, and main track capacity (i.e., the maximum number of products that can be accommodated on the main track at any one time). In the simulation model, the main track information table is used to simulate the transportation of products to be processed on the main track, calculate the transportation time of products to be processed on the main track, determine whether congestion will occur, and thus evaluate the main track's transportation efficiency and its impact on the overall production process.

[0081]

[0082] Table 1

[0083] The processing station information table describes the details of each processing node on the production line. Each processing station is responsible for performing specific processing operations on the product. In this embodiment, the types of processing stations primarily include hanging stations, sewing lines, and post-processing stations. As shown in Table 2, the processing station information table includes information such as the station number, station role, enabled status, capacity, on-track capacity, output buffer capacity, inbound station number, and outbound station number. This processing station information is used to simulate the processing of the product at each processing station, calculate processing time, assess the production capacity and utilization rate of the processing station, and analyze the collaborative work between different processing stations.

[0084]

[0085] Table 2

[0086] The location information table records information about various locations within the production line. These locations are used to store raw materials, work-in-progress, and finished products. Location types include locations such as stations and empty hanger recycling stations. As shown in Table 3, the location information table includes information such as location number, location type, location role, enabled status, capacity, on-track capacity, inbound station number, and outbound station number. The location information table is used in the simulation model to simulate the storage and inbound / outbound processes of products waiting to be processed at the locations, analyze inventory level fluctuations, evaluate location utilization efficiency, and determine whether inventory overstocking or shortages are occurring.

[0087]

[0088] Table 3

[0089] Step 404 : constructing a simulation model based on the preset discrete event simulation strategy, the main track information table, the processing site information table, and the storage location information table.

[0090] Alternatively, corresponding simulation objects can be created in the simulation software based on the data in the master track information table, processing site information table, and storage location information table. For example, parameters such as track length and transport speed from the master track information table can be mapped to the master track object in the simulation software; information such as processing equipment performance and processing time from the processing site information table can be assigned to the processing site object; and information such as storage location capacity and location from the storage location information table can be transferred to the storage location object. Events and rules can then be defined for each simulation object based on the pre-set discrete event simulation strategy. For example, for the master track object, events for product entry and exit are defined, as well as rules for handling congestion during transportation; for the processing site object, events for product arrival, processing start, and completion are defined, as well as rules for processing sequence and resource allocation; and for the storage location object, events for product entry and exit are defined, as well as inventory management rules. Finally, based on the actual layout of the production line and logistics processes, associations are established between the master track, processing site, and storage location objects. For example, the connections between the master track, processing site, and storage location are determined, allowing products to move between these objects according to their actual flow paths. Finally, set the simulation timeframe, initial conditions, and other parameters, and then run the simulation model. During the simulation, record the product flow between the main track, processing stations, and storage locations, the status changes of each object, and related performance indicators (such as transportation time, processing time, inventory levels, etc.).

[0091] In this embodiment, a simulation model is constructed based on the preset discrete event simulation strategy, main track information table, processing site information table and storage location information table, which helps to ensure the scientificity and reliability of the simulation model construction, thereby ensuring the reliability of the subsequent simulation of each initial production scheduling task based on the simulation model.

[0092] In one embodiment, the simulation model includes a main track sub-model, a buffer area sub-model, and a production process sub-model.

[0093] Specifically, the data foundation for the main track submodel is derived from the data in the main track table. When creating the main track submodel, a preset discrete event simulation strategy is first launched. The preset simulation software reads parameters such as length, width, capacity, operating speed, and pusher spacing from the main track information table. Based on these parameters, entry and exit control methods are automatically added to the conveyor line (i.e., the main track), achieving uniformly spaced garment transport along the conveyor line, simulating a real pusher line. The entry control method specifies the logic to be executed when a hanger is transported from the entry station to the main track, while the exit control method specifies the logic to be executed when a hanger on the main track leaves the processing equipment and arrives at the exit station. Through these two control methods, the process of garments moving along a physical hanging line is simulated, effectively simulating the operation of a real pusher line, ultimately forming the main track submodel.

[0094] The data foundation for the cache submodel comes from the data in the location information table. When creating the cache submodel, pre-configured simulation software automatically sets parameters such as station capacity, on-track capacity, and on-track quantity based on the information in the location information table. An inbound control method is then automatically added. This method, executed when a hanger is loaded onto a storage rod, updates the on-track quantity, records the process number for the hanger's required processing, and counts the number of times the hanger has exited the main rail. An outbound routing method is then automatically added. This method determines whether a hanger can be released based on its current status, primarily based on outbound avoidance (only one hanger can be transported from the same push rod on the main rail). Through these operations, the cache submodel is constructed, simulating the actual operation of the cache warehouse.

[0095] The data foundation for the production process submodel comes from the processing station information table. When creating the production process submodel, pre-configured simulation software automatically sets parameters such as station role, station capacity, on-track capacity, on-track quantity, and exit buffer quantity based on the station information table. Various activity methods are added. For example, the entry control method updates the on-track quantity and counts the number of exits. The hanging activity method hangs garments based on the current station task table and adds custom attributes such as the production schedule number, process number, and route map. The operation time setting method automatically sets the processing time for the garment at the current station based on the route map. The completion activity method updates the process number of the garment after processing is complete. The exit activity method sets the next target station or storage bar for the hung garment based on the route map and determines whether to allow exit based on the capacity and actual quantity of the target station or storage bar. This completes the modeling of the hanging station portion of the production process.

[0096] Similarly, we set the relevant parameters and add activity methods based on the processing station information table. This is similar to the hanging station, but with the addition of a hanger removal method to determine whether there are garments in the buffer that meet the removal criteria and handle the return flow if they do not meet the criteria. This completes the modeling of the sewing line portion of the production process.

[0097] Then, according to the processing station information table, the parameters are set. In addition to the added activity method similar to the sewing line, the completion activity method will clear all custom attributes of the garment and reset it to an empty hanger. An empty hanger return method is also added to return the empty hanger to the hanging station or empty hanger storage rod according to the strategy. In this way, the model construction of the back-end part of the production process is completed. Finally, based on the main track sub-model, the buffer area sub-model and the production process sub-model, the following can be obtained: Figure 5 The simulation model shown.

[0098] like Figure 6 As shown in the figure, based on the initial production scheduling task and the simulation model, the performance quantification value corresponding to the initial production scheduling task is obtained, including:

[0099] Step 602: Obtain the average number of on-track items on the main track corresponding to the initial production scheduling task through the main track sub-model.

[0100] The average number of trains on the main track reflects its utilization efficiency and load capacity. A high average number of trains on the main track may indicate congestion, impacting transportation efficiency; a low average number of trains on the main track may indicate underutilization of main track resources. Calculating the average number of trains on the main track helps optimize transportation scheduling and improve the overall flow of production logistics.

[0101] Optionally, during the simulation, the main track sub-model continuously records the number of on-track hangers on the main track at each time point. After the simulation ends, all recorded on-track counts are added together and divided by the total number of simulation time steps to obtain the average on-track count of the main track.

[0102] For example, assume the simulation lasts 100 time units. During these 100 time units, the number of on-track hangers on the main rail is 10, 12, 11, ..., 13. Adding these 100 values ​​together gives a total of 1150. Therefore, the average number of on-track hangers on the main rail = 1150 ÷ ​​100 = 11.5.

[0103] Step 604: Obtain the cache rod utilization rate and product return times corresponding to the initial production scheduling task through the cache area sub-model.

[0104] The cache rod utilization rate reflects the space utilization in the cache area. Excessive utilization can lead to cache congestion, impacting inbound and outbound efficiency; low utilization results in wasted space. The number of product return flows reflects abnormalities or irrational arrangements during the production process; excessive return flows increase transportation costs and production cycle time. These two indicators can be used to optimize cache area management and production processes.

[0105] Optionally, during the simulation, the cache sub-model records the actual used capacity and total capacity of each cache bar. After the simulation, the ratio of the sum of the actual used capacity of all cache bars to the total capacity is calculated as the cache bar utilization rate. In the cache sub-model, every product backflow is counted. After the simulation, the count result is the number of product backflows.

[0106] For example, assume the cache area has 10 cache bars, each with a total capacity of 20 hangers. After the simulation, the total actual used capacity of all cache bars is 150 hangers. Therefore, cache bar utilization = 150 ÷ ​​(10 × 20) = 75%. If product reflow occurs five times during the simulation, the number of product reflows is 5.

[0107] Step 606: Obtain the production cycle corresponding to the initial production scheduling task through the production process sub-model.

[0108] The production cycle is a key indicator of production efficiency, reflecting the time required from the start of production to the final completion of a product. Shortening the production cycle can speed up the product's time to market, improve the company's market responsiveness, and reduce production costs.

[0109] Optionally, during the simulation, the production process submodel records the time it takes for each product to complete, from the moment it enters the hang-up station, through each processing station (sewing line, finishing process, etc.). For all products in the initial production schedule, the average production time is calculated to represent the production cycle for that schedule.

[0110] For example, there are five products in the initial production schedule, and their production times are 20, 22, 18, 21, and 23 time units, respectively. The production cycle time = (20 + 22 + 18 + 21 + 23) ÷ 5 = 20.8 time units.

[0111] Step 608 : performing weighted processing on the average number of on-track devices on the main track, the cache rod utilization rate, the number of product return times, and the production cycle according to preset weights to obtain a performance quantification value.

[0112] It's understandable that different indicators have varying degrees of impact on the production system. By applying pre-set weights, we can comprehensively consider the importance of each indicator and generate a comprehensive and objective performance quantification. This performance quantification can be used to compare the performance of different production scheduling tasks, providing a scientific basis for decision-making.

[0113] For example, we first determine the preset weights for each indicator. For example, we assume that the weights for the average number of on-track devices on the main rail, cache rod utilization, product reflow times, and production cycle are 0.2, 0.3, 0.1, and 0.4, respectively. We then multiply the actual value of each indicator by the corresponding weight and add the results together to obtain the performance quantification value.

[0114] In this embodiment, by comprehensively considering indicators of multiple aspects such as main rail transportation, buffer area management and production process, a comprehensive and objective evaluation of the initial production scheduling task can be carried out, avoiding decision-making deviations caused by focusing only on a single indicator, thereby helping to improve the accuracy and reliability of the final target production scheduling task, and thus improving product processing efficiency.

[0115] In one possible embodiment, when inputting the initial production scheduling tasks into the simulation model for simulation, after importing the corresponding processing task table and the processing route map data of each style of clothing for each processing site, different processing and production processes are simulated using preset simulation software, specifically including the following steps:

[0116] In step A10, import the task table for each station. The task table is shown in Table 4. The route map within the task table is shown in Figure 5. Each workstation, including the hanging station, sewing line, and back-end, has a corresponding task table. The current station's task table determines the production schedule and production sequence that station can process. The task table contains the following information: production schedule number, status, number of hung pieces, number of tasks, number of completed items, batch size, route map, style number, color, and size. The four parameters, status, number of hung pieces, number of tasks, and number of completed items, are updated in real time during the simulation model's execution.

[0117]

[0118] Table 4

[0119]

[0120] Table 5

[0121] Step A20: Import a route map. A simulation model can have multiple route maps, each of which defines a garment processing route. Different production schedules can use the same route map, or the same production process. The route map contains the following information: process number, station, and operation time.

[0122] Step A30 sets the initial state and begins the simulation. The simulation model is initialized using the hanging station initialization table and the storage area initialization table. The hanging station initialization table includes the storage location number and storage location quantity, while the storage area initialization table includes the storage location number, production schedule number, storage quantity, process number, and route map. Based on the table contents, the corresponding number of empty hangers or garments are automatically generated at the corresponding locations at the start of the simulation.

[0123] Step A40, outputting the flow data of the hanging main rail and the buffer warehouse: running the clothing hanging production line simulation model considering the return of empty hangers under different production scheduling task data, and outputting the flow data and utilization rate of each main rail and buffer warehouse under different production scheduling task plans.

[0124] Specific operations include: first importing different production task lists and route maps, such as adjusting the task quantity of each production schedule, the type of production schedule in each site task list, and adjusting the processing time of each process in the route map; then running the simulation model under different clothing production conditions, and recording the number of times clothing enters the track, the number of times it derails, the number of stations on the track, the utilization rate of each cache rod in the cache warehouse, and the number of hanger returns on each main track during the simulation process.

[0125] In summary, the production scheduling task determination method of the present application first sorts out the data of clothing production and processing orders, processes, equipment, etc., measures the production line layout, extracts key indicator parameters such as the main track, site, and storage location, organizes them into a table and imports them into the simulation model. Then, based on the discrete event simulation software, the main track, buffer warehouse, hanging station, sewing line, back-end and other objects are created according to the information such as the facility layout, and the corresponding control methods and activity logic are set to realize the blocked backflow of clothing out of the warehouse and the backflow of empty hangers, and simulate the operation of the push rod line. Then, the task table and route map of each site are imported, and the simulation is run after setting the initial state through the initialization table to simulate different processing and production processes. Finally, the simulation model is run under different production scheduling tasks to record and output the flow data and utilization rate such as the number of times the main track clothing enters / derails, the number of on-track clothes, the utilization rate of the buffer warehouse buffer rod, the number of hanger backflows, etc., so as to evaluate the performance of the production line and provide data support for the optimization of production line layout and production scheduling decisions.

[0126] In one embodiment, the method further includes: optimizing the target production scheduling tasks based on the performance quantification values ​​of the initial production scheduling tasks.

[0127] It's understandable that while the target production schedule is the optimal choice, it's not necessarily the right one. By comparing its performance metrics with those of other initial production schedules, we can identify areas within the target production schedule where there's room for improvement. In other words, after determining the target production schedule, staff can further adjust it based on actual production needs, obtaining an adjusted target production schedule. The final production schedule is then determined based on this adjusted target production schedule.

[0128] Optionally, the staff can further simulate the adjusted target production scheduling task based on the above-mentioned preset discrete event simulation strategy. If the performance quantification value corresponding to the adjusted target production scheduling task is indeed higher, the adjusted target production scheduling task will be used as the final production scheduling task.

[0129] In a detailed embodiment, Figure 7 As shown in the figure, a complete hanging production line at a clothing manufacturer is used as an example. The production process includes five stages: hanging pieces, storage of cut pieces, sewing, storage of finished garments, and final inspection. This hanging production line has two hanging stations, five sewing lines, one final inspection station, and two buffer warehouses. The buffer warehouses are divided into a cutting warehouse and a garment warehouse. Due to layout constraints, the garment warehouse is divided into two separate buffer warehouses, one on top and one on the bottom. Garment pieces are hung on hangers at the hanging stations and temporarily stored in the cutting warehouse. Each sewing line then calls the pieces out of the warehouse for processing. After processing, they are temporarily stored in the garment warehouse. The final station then calls the finished garment out of the warehouse for quality inspection. After quality inspection, the garments are removed from the hangers, and the empty hangers are returned to the warehouse.

[0130] Step 1: Prepare basic data: Organize garment production and processing order data and process data, measure the layout of the hanging production line facilities, and obtain the relative position and length of each main rail. Obtain information on the type and quantity of processing equipment used for each sewing line, hanging station, and back-end process, as well as the corresponding operator information. Count the number of storage areas and the number and capacity of storage rods in each area. Organize this data into a main rail table, a station table, and a storage location table, and import them into the simulation model.

[0131] Step 2: Build a simulation model: Based on the discrete event simulation engine and factory layout information, a graphical model is created for the factory layout. The hanging stations, buffer warehouses, sewing lines, and back-end processes are set up in corresponding locations. The basic logic of the production and logistics process is set up, including hanging activities, clothing in and out of the warehouse strategy, task list execution strategy, operation time settings, in and out of the station method, in and out of the track method, empty hanger return, and other important time parameters such as the simulation calendar. This completes the establishment of a simulation model for the garment hanging production line.

[0132] Step 3: Input production scheduling data and run the simulation model: Create a route map. The route map represents the garment processing route. Multiple route maps can exist within the same model, including "process number," "station," and "operation time." The route map must encompass all stages of garment processing, namely, the hanging station, cutting warehouse, sewing line, garment warehouse, and finishing process. The "station" for the cutting warehouse and garment warehouse can be either a warehouse name, such as "Warehouse A" or "Warehouse B," or a storage rod name, such as "A-1" or "B-1." When the name is a warehouse name, a storage rod is automatically assigned based on the corresponding warehousing rules. Create a task table for each station, including the hanging station, sewing line, and finishing process, and name it "Station Name + Task Table." Fill in all parameters in the task table, including the production order number, production status, task quantity, batch size, route map, style, color, and size. The production status can be divided into "in production" and "completed". The production order with the status of "in production" will be produced normally. When the completed number is equal to the task quantity, the production status will be updated to "completed". The batch size refers to the number of garments shipped out of the production order when the processing site calls for it. For example, if the batch size is 4, it means that when the number of garments with the production order number in the cache warehouse is greater than 4, the warehouse will be shipped out 4 pieces at a time. The route map and task table are as follows Figure 8 As shown;

[0133] Step 4: Output the flow data of the hanging main rail and the buffer warehouse: Run the simulation model of the clothing hanging production line considering the return of empty hangers under different production scheduling task data, and output the flow data and utilization rate of each main rail and buffer warehouse under different production scheduling task plans, such as Figure 9 shown.

[0134] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0135] Based on the same inventive concept, the embodiments of the present application also provide a production scheduling task determination device for implementing the production scheduling task determination method involved above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more embodiments of the production scheduling task determination device provided below can be found in the above-mentioned limitations of the production scheduling task determination method and will not be repeated here.

[0136] In one embodiment, Figure 10 As shown, a production scheduling task determination device is provided, comprising: a production information acquisition module 1002, a simulation model construction module 1004 and a production scheduling task simulation module 1006, wherein:

[0137] The production information acquisition module 1002 is used to acquire multiple initial production scheduling tasks; the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed;

[0138] The simulation model building module 1004 is used to build a simulation model based on a preset discrete event simulation strategy, parameter information, and processing information; the simulation model is used to simulate the performance quantification value of the initial production scheduling task on the production line;

[0139] The production scheduling task simulation module 1006 is used to input multiple initial production scheduling tasks into the simulation model respectively, and determine the target production scheduling task from the multiple initial production scheduling tasks.

[0140] In one embodiment, the production scheduling task simulation module 1006 is also used to: for each initial production scheduling task, obtain the performance quantification value corresponding to the initial production scheduling task according to the initial production scheduling task and the simulation model; and determine the target production scheduling task from multiple initial production scheduling tasks based on the performance quantification value of each initial production scheduling task.

[0141] In one embodiment, the production scheduling task simulation module 1006 is further configured to: determine the initial production scheduling task corresponding to the maximum performance quantization value as the target production scheduling task.

[0142] In one embodiment, the simulation model construction module 1004 is also used to: determine the main track information table, processing site information table and storage location information table of the production line based on parameter information and processing information; and construct a simulation model based on the preset discrete event simulation strategy, the main track information table, the processing site information table and the storage location information table.

[0143] In one embodiment, the production scheduling task simulation module 1006 is also used to: obtain the average number of main rails on track corresponding to the initial production scheduling task through the main rail sub-model; obtain the cache rod utilization rate and product return times corresponding to the initial production scheduling task through the cache area sub-model; obtain the production cycle corresponding to the initial production scheduling task through the production process sub-model; and perform weighted processing on the average number of main rails on track, cache rod utilization rate, product return times and production cycle according to preset weights to obtain a performance quantification value.

[0144] In one embodiment, the device further includes an optimization module, which is configured to optimize the target production scheduling tasks based on the performance quantification values ​​of the initial production scheduling tasks.

[0145] Each module in the above-mentioned production scheduling task determination device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0146] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 11As shown. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication. Wireless communication can be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for determining production scheduling tasks. The display screen of the computer device can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer covering the display screen, or keys, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0147] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0148] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0149] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0150] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0152] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0153] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are 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.

[0154] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining production scheduling tasks, characterized in that: The method comprises: Acquire multiple initial production scheduling tasks; the multiple initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed; Constructing a simulation model based on a preset discrete event simulation strategy, the parameter information, and the processing information; the simulation model is used to simulate the performance quantification value of the initial production scheduling task under the production line; The multiple initial production scheduling tasks are respectively input into the simulation model, and a target production scheduling task is determined from the multiple initial production scheduling tasks.

2. The method according to claim 1, characterized in that Inputting the plurality of initial production scheduling tasks into the simulation model respectively and determining a target production scheduling task from the plurality of initial production scheduling tasks includes: For each initial production scheduling task, obtaining a performance quantification value corresponding to the initial production scheduling task according to the initial production scheduling task and the simulation model; Based on the performance quantification value of each of the initial production scheduling tasks, a target production scheduling task is determined from the multiple initial production scheduling tasks.

3. The method according to claim 2, characterized in that The step of determining a target production scheduling task from the plurality of initial production scheduling tasks based on the performance quantified value of each of the initial production scheduling tasks includes: The initial production scheduling task corresponding to the maximum performance quantization value is determined as the target production scheduling task.

4. The method according to claim 2 or 3, characterized in that The constructing of the simulation model based on the preset discrete event simulation strategy, the parameter information and the processing information includes: Determine the main track information table, processing site information table, and storage location information table of the production line according to the parameter information and the processing information; The simulation model is constructed based on the preset discrete event simulation strategy, the main track information table, the processing site information table and the storage location information table.

5. The method according to claim 2 or 3, characterized in that The simulation model includes a main track sub-model, a buffer area sub-model, and a production process sub-model; obtaining a performance quantization value corresponding to the initial production scheduling task based on the initial production scheduling task and the simulation model includes: Obtaining the average number of main track on-track corresponding to the initial production scheduling task through the main track sub-model; Obtaining the cache rod utilization rate and product return times corresponding to the initial production scheduling task through the cache area sub-model; Obtaining the production cycle corresponding to the initial production scheduling task through the production process sub-model; The average number of on-track main rails, the cache rod utilization rate, the product reflow times, and the production cycle are weighted according to preset weights to obtain the performance quantification value.

6. The method according to claim 1, characterized in that The method further comprises: Based on the performance quantification value of each of the initial production scheduling tasks, the target production scheduling tasks are optimized.

7. A production scheduling task determination device, characterized in that: The device comprises: A production information acquisition module is used to acquire a plurality of initial production scheduling tasks; the plurality of initial production scheduling tasks are different production scheduling tasks generated based on parameter information of each processing node on the production line and processing information of the product to be processed; A simulation model construction module is used to construct a simulation model based on a preset discrete event simulation strategy, the parameter information and the processing information; the simulation model is used to simulate the performance quantification value of the initial production scheduling task on the production line; The production scheduling task simulation module is used to input the multiple initial production scheduling tasks into the simulation model respectively, and determine the target production scheduling task from the multiple initial production scheduling tasks.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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