WO2023202326A1

WO2023202326A1 - Data processing method and related apparatus

Info

Publication number: WO2023202326A1
Application number: PCT/CN2023/084017
Authority: WO
Inventors: 艾又琼; 孙磊; 刘复兴; 金啸宇; 王亮; 明威
Original assignee: 华为技术有限公司
Priority date: 2022-04-18
Filing date: 2023-03-27
Publication date: 2023-10-26
Also published as: CN116976576A

Abstract

Disclosed in the present application are a data processing method and a related apparatus, which are used for processing input data to generate a production scheduling plan in a multi-factory scenario, such that production conflicts can be reduced by using the production scheduling plan to perform production scheduling, the inventory cost is reduced, and the inventory availability rate is improved. The solution comprises: firstly, acquiring input data; constructing a target model on the basis of the input data, wherein a supply constraint condition in the target model is established on the basis of a manufacturing period and material list of a target product and transfer data between a plurality of nodes in a supply system of the target product, an objective function takes the production priority of the target product into consideration, production scheduling is driven on the basis of a service target, and the supplies of a plurality of factories are combined into a whole by means of the supply constraint condition; and finally, under the limitation of the constraint condition and the drive of the objective function, solving the target model which represents the overall production scheduling problem of the plurality of factories, so as to obtain a production scheduling plan with regard to the plurality of factories.

Description

Data processing methods and related devices

This application claims priority to the Chinese patent application filed with the China Patent Office on April 18, 2022, with the application number CN202210405762.4 and the application title "Data processing method and related devices", the entire content of which is incorporated into this application by reference. middle.

Technical field

This application relates to the field of data processing technology, and specifically to a data processing method and related devices.

Background technique

Production scheduling is the formulation of production plans. It is the work of enterprises to make overall arrangements for production tasks and specifically formulate the varieties, quantities, and progress plans of production products. Production scheduling is an important part of the enterprise's business plan and an important basis for the enterprise's production management. It is not only an important means to achieve the enterprise's business objectives, but also the basis for organizing and guiding the enterprise's production activities in a planned manner. At the same time, the reasonable arrangement of production plans is also conducive to improving production organization.

In recent years, the manufacturing networks of many manufacturing companies have gradually expanded across the globe, consisting of global suppliers, manufacturing and assembly plants, and outsourcing entities. This requires considering multi-factory factors when scheduling production; in the context of multi-factory production, the production schedule needs to simultaneously schedule all production operations in different factories in each cycle. In the existing technology, manufacturing enterprises decouple the production scheduling problem of multiple factories into multiple single-factory sub-problems, then solve the sub-problems individually, and finally combine the solution results of multiple sub-problems to generate a final production scheduling plan.

In a multi-factory scenario, although the methods using the existing technology have obtained corresponding solutions to each sub-problem, in actual production scheduling, the production activities of each factory are interdependent, so according to the existing technology, the Production scheduling plans are prone to production conflicts, which leads to increased inventory costs and low inventory completion rates.

Contents of the invention

This application provides a data processing method and related devices for processing data to generate a production schedule, so that when using the production schedule for production, it can reduce production conflicts, reduce inventory costs, and improve the inventory completeness rate.

The first aspect of this application provides a data processing method, which is applied to a computer device. The computer device may specifically be a terminal, a server, or other computer device with data processing capabilities. The following description takes application to a server as an example.

When scheduling production in a multi-factory scenario, the server can first obtain input data. Among them, the input data is data related to the production of the target product by multiple factories. The input data includes the demand for the target product, the transfer data between multiple nodes in the supply system of the target product, and the list of materials required for the processing of the target product. (bill of material, BOM) manufacturing cycle; the transfer data is used to indicate the path and time of material transportation. The multiple nodes include factory nodes and warehouse nodes.

Among them, the target product refers to the product that is finally delivered to the demander. The BOM required for the processing of the target product includes the BOM of the target product and the BOM of all levels of sub-components of the target product. It can be understood that materials used for direct processing into target products are first-level sub-components, materials used for direct processing into first-level sub-components are second-level sub-components, and so on.

Among them, the supply system refers to the supply network composed of subordinate nodes related to the production of target products by manufacturing enterprises, and the unified coding system of materials transported between various subordinate nodes. The subordinate nodes include factory nodes, warehouse nodes and sales nodes. Sales node; the materials include products, middleware, raw materials, components and consumables.

Among them, the manufacturing cycle required for processing the target product refers to the processing cycle corresponding to each BOM required for processing the target product.

Among them, the path of material transportation refers to the sequence of nodes through which the material transportation passes, and the actual path between two nodes in adjacent sequences, such as from the node in A to B, and then from B to C. .

After obtaining the input data, the server can build a target model based on the input data, where the target model includes supply constraints based on the decision variables and an objective function based on the decision variables.

Among them, the supply constraint condition is established based on the BOM, the manufacturing cycle and the transshipment data. The supply constraint condition is used to constrain the inventory status of the factory node and the warehouse node to remain stable. The inventory status is based on the manufacturing cycle and the transshipment data. Decision, the decision variable is obtained based on the demand of the target product, and the objective function is used to indicate the optimization goal of the production scheduling plan of the target product.

Keeping the inventory status stable means that the inventory sluggishness rate of all coded materials in the inventory is kept as low as possible, so that the process of producing the target product can achieve the balance of supply and demand of production as much as possible, that is, the production of the current factory production line. The materials meet production needs and market demand as accurately as possible.

It can be seen that the supply constraints actually restrict the inventory status of materials in all nodes of the entire supply system, so that the materials in all nodes can maintain a stable state as much as possible, reduce the sluggish backlog of materials, and thereby reduce inventory costs and production risks.

Finally, the server can call the solver to solve the target model and obtain the production schedule.

Among them, the production scheduling plan includes the processing volume of the target product processed by the factory node, and the transportation volume of materials corresponding to the target product between the multiple nodes.

In this application, in the scenario of multi-factory scheduling, the server obtains input data and builds a target model based on the input data. The supply constraints in the target model are based on the manufacturing cycle of the target product and the supply system of the target product. The transfer data between multiple nodes is established; the supply constraints of the multiple factories are combined into a whole, and finally the target model representing the overall production scheduling problem of the multiple factories is solved to obtain the information about the multiple factories. The production scheduling plan of each factory can reduce production conflicts between factories, reduce inventory costs, and improve the inventory completeness rate.

In a possible implementation, the input data also includes the production line material relationship of the factory node; the transshipment data includes transshipment paths and transshipment cycles; and the target model is built based on the input data, including: based on the BOM, the production line According to the material relationship and the transfer path, the target factory node for processing the target product and the target warehouse node for storing the corresponding materials of the target product are obtained from the multiple nodes; each target factory node and the target warehouse node are established based on the manufacturing cycle and the transfer cycle. The material inventory equation of the target warehouse node serves as the supply constraint.

Among them, the transshipment path refers to the path of material transportation, and the transshipment cycle refers to the time for materials to be transported between two nodes.

Among them, the production line material relationship is used to indicate the materials required for production and the materials obtained from production of the production line.

Among them, the material inventory equation is used to constrain the inventory status of each material in the factory node or warehouse node to remain stable in the current period.

In a possible implementation, the server can first establish the information of all nodes in the supply system based on the input data. The material inventory equation is then filtered from the material inventory equations of all nodes to obtain the target material inventory equation with complete equation data as the supply constraint.

In a possible implementation, establishing a material inventory equation for each target factory node and target warehouse node based on the manufacturing cycle and the transfer cycle as the supply constraint includes: according to the BOM network and the manufacturing cycle, The new processing items and processing consumption items of the target factory node are calculated; according to the transshipment network and the transshipment cycle, the new transfer items and transfer-out consumption items of the target factory node and the target warehouse node are calculated; according to the The inventory input is calculated by processing the new item and the transferred-in new item, and the inventory output is calculated based on the processing consumption item and the transfer-out consumption item; substitute the inventory input and the inventory output into the preset inventory relationship formula, Establish the material inventory equation as the supply constraint.

Among them, according to the BOM, production line material relationship and manufacturing cycle, the new processing items and processing consumption items of the production line of the target product can be obtained for each target factory node; according to the target factory node, the target warehouse node and the transfer path and In the transfer cycle, you can get the transferred-in new items of materials corresponding to the target product that are transferred into each target factory node or target warehouse node, as well as the transferred-out consumption items of the transferred materials corresponding to the target product.

The inventory relationship equation can be obtained according to preset supply matching business rules, and the inventory relationship equation is one of the material inventory equations.

In a possible implementation, the input data includes demand priority information of the target product; building the target model based on the input data includes: establishing an objective function based on the demand priority information.

The demand priority information is used to indicate the priority of the target product in scheduling and production.

In a possible implementation, the input data also includes business goals; establishing the goal function based on the demand priority information includes: establishing an initial function based on the business goal, and the initial function is a demand priority that does not consider the target product. A function that indicates the optimization goal of the production schedule of the target product; determines a penalty coefficient based on the demand priority information, and the penalty coefficient is used to adjust the priority of each decision variable in the initial function under the business goal; The penalty coefficient is assigned to the corresponding decision variable in the initial function to obtain the objective function.

Among them, the business goal is used to indicate the optimization direction of production scheduling. Therefore, it can be understood that there are decision variables in the initial function established according to the business goal. The server can obtain the business goal by solving the values of each decision variable in the initial function. Under this condition, the optimized production scheduling plan does not consider the demand priority of the target product.

Among them, the decision variable corresponds to the demand of the target product, and the penalty coefficient corresponds to the demand priority of the target product. Therefore, the decision variable in the initial function has a corresponding relationship with the penalty coefficient.

In this application, by combining the demand priority information with the initial function, the priority factor can be considered when defining the target function, so that the server can prioritize high-priority demands when scheduling production.

In this application, the priority of the decision variables is quantified by determining the penalty coefficient based on the demand priority information, so that the server can perform priority-related operations when scheduling production according to the objective function, so that the final obtained The production schedule meets the requirements of demand priorities as much as possible.

In a possible implementation, before determining the penalty coefficient based on the demand priority information, the method further includes: calculating the feasible region of the preset constraint element function based on the target constraint factor corresponding to the decision variable, the target constraint The elements are constraint elements that affect the priority of the decision variable under the business goal when solving the initial function; the penalty coefficient is determined based on the demand priority information, including: based on the demand priority of the decision variable and the constraint element function feasible region, determine the penalty coefficient.

Among them, the constraint elements refer to the constraints of decision variables when performing optimization calculations of business objectives.

Among them, the constraint element function is used to calculate the penalty coefficient.

In this application, by determining the penalty coefficient based on the demand priority and the feasible region of the constraint factor function, compared with the method of directly determining the penalty coefficient based on the demand priority, the penalty coefficient can be made more accurate, and a more reasonable production schedule can be obtained plan.

In another possible implementation, the server can determine the penalty coefficient corresponding to each priority based on the demand priority information, and then establish an objective function based on the penalty coefficient, the target product demand and the business goal.

In another possible implementation, the demand priority information includes the demand priority of each decision variable; determining a penalty coefficient according to the demand priority information includes: determining a corresponding preset penalty coefficient according to the demand priority.

In a possible implementation, the input data is obtained, including: production schedules constructed from an enterprise resource planning (ERP) system or a manufacturing execution system (MES) based on each node in the supply system. The input data is obtained from the computer network.

Among them, the server can automatically collect the above data of each node in the supply system through the production scheduling computer network, or can obtain the above data from the data file input by the planning and dispatching specialist of each node through the production scheduling computer network; these data can be processed After preprocessing, data in a standard format required by the server to execute the data processing method provided by this application is obtained.

In one possible implementation, the target model also includes demand matching constraints and capacity occupancy constraints.

Among them, the demand matching constraints are obtained based on the demand matching rules in the business rules, and are used to constrain the matching of new demand in the current period and the satisfied demand in the current period; the capacity occupancy constraints are obtained based on the capacity occupancy rules in the business rules, and are used to constrain the processing volume in the current period. match the actual occupied capacity.

In one possible implementation, the target model also includes holiday constraints.

Among them, the holiday constraint conditions are obtained based on the holiday rules in the business rules. The holiday constraint conditions are used to constrain the relevant values of production, transshipment and procurement to be 0 when the date is a holiday.

A second aspect of this application provides a data processing device, including:

The acquisition unit is used to acquire input data, which is data related to the target product produced by multiple factories. The input data includes the demand for the target product, the transfer data between multiple nodes in the supply system of the target product, and The bill of materials BOM and manufacturing cycle required for the processing of the target product. The transshipment data is used to indicate the path and time of material transportation. The multiple nodes include factory nodes and warehouse nodes;

A modeling unit for constructing a target model based on the input data, the target model including supply constraints and an objective function based on decision variables, wherein the supply constraints are established based on the manufacturing cycle and the transshipment data, and the supply constraints are It is used to constrain the inventory status of the factory node and the warehouse node to remain stable. The inventory status is determined based on the manufacturing cycle and the transshipment data. The decision variable is obtained based on the demand for the target product. The objective function is used to indicate the target product. The optimization goal of the production scheduling plan;

The solving unit is used to call the solver to solve the target model and obtain the production schedule.

In a possible implementation, the input data also includes the production line material relationship of the factory node; the transshipment data includes the transshipment path and transshipment cycle; the modeling unit is specifically used to: based on the BOM, the production line material relationship and The transfer road Path, obtain the target factory node that processes the target product and the target warehouse node that stores the materials corresponding to the target product from the multiple nodes; establish a relationship between each target factory node and the warehouse node based on the manufacturing cycle and the transfer cycle. The material inventory equation serves as this supply constraint.

In a possible implementation, the modeling unit is specifically used to: calculate new processing items and processing consumption items of the target factory node based on the BOM network and manufacturing cycle; calculate based on the transshipment network and transshipment cycle. The transfer-in new items and transfer-out consumption items of the target factory node and the target warehouse node; the inventory input is calculated based on the new processing items and the transfer-in new items, and the inventory input is calculated based on the processing consumption items and the transfer-out consumption items. Calculate the inventory output; substitute the inventory input and inventory output into the preset inventory relationship equation, and establish the material inventory equation as the supply constraint.

In a possible implementation, the modeling unit is specifically used to: establish the objective function based on the demand priority information.

In a possible implementation, the input data also includes business goals; the modeling unit is specifically used to: establish an initial function based on the business goals, and the initial function indicates the target product without considering the demand priority of the target product. function of the optimization goal of the production scheduling plan; determine the penalty coefficient based on the demand priority information, and the penalty coefficient is used to adjust the priority of each decision variable in the initial function under the business goal; assign the penalty coefficient to the initial The corresponding decision variables in the function are used to obtain the objective function.

In a possible implementation, the device further includes: a calculation unit, used to calculate the feasible region of the preset constraint element function according to the target constraint element corresponding to the decision variable, where the target constraint element is when solving the initial function Constraint elements that affect the priority of the decision variable under the business goal; the modeling unit is specifically used to determine the penalty coefficient based on the demand priority of the decision variable and the feasible region of the constraint element function.

In a possible implementation, the acquisition unit is specifically configured to: acquire the input data from a production scheduling computer network constructed based on the ERP system or MES of each node in the supply system.

A third aspect of the present application provides a computer device, including: a processor and a memory; instruction operations or codes are stored in the memory; the processor is configured to communicate with the memory and execute instructions in the memory Operations or code to perform the method described in the first aspect above. Optionally, the computer device may also include an input/output (I/O) interface.

A fourth aspect of the present application provides a computer-readable storage medium. The computer-readable storage medium includes instructions. When the instructions are run on a computer, they cause the computer to execute the method described in the first aspect.

A fifth aspect of the present application provides a computer program product, which is characterized in that it includes computer readable instructions. When the computer readable instructions are run on a computer device, the computer device causes the computer device to execute the method described in the first aspect. method.

A sixth aspect of the present application provides a chip system. The chip system includes a processor for supporting the data processing device of the second aspect to implement the functions involved in the above aspect, such as generating or processing the method of the first aspect. The data and/or information involved. In a possible implementation, the chip system further includes a memory, which is used to store necessary program instructions and data of the data processing device to implement the function of the first aspect. The chip system can be composed of chips or include chips and other discrete devices.

The solutions provided by the above-mentioned second aspect to the sixth aspect are used to implement or cooperate with the method provided by the above-mentioned first aspect, and therefore can achieve the same or corresponding beneficial effects as those of the first aspect, and will not be described again here.

Description of the drawings

Figure 1 is a schematic diagram of the application architecture of the data processing method provided by the embodiment of the present application;

Figure 2 is a schematic diagram of a deployment environment of the data processing device provided by the embodiment of the present application;

Figure 3 is a schematic diagram of another deployment environment of the data processing device provided by the embodiment of the present application;

Figure 4 is a schematic structural diagram of a data processing device provided by an embodiment of the present application;

Figure 5 is a schematic flowchart of a data processing method provided by an embodiment of the present application;

Figure 6 is a schematic diagram of input data processing of a data processing method provided by an embodiment of the present application;

Figure 7 is a schematic diagram of the network architecture of the production scheduling computer network provided by the embodiment of the present application;

Figure 8 is a schematic diagram of a supply system provided by an embodiment of the present application;

Figure 9 is a graph showing the comparison results of the cumulative inventory completeness rate provided by the embodiment of the present application;

Figure 10 is a schematic structural diagram of another data processing device provided by an embodiment of the present application;

Figure 11 is a schematic structural diagram of a computer network provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clear, the embodiments of the present application are described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Persons of ordinary skill in the art will know that with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

The terms "first", "second", etc. in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the descriptions so used are interchangeable under appropriate circumstances so as to enable the embodiments to be practiced in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules and need not be limited to those explicitly listed. Those steps or modules may instead include other steps or modules not expressly listed or inherent to the processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time/logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can be implemented according to the purpose to be achieved. The order of execution can be changed for technical purposes, as long as the same or similar technical effect can be achieved. The division of units presented in this application is a logical division. In actual applications, there may be other divisions. For example, multiple units may be combined or integrated into another system, or some features may be ignored. , or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed may be through some interfaces, and the indirect coupling or communication connection between units may be electrical or other similar forms. There are no restrictions in the application. Furthermore, the units or subunits described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed into multiple circuit units, and some or all of them may be selected according to actual needs. unit to achieve the purpose of this application plan.

With the improvement of economic level, the manufacturing network of many manufacturing companies continues to expand, and for cost considerations, corresponding factories are usually established near the production areas of raw materials, so that the various factories of manufacturing companies are in different geographical locations. Set. Multiple factories of a manufacturing company can process and generate products independently, or they can use the transfer path between factories to obtain semi-finished products processed from local raw materials by factories in other regions and reprocess them to obtain products.

In the context of the joint production of multiple factories of the above-mentioned manufacturing enterprises, production activities also have the characteristics of multi-cycle, multi-priority and sharing.

Specifically, the multi-period feature means that the arrangement of a production activity can consider multiple periods, such as daily or weekly production schedules in the future.

The multi-priority feature means that product demand has multiple priorities. The priority is determined based on attributes such as product demand source, demand type, demand time, demand quantity, and product characteristics. Production scheduling needs to determine the production sequence of products based on demand priority.

Sharing features include material sharing, supply network sharing and production line sharing. Material sharing means that multiple products share the same material; supply network sharing means that one warehouse can supply to multiple different factories at the same time, and materials can be shared between different factories; production line sharing means that the same production line in the factory can produce Processes a variety of products.

It can be seen that in a multi-factory context, the connections and dependencies between various factories are greatly strengthened. In the existing technology, the joint production activity arrangement problem of multiple factories is divided into sub-problems of a single factory and solved, and finally the multiple single factory production schedules are combined into an overall production schedule, which will lead to an increase in production conflicts. Production activities cannot proceed smoothly, which leads to high inventory costs and low inventory consistency, which affects the efficiency of manufacturing companies.

Therefore, there is an urgent need for a method of generating a production scheduling plan that can solve the above problems.

Embodiments of the present application provide a data processing method and related devices for processing data to generate a production schedule, so that when using the production schedule for production, production conflicts can be reduced, inventory costs can be reduced, and inventory completion rates can be improved.

In the supply chain planning management system, the market makes a demand plan based on sales forecasts, and the manufacturer's product department makes a demand forecast for the main production plan based on market demand and current material supply capacity, so that the manufacturer's production planning department makes a demand forecast based on this. Further consider various constraints such as materials, production capacity, demand priority, and supply network to coordinate and optimize the production plan for the next period of time. In this management system, the production planning department needs to comprehensively consider the balance of upstream and downstream supply and demand, and at the same time consider the factory's production capacity arrangement to schedule production, output the production scheduling plan, and lock in materials and production capacity in advance. On the basis of the production schedule, the product department can output the supply plan for specific products and make supply commitments.

Please refer to Figure 1, which is a schematic diagram of the application architecture 100 of the data processing method provided by the embodiment of the present application. As shown in Figure 1, the application architecture 100 includes a data processing device 110, a master production schedule (MPS) module 120, an ERP system or MES 130 of supply system nodes (such as factory nodes, warehouse nodes and sales nodes), Allocation commitment module 140, supplier collaboration module 150, order commitment 160, inventory optimization (IO) module 170, material requirement planning (material requirement planning, MRP) module 180, demand management module 190, demand reduction module 191, ERP order management module 192, and ERP inventory or purchasing management module 193.

The data processing device 110 is used to execute the data processing method provided by this application, and is specifically used to receive the demand forecast of the master production plan made by the MPS module 120 and the production data provided by the ERP system or MES130 of the supply system node to schedule production and obtain the schedule. production plan; also used to send the production schedule to the MPS module 120.

The data processing device 110 is also used to update the available-to-promise (ATP) of the product to the distribution commitment module 140 according to the obtained production schedule, send a corresponding material delivery request to the supplier collaboration module 150, and The order commitment module 160 makes delivery commitments for existing orders.

The MPS120 module is used to obtain the demand data provided by the demand offset module, the material inventory data provided by the IO module 170, and the procurement commitment provided by the supplier collaboration module, and then make a demand forecast for the master production plan based on the current market demand and material supply capacity. , and sends the demand forecast to the data processing device 110 . The MPS120 module is also used to commit the available supply amount ATP of the product to the allocation commitment module.

The ERP system or MES130 of the supply system node is used to provide production data, material inventory data and transfer data with other nodes of the corresponding node.

The allocation commitment module 140 is used to feed back the ATP of the product to the demand management module 190, and send to the order commitment module 160 the net predicted ATP obtained after excluding the supply of items such as safety stock and stocking plans.

The supplier collaboration module 150 is used to obtain inventory information and procurement information from the ERP inventory management module or procurement management module 193, provide the inventory information and the procurement information to the IO module 170, and provide the MPS with the inventory information and the procurement information based on the inventory information and the procurement information. Module 120 proposes a purchase commitment.

The order commitment module 160 is used to receive market inquiries and make commitments to the market based on the acquired net predicted ATP and the promised delivery date of existing orders; it is also used to store committed orders in the ERP order management module 192 .

The IO module 170 is used to perform inventory management based on the material demand information provided by the MRP module, the purchasing information and inventory information provided by the supplier collaboration module 150, and the production schedule or production plan provided by the MPS module 120.

The MRP module 180 is used to calculate the demand and demand time for materials required for product production based on the offset demand data provided by the demand offset module 191 and the inventory information provided by the IO module, and determine the processing progress and procurement schedule of the product.

The demand management module 190 is used to generate demand forecasts based on the order information provided by the ERP order management module 192, the demand forecast obtained from the sales and operations forecast process model (S&OP), the product demand of the manufacturer's internal safety stock or stocking plan, and distribution. The available supply ATP provided by the commitment module 140 calculates and manages the demand for the product; and provides the available supply ATP that non-order items need to occupy to the allocation commitment module 140, so that the allocation commitment module calculates the net forecast ATP.

The demand reduction module 191 is used to perform demand reduction based on the demand data provided by the demand management module 190 and the current supply amount ATP of the product, to obtain net forecast demand data.

The ERP order management module 192 is used to provide order information from the producer.

The ERP inventory management module or procurement management module is used to provide inventory information and procurement information from the producer.

It is understandable that due to the shared characteristics of production activities in a multi-factory context, the same material may play different roles in different nodes or different production lines. For example, material A is used as a raw material for processing in production line 1 and in production line 1. 2 consumables used as auxiliary production. Therefore, in the embodiment of this application, when executing the data processing method provided by this application, a unified coding system is used to uniformly code various materials, so that the data of each node in the supply system can be processed uniformly.

It is worth noting that Figure 1 is only a schematic diagram of an application architecture provided by an embodiment of the present application, and the positional relationship between the devices, devices, modules, etc. shown in Figure 1 does not constitute any limitation.

The modules or devices shown in Figure 1 can be independently arranged on the computer device, or multiple modules or devices can be jointly deployed on the same computer device, which is not specifically limited here. The computer device may be a terminal, a server, or other computer device with data processing capabilities.

Referring to Figures 2 and 3, the data processing device 110 can be deployed on a server or in the cloud.

Specifically, as shown in Figure 2, in one embodiment, the data processing device 110 provided by this application can also be deployed on network nodes in a centralized deployment manner or a distributed deployment manner, such as an enterprise's computer room or a research institute server. Or supply chain office server, etc., all data is transmitted through the Internet. After the server obtains the data, it establishes a target model based on the data, then uses a solver to solve the target model to obtain the production schedule, and finally outputs the production schedule to the human-computer interaction interface of the server.

In another embodiment, the data processing device 110 can be deployed in the cloud. After the supply system node uploads data to the cloud, the data processing device 110 uses the solver and related software services provided by the cloud to complete the establishment and creation of the target model in the cloud. Solve, and finally return the solved production schedule to the corresponding supply system node.

The application system framework and deployment environment of the data processing method provided by the embodiment of the present application have been described above. The internal structure of the data processing device provided by the embodiment of the present application will be described below. Please refer to Figure 4, which is a schematic structural diagram of a data processing device in an embodiment provided by this application. The data processing device includes an input unit 401, a data pre-processing unit 402, a modeling and solving unit 403, a data post-processing unit 404 and Output unit 405.

The input unit 401 is used to input input data from various nodes in the supply system and from other devices or modules in the application system to the data preprocessing unit 402.

The data preprocessing unit 402 is used to convert the input data input by the input unit 401 into a unified preset input format and standard, which may specifically include unified material coding data, planning time and work calendar, supply and production capacity data, transfer paths and Offset data.

The modeling and solving unit 403 is used to perform mathematical modeling and solving based on the preprocessed input data to obtain a production schedule.

The data post-processing unit 404 is used to analyze and calculate based on the production schedule to obtain the recommended manufacturing table, material transfer table, beginning and ending inventory table, production capacity usage detailed table, etc. These detailed tables are used to reflect demand satisfaction, supply and usage, and material shortages. . The data post-processing unit 404 is also used to obtain a pegging table based on the foregoing table.

Based on the above table, the content finally output by the data post-processing unit 404 through the output unit 405 includes: recommended task orders, demand satisfaction and delays, material consumption details, production capacity utilization details, material transfer details, ending inventory, and supply and demand matching details.

It is understandable that the production staff can adjust the production schedule based on these output contents.

Based on the application architecture shown in Figure 1, embodiments of the present application also provide a data processing method. Please refer to FIG. 5 , which is a schematic flowchart of a data processing method in an embodiment. As shown in Figure 5, the data processing method includes the following steps 501-503.

501. The data processing device obtains input data.

Among them, the input data is data related to the production of the target product by multiple factories. The input data includes the demand for the target product, the transfer data between multiple nodes in the supply system of the target product, and the BOM and BOM required for the processing of the target product. Manufacturing cycle; this transshipment data is used to indicate the path and time of material transportation. The multiple nodes include factory nodes and warehouse nodes.

More specifically, the requirements of the target product include the type of requirement, time of requirement, priority of requirement, quantity of requirement, priority of requirement and ID of requirement.

The BOM required for the processing of the target product includes the material's parent code, sub-item code, BOM usage, BOM effective date and expiry date, etc.

The input data also includes the work calendar and production capacity information of each factory in the multiple factories, including factory location ID, standard date, time, whether it is a holiday, product series, production capacity code and public capacity information.

The input data also includes supply data, which includes factory supply data and warehouse supply data, specifically including the type of material supply, supply time, supply priority, supply quantity, and supply ID.

Referring to Figure 7, in one possible implementation, the data processing device obtains the input data from a production scheduling computer network built based on ERP systems of multiple factories. The data processing device can connect the ERP system, MES or other software that manages input data at factory nodes, assembly plant nodes, warehouse nodes and sales nodes in the entire supply system through the production scheduling computer network, and obtain corresponding data.

In one possible implementation, the data processing device converts the input data into a unified preset input format and performs standard preprocessing before performing step 502.

502. The data processing device builds a target model based on the input data.

Please refer to FIG. 6 , which is a schematic diagram of input data processing when building a target model based on the input data in one embodiment, specifically including steps 601 to 604.

601. The data processing device defines decision variables based on the input data.

The data processing device can define decision variables based on the target product demand in the input data and the coding of the target product. For example, the processing amount of each target product can be defined as X. If the factory that processes product A includes factory 1 and factory 2, Then the main decision variables can be defined as X _A1 and X _A2 .

In addition, before establishing the objective function, the data processing device can also define auxiliary decision variables based on the target product requirements and business goals, such as the demand satisfaction quantity and the delay quantity. The demand satisfaction quantity refers to the demand satisfaction quantity at a certain point in time or a certain time period. The satisfied quantity of a demand, the delayed quantity refers to the unsatisfied quantity of the target product produced after the end of the current period relative to a certain demand.

602. The data processing device establishes constraints based on the input data.

The data processing device can establish constraints based on preset business rules and decision variables, as well as transshipment data, production line information and target product BOM in the input data.

Among them, the business rules refer to the description of business definitions and constraints, which are used to maintain the business structure, control and influence production behavior.

Among them, the transshipment data includes transshipment cycles and transshipment paths; the production line information includes production line material relationships and manufacturing cycles of each of the multiple factories.

Among them, supply constraints can be established according to the supply matching rules in the business rules, as well as the BOM in the target product information, the production line information, the transshipment path and the transshipment cycle, and the supply constraint conditions are used to constrain the factory nodes. And the inventory status of the warehouse node remains stable.

Keeping the inventory status stable means that the inventory sluggishness rate of all coded materials in the inventory is kept as low as possible. It can be seen that the supply constraints actually constrain the inventory status of materials in all nodes of the entire supply system.

In a possible implementation, the data processing device can first obtain the target factory node that processes the target product and the node that stores the corresponding materials of the target product from the multiple nodes based on the BOM, the production line material relationship, and the transfer path. The target warehouse node; and then based on the manufacturing cycle and the transfer cycle, the material inventory equation of each target factory node and target warehouse node is established as the supply constraint.

In a possible embodiment, the BOM of target product E is processed into a processing relationship of raw material A and raw material B being processed into semi-finished product D, and semi-finished product D and raw material C being processed into target product E; and, through the production line material relationships of the multiple factories It is known that factory 1 can process A and B into D, and factory 2 can process D and C into E. At the same time, the three raw materials A, B, and C are all in the central warehouse. According to the transfer path data, it is known that the central warehouse and Direct transshipment is possible between factory 1 and factory 2, and direct transshipment is also possible between factory 1 and factory 2.

Please refer to Figure 8 for details. Figure 8 is a schematic diagram of the supply system in this embodiment. Among them, each node plane represents a corresponding BOM. The points in the node plane are the codes of a material. The arrows in the node plane are used to indicate the BOM relationship. The arrows between different node planes are used to indicate the transfer relationship. There may be two-way Transport, the unidirectional arrow shown in Figure 8 indicates unidirectional transport.

Referring to Figure 8, we can see that the target warehouse node is the central warehouse, and the target factory nodes are factory 1 and factory 2. The central warehouse transfers A and B to factory 1, and transfers C to factory 2; factory 1 processes A and B into semi-finished product D, and transfers D to factory 2; factory 2 processes raw material C and semi-finished product D into target product E .

On the basis of clarifying the processing flow of the target product, the material inventory equation corresponding to the target factory node and the corresponding target warehouse node can be established as the supply constraint based on the manufacturing cycle and corresponding transfer cycle of the target product.

Among them, the inventory relationship corresponding to the supply matching rule is:
Ending inventory = Beginning inventory + Inventory input - Inventory output Ending inventory = Beginning inventory + New supply at this node + Transfer in from other nodes - Consumption at this node - Transfer out to other nodes

According to the material inventory equation obtained by further expanding the inventory relationship, combined with the coding of various materials in the unified coding system, the material inventory equation can be expressed as:

Among them, i represents the material code, s represents the factory node or warehouse node, p represents the demand priority, t represents time, L represents the processing cycle, and PL represents the purchasing cycle. The specific definition of variables is as follows: supply quantity Supply _ist , demand satisfaction quantity D _istp , production quantity X _ist , ending inventory N _ist , transshipment quantity Y _is1s2t , recommended purchase quantity DummyPO _ist , parent material Parent. BOD stands for supply system. I is an indicator function that outputs 1 when the input is True and 0 when the input is False.

In a possible implementation, establishing a material inventory equation for each target factory node and target warehouse node based on the manufacturing cycle and the transfer cycle as the supply constraint includes: calculating the target based on the manufacturing cycle The new processing items and processing consumption items of the factory node; according to the transfer cycle, the target factory node and the The transferred-in new items and transferred-out consumption items of the target warehouse node; the inventory input is calculated based on the new processing items and the transferred-in new items, and the inventory output is calculated based on the processing consumption items and the transferred-out consumption items; Substitute the inventory input and inventory output into the preset inventory relationship equation, and establish the material inventory equation as the supply constraint.

Among them, according to the material relationship and manufacturing cycle, the processing new items and processing consumption items of the production line of the target product can be obtained for each target factory node; according to the target factory node, the target warehouse node, and the transfer cycle, each target factory node can be obtained The new imported items of raw materials or semi-finished products corresponding to the target product that are transferred into the target factory node or target warehouse node, and the transferred-out consumption items of raw materials or semi-finished products corresponding to the target product.

For example, the complete machine (FG) and the lower bare metal (MF) are processed in factory A, and the processing time is 2 days respectively; but the structural parts required for processing the bare metal are not only in factory A, but also in the central warehouse, from the central warehouse to The transfer cycle of factory A is 2 days. Therefore, taking the date of transfer of structural parts as the current period, the inventory balance equation as shown below can be constructed.

For central warehouse:

Structural Parts Part: Beginning inventory + committed supply quantity - warehouse transfer (T-2) = ending inventory

For factory A:

(1) Structural Parts:

Beginning inventory + purchase quantity + warehouse transfer (T) - demand satisfaction (Part) - quantity of manufacturing MF (T-2) * BOM usage = ending inventory

(2) Bare metal MF:
Beginning inventory + quantity of work in progress (MF) + new manufacturing quantity (MF, T) - demand satisfaction (MF) - quantity of manufactured FG
(T-2)*BOM usage=ending inventory

(3) Complete machine FG:
Beginning inventory + work in progress quantity (FG) + new manufacturing quantity (FG, T) - demand satisfaction (MF) = ending inventory

Among them, complete machines and bare metals are processed parts, inventory inputs include previously unfinished work-in-progress and new manufacturing in the current period, and inventory outputs include satisfaction of own needs; inventory inputs for bare metals are similar to complete machines, except for their own needs (independent orders) etc.) are satisfied with inventory, the parent item (complete machine) processing will also consume bare metal inventory. Structural parts are purchased parts, and their inventory inputs include the arrival of new purchases from the factory and supplies transferred from the central warehouse, and their inventory outputs include the satisfaction of their own needs and the consumption of parent items (bare metal). The input of the central warehouse is the supply promised by the supplier in each period, and the output is the supply quantity transferred from the warehouse to the factory.

By introducing the manufacturing cycle and transfer cycle into the material inventory equation, and correlating the supply of different materials in different factories through a unified coding system, the target model built based on the supply constraints can be guided to realize the coordinated production scheduling of multiple factories.

In a possible implementation, the server can first establish the material inventory equations of all nodes in the supply system based on the input data, and then filter the material inventory equations of all nodes to obtain the target material inventory with complete equation data. As this supply constraint, Eq.

In one possible implementation, the target model also includes demand matching constraints, capacity occupancy constraints, and/or holiday constraints.

Among them, the demand matching rule is: new demand in the current period + historical unsatisfied demand = satisfied demand in the current period + accumulated unsatisfied demand in the current period; the production capacity occupation rule is: processing volume in the current period = production capacity utilized in the current period * yield, production capacity utilized in the current period + empty production capacity in the current period =Total production capacity for the current period; holiday rules are: production, transshipment, and procurement on holidays = 0.

Among them, the mathematical equation of demand matching constraints:
Demand _istp +GAP _is(t-1)p =D _istp +GAP _istp

The mathematical equation of capacity occupancy constraints is:

The equation for the holiday constraint condition is:

Among them, i represents coding, s represents factory or warehouse, p represents demand priority, t represents time, and g represents capacity sharing group. The variables are specifically _defined _as follows _: _demand satisfaction _quantity D _istp , cumulative demand delay quantity GAP _itsp , production quantity _isg , capacity short quantity CapLeft _gst , total capacity Capacity _gst .

603. The data processing device establishes an objective function based on the input data.

The data processing device can establish an objective function based on target product requirements, business objectives and requirement priority information in the input data.

In a possible implementation, the data processing device can first establish an initial function based on the business goal; then determine a penalty coefficient based on the demand priority information; and finally assign the penalty coefficient to the corresponding decision variable in the initial function to obtain the goal. function.

The penalty coefficient is used to adjust the priority of each decision variable in the initial function under the business goal.

Wherein, the initial function is a function indicating the optimization objective of the production schedule of the target product without considering the demand priority of the target product.

The demand priority information is used to indicate the priority of the target product during production scheduling.

Specifically, the method for determining the penalty coefficient may be: the data processing device determines the penalty coefficient based on the target approximation corresponding to the decision variable. Constraint elements are used to calculate the feasible region of the preset constraint element function; and then the penalty coefficient is determined based on the demand priority of the decision variable and the feasible region of the constraint element function.

Wherein, the target constraint element is a constraint element that affects the priority of the decision variable under the business goal when solving the initial function.

In one possible implementation of calculating the penalty coefficient, when the penalty coefficient is calculated with reference to the target constraint elements corresponding to all demand priorities, the constraint element function can be:
F(obj ₁ ,obj ₂ ,...,obj _n )＞Max(obj _x /obj _y )(x＞y，x,y∈n)

Among them, obj _n is the target constraint element corresponding to the decision variable with demand priority n, and there are n levels of demand priority.

The corresponding calculation equation of the penalty coefficient is:
Penalty _p =F(obj ₁ ,obj ₂ ,...,obj _n ) ^np

Among them, Penalty _p is the penalty coefficient corresponding to the decision variable with demand priority P.

In another possible implementation of calculating the penalty coefficient, when the penalty coefficient is calculated with reference to the target constraint elements corresponding to adjacent demand priorities, the constraint element function can be:
f(obj _n )＝Penalty _p /Penalty _p+1

The meaning of the parameters in this constraint feature function is consistent with the aforementioned equation and will not be described again here.

The corresponding calculation equation of the penalty coefficient is:
Penalty _p = Penalty _p+1 *f(obj _n )
Penalty _n = Basic Penalty

Among them, BasicPenalty is the preset penalty base.

For example, in a certain order, there are three requirements for target products A, B and C, each of which is 100. The priorities of the three requirements of A, B and C decrease in order, to 1, 2 and 3 respectively. A, B and C share material D, and the corresponding BOM quantities are 10, 5 and 1 respectively. The business goal is to maximize the complete set of target products, that is, to minimize the number of delays in target products. The initial function can be constructed as:

Min GAP _A +GAP _B +GAP _C

Among them, GAP _n is the delayed quantity of the target product.

When the production scheduling goal is to maximize demand satisfaction, these 100 pieces of D will be completely allocated to C, and up to 100 pieces of C can be used together. However, this result violates the principle of demand priority, and the requirements of A and B with a higher priority than C are not satisfied. The key factor leading to this result is the BOM usage involved in the production scheduling target. The unit demand for bottleneck material D by C with low demand priority is smaller than A and B with high demand priority. Therefore, the target constraint element can be determined to be the BOM amount, and obj ₁ =10, obj ₂ =5, and obj ₃ =1 are obtained.

When the first possible implementation is used to calculate the penalty coefficient, it can be calculated that F (obj ₁ , obj ₂ ,..., obj _n )>10, and the data processing device can take any value from the feasible region to calculate the penalty coefficient. , for example 11. When F(obj ₁ , obj ₂ ,..., obj _n )=11, the corresponding penalty coefficients can be calculated as Penalty ₁ =121, Penalty ₂ =11, and Penalty ₃ =1.

When using the second possible implementation to calculate the penalty coefficient, the penalty cost can be constructed first.

Option 1.1: If D is completely used to manufacture A, A can satisfy 10 pieces, GAP _A = 90, GAP _B = 100, GAP _C = 100.

Option 1.2: If D is completely used to manufacture B, B can satisfy 20 pieces, GAP _A = 100, GAP _B = 80, GAP _C = 100.

Option 1.3: If D is completely used to manufacture C, C can satisfy 100 pieces, GAP _A = 100, GAP _B = 100, GAP _C = 0.

By minimizing the penalty cost and guiding the data processing device to prioritize high-priority requirements during production scheduling, the penalty cost of plan 1.1 can be smaller than the penalty cost of plan 1.2, and the penalty cost of plan 1.2 can be smaller than the penalty cost of plan 1.3. You can create a calculation:
P ₁ *90+P ₂ *100+P ₃ *100＜P ₁ *100+P ₂ *80+P ₃ *100
P ₁ *100+P ₂ *80+P ₃ *100＜P ₁ *100+P ₂ *100+P ₃ *0

The calculation results are P ₁ > 2P ₂ and P ₂ > 5P ₃ , and further we can obtain f(obj ₁ ) > 2 and f (obj ₂ ) > 5.

Among them, P _n is the penalty coefficient, and n is the demand priority.

We can assume that f(obj ₁ )=2.1, f(obj ₂ )=5.1, and the penalty base BasicPenalty=1, and then calculate P ₁ =1, P ₂ =5.1, and P ₃ =10.71.

It can be understood that the calculation of the penalty coefficient is based on the calculation equation adopted when the initial function is the Min function, so that the penalty coefficient increases step by step, which can guide the data processing device to give priority to satisfying high-priority needs during production scheduling. . When the initial function is the Max function, the above calculation equation can be changed so that the penalty coefficient is gradually reduced, which can also guide the data processing device to give priority to satisfying high-priority requirements during production scheduling. This is an invention according to the embodiment of the present application. The simple evolution of the idea and the corresponding solution are also within the protection scope of this application and will not be described again here.

The embodiments of this application can quantify the priority of decision variables by determining the penalty coefficient based on demand priority information, so that the server can perform priority-related operations when scheduling production according to the objective function, so that the final result is The production scheduling plan meets the requirements of demand priority as much as possible.

In another possible implementation, the demand priority information includes the demand priority of each decision variable; the data processing device can determine the corresponding preset penalty coefficient according to the demand priority.

Among them, the data processing device can first establish an initial function according to the target product requirements and business goals, then determine the corresponding preset penalty coefficient according to the demand priority, and finally assign the penalty coefficient to the corresponding decision variable in the initial function to obtain the goal function.

For example, after the data processing device determines the change direction of the penalty coefficient based on the initial function, it sets the penalty coefficient with the highest or lowest priority as the base, and then determines the penalty coefficient of the decision variable with demand priority N as demand priority based on the demand priority information. The penalty coefficient of the decision variable with level N+1 is 100 times or one-hundredth of one, thereby guiding decision variables with demand priority N to be given priority when scheduling production.

In another possible implementation, the data processing device can also determine the penalty coefficient corresponding to each priority based on the demand priority information, and then establish an objective function based on the penalty coefficient, the target product demand and the business goal.

It can be understood that there is no fixed execution order of step 602 and step 603, and the two steps can be executed at the same time, or in a specific or random order.

604. The data processing device constructs an objective model based on the decision variable, the constraint condition and the objective function.

Among them, the target model is a mathematical model.

503. The data processing device solves the target model and obtains the production schedule.

In the embodiment of the present application, in the scenario of multi-factory production scheduling, the data processing device obtains input data and generates data based on the input data. Input data to construct a target model. The supply constraints in the target model are established based on the manufacturing cycle of the target product and the transfer data between multiple nodes in the supply system of the target product; through the supply constraints, the multiple factories The inventories are combined into a whole, and finally the target model representing the overall production scheduling problem of the multiple factories is solved to obtain the production scheduling plans for the multiple factories, which can reduce production conflicts between various factories and reduce inventory costs. , improve the inventory completeness rate.

The beneficial effects of the embodiments of the present application will be further described below in conjunction with actual production data. Please refer to Figure 9 and Table 1, which are the cumulative inventory completion rate comparison results provided by the embodiments of the present application.

This application uses three pairs of similar terminal product orders as comparison objects. Each pair of orders is scheduled using the data processing method provided by this application and the existing technology to decouple multi-factory problems into multiple single-factory problem scheduling methods. The cumulative matching rate for a total of 4 weeks in a cycle is calculated. The comparative statistical results are shown in Figure 9 and Table 1.

Table 1

It can be seen that when using the data processing method provided by this application, compared with the existing technology, the average cumulative inventory demand fulfillment rate can be increased by more than 5%, and the cumulative inventory fulfillment rate of 80% of products is improved. Among all product series, Inventory completeness rate improved to nearly 80%.

The data processing method in the embodiment of the present application is described above, and the data processing device in the embodiment of the present application is described below. Please refer to Figure 10. An example of the data processing device 1000 in the embodiment of the present application includes:

The acquisition unit 1001 is used to acquire input data. The input data is data related to the target product produced by multiple factories. The input data includes the demand for the target product and the transfer data between multiple nodes in the supply system of the target product. As well as the bill of materials BOM and manufacturing cycle required for the processing of the target product, the transshipment data is used to indicate the path and time of material transportation. The multiple nodes include factory nodes and warehouse nodes;

The modeling unit 1002 is configured to build a target model based on the input data. The target model includes supply constraints and objective functions based on decision variables, where the supply constraints are established based on the manufacturing cycle and the transshipment data. The supply constraints The conditions are used to constrain the inventory status of the factory node and the warehouse node to remain stable. The inventory status is determined based on the manufacturing cycle and the transshipment data. The decision variable is obtained based on the demand for the target product. The objective function is used to indicate the goal. Optimization goals of product scheduling plans;

The solving unit 1003 is used to call the solver to solve the target model and obtain the production scheduling plan.

In the embodiment of the present application, in the scenario of multi-factory production scheduling, the data processing device obtains input data through the acquisition unit 1001, and builds a target model based on the input data through the modeling unit 1002. The supply constraints in the target model are based on the target product. The manufacturing cycle of the target product, and the transfer data between multiple nodes in the supply system of the target product are established; through the supply constraints, the supply of the multiple factories is combined into a whole, and finally the solving unit 1003 is used to represent the This objective model is used to solve the overall production scheduling problem of multiple factories and obtain the production scheduling plans for the multiple factories, which can reduce production conflicts between factories, reduce inventory costs, and improve the inventory completeness rate.

In a possible implementation, the input data also includes the production line material relationship of the factory node; the transshipment data includes the transshipment path and transshipment cycle; the modeling unit 1002 is specifically used to: based on the BOM, the production line material relationship and the transfer path, obtain the target factory node for processing the target product and the target warehouse node for storing the materials corresponding to the target product from the multiple nodes; establish each target factory node and the said target product based on the manufacturing cycle and the transfer cycle. The material inventory equation of the warehouse node serves as the supply constraint.

In a possible implementation, the modeling unit 1002 is specifically configured to: calculate new processing items and processing consumption items of the target factory node based on the BOM and the manufacturing cycle; based on the transshipment path and the transshipment cycle, Calculate the transfer-in new items and transfer-out consumption items of the target factory node and the target warehouse node; calculate the inventory input based on the new processing items and the transfer-in new items, and calculate the inventory input items based on the processing consumption items and the transfer-out items. The consumption item is calculated to obtain the inventory output item; the inventory input item and the inventory output item are substituted into the preset inventory relationship equation, and the material inventory equation is established as the supply constraint condition.

In a possible implementation, the modeling unit 1002 is specifically configured to establish the objective function based on the demand priority information.

In a possible implementation, the input data also includes business goals; the modeling unit 1002 is specifically configured to: establish an initial function based on the business goals, and the initial function indicates the goal without considering the demand priority of the target product. The function of the optimization goal of the product's production scheduling plan; determine the penalty coefficient based on the demand priority information, and the penalty coefficient is used to adjust the priority of each decision variable in the initial function under the business goal; assign the penalty coefficient to the The corresponding decision variables in the initial function are used to obtain the objective function.

In a possible implementation, the device 1000 further includes: a calculation unit 1004, configured to calculate the feasible region of the preset constraint element function according to the target constraint element corresponding to the decision variable, the target constraint element being to solve the initial The function is a constraint element that affects the priority of the decision variable under the business goal; the modeling unit 1002 is specifically used to determine the penalty coefficient according to the demand priority of the decision variable and the feasible region of the constraint element function.

In one possible implementation, the acquisition unit 1001 is specifically configured to: acquire the input data from a production scheduling computer network constructed based on the ERP system or MES of each node in the supply system.

The data processing device 1000 provided in the embodiment of the present application can be understood by referring to the corresponding content of the foregoing data processing method embodiment, and the details will not be repeated here.

As shown in FIG. 11 , a possible logical structure diagram of a computer device 1100 is provided for an embodiment of the present application. The computer device 1100 includes a processor 1101, a communication interface 1102, a memory 1103, and a bus 1104. The processor 1101 may include a CPU, or at least one of a CPU and a GPU, an NPU, and other types of processors. The processor 1101, the communication interface 1102, and the memory 1103 are connected to each other through a bus 1104. In the embodiment of the present application, the processor 1101 is used to control and manage the actions of the computer device 1100. For example, the processor 1101 is used to perform the steps in FIG. 5 and/or other processes for the technology described herein. The communication interface 1102 is used to support the computer device 1100 to communicate. Memory 1103 is used to store program code and data for computer device 800 .

The processor 1101 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logical blocks, modules and functions described in connection with the disclosure of this application. circuit. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 1104 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of presentation, only one thick line is used in Figure 11, but it does not mean that there is only one bus or one type of bus.

In another embodiment of the present application, a computer-readable storage medium is also provided. Computer-executable instructions are stored in the computer-readable storage medium. When at least one processor of the device executes the computer-executed instructions, the device executes the above figure. Data processing methods described in Part 5 embodiments.

In another embodiment of the present application, a computer program product is also provided. The computer program product includes computer-executable instructions, and the computer-executable instructions are stored in a computer-readable storage medium; at least one processor of the device can obtain data from a computer-readable storage medium. The storage medium is read to read the computer execution instructions, and at least one processor executes the computer execution instructions to cause the device to execute the data processing method described in part of the embodiment in FIG. 5 .

In another embodiment of the present application, a chip system is also provided. The chip system includes a processor and is used to support the above-mentioned data processing device to implement the functions involved in the above-mentioned data processing method. In a possible implementation, the chip system further includes a memory, which is used to store necessary program instructions and data of the data processing device to implement the functions of the above data processing method. The chip system can be composed of chips or include chips and other discrete devices.

The above is a detailed introduction to the embodiments of the present application. The steps in the methods of the embodiments of the present application can be sequentially scheduled, merged or deleted according to actual needs; the modules in the devices of the embodiments of the present application can be divided, merged or deleted according to actual needs. .

It will be understood that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its functions and internal logic, which does not correspond to the embodiments of the present application. The implementation process constitutes any limitation.

The term "and/or" in this article is just an association relationship that describes related objects, indicating that three relationships can exist. For example, A and/or B can mean: A exists alone, A and B exist simultaneously, and they exist alone. B these three situations. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship.

It should be understood that in the embodiment of the present application, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and/or other information.

Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be described again here.

In the several embodiments provided in the embodiments of this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented. another point, The coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application are essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the embodiments of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program code. .

Claims

A data processing method, characterized in that the method includes:

Obtain input data, which is data related to the target product produced by multiple factories. The input data includes the demand for the target product, the transfer data between multiple nodes in the supply system of the target product, and all The bill of materials BOM and manufacturing cycle required for the processing of the target product are described. The transshipment data is used to indicate the path and time of material transportation. The multiple nodes include factory nodes and warehouse nodes;

A target model is constructed based on the input data, and the target model includes supply constraints and objective functions based on decision variables, wherein the supply constraints are established based on the bill of materials, the manufacturing cycle and the transshipment data, The supply constraints are used to constrain the inventory status of the factory node and the warehouse node to remain stable. The inventory status is determined based on the manufacturing cycle and the transportation data. The decision variable is based on the target product. The demand is obtained, and the objective function is used to indicate the optimization goal of the production schedule of the target product;

The solver is called to solve the target model and the production scheduling plan is obtained.
The method according to claim 1, wherein the input data also includes production line material relationships of the factory nodes; the transfer data includes transfer paths and transfer cycles; and the target model is constructed based on the input data. ,include:

Based on the BOM, the production line material relationship and the transfer path, obtain the target factory node that processes the target product and the target warehouse node that stores the corresponding materials of the target product from the multiple nodes;

A material inventory equation for each of the target factory node and the target warehouse node is established as the supply constraint based on the manufacturing cycle and the transfer cycle.
The method according to claim 2, wherein the material inventory equation of each target factory node and the target warehouse node is established as the supply constraint based on the manufacturing cycle and the transfer cycle. ,include:

According to the BOM and the manufacturing cycle, calculate the new processing items and processing consumption items of the target factory node;

According to the transshipment path and the transshipment cycle, calculate the transfer-in new items and transfer-out consumption items of the target factory node and the target warehouse node;

The inventory input is calculated based on the new processing items and the transferred-in new items, and the inventory output is calculated based on the processing consumption items and the transferred-out consumption items;

The inventory input items and the inventory output items are substituted into the preset inventory relationship equation, and the material inventory equation is established as the supply constraint condition.
The method according to any one of claims 1 to 3, wherein the input data includes demand priority information of the target product; and building a target model based on the input data includes:

The objective function is established based on the demand priority information.
The method of claim 4, wherein the input data further includes business goals; and establishing the goal function based on the demand priority information includes:

Establish an initial function according to the business goal, and the initial function is a function that indicates the optimization goal of the production schedule of the target product without considering the demand priority of the target product;

Determine a penalty coefficient according to the demand priority information, and the penalty coefficient is used to adjust the priority of each decision variable in the initial function under the business goal;

The penalty coefficient is assigned to the corresponding decision variable in the initial function to obtain the objective function.
The method according to claim 5, characterized in that before determining the penalty coefficient according to the demand priority information, the method further includes:

Calculate the feasible region of the preset constraint element function according to the target constraint element corresponding to the decision variable. The target constraint element is the factor that affects the priority of the decision variable under the business goal when solving the initial function. Constraint elements;

Determining the penalty coefficient based on the demand priority information includes:

The penalty coefficient is determined according to the demand priority of the decision variable and the feasible region of the constraint factor function.
The method according to any one of claims 1 to 6, characterized in that said obtaining input data includes:

The input data is obtained from a production scheduling computer network constructed based on an enterprise resource planning ERP system or a manufacturing execution system MES of each node in the supply system.
A data processing device, characterized in that the device includes:

An acquisition unit is used to acquire input data. The input data is data related to the target product produced by multiple factories. The input data includes the demand for the target product and the communication between multiple nodes in the supply system of the target product. Transshipment data, as well as the bill of materials BOM and manufacturing cycle required for processing the target product, the transshipment data is used to indicate the path and time of material transportation, and the multiple nodes include factory nodes and warehouse nodes;

A modeling unit configured to construct a target model based on the input data, the target model including supply constraints and an objective function based on decision variables, wherein the supply constraints are established based on the manufacturing cycle and the transportation data , the supply constraints are used to constrain the inventory status of the factory node and the warehouse node to remain stable. The inventory status is determined based on the manufacturing cycle and the transshipment data. The decision variable is based on the target product. The demand is obtained, and the objective function is used to indicate the optimization goal of the production schedule of the target product;

A solving unit is used to call a solver to solve the target model and obtain the production scheduling plan.
The device according to claim 8, characterized in that the input data also includes production line material relationships of the factory nodes; the transfer data includes transfer paths and transfer cycles; the modeling unit is specifically used to:

Based on the BOM, the production line material relationship and the transfer path, obtain the target factory node that processes the target product and the target warehouse node that stores the corresponding materials of the target product from the multiple nodes;

A material inventory equation for each of the target factory node and the target warehouse node is established as the supply constraint based on the manufacturing cycle and the transfer cycle.
The device according to claim 9, characterized in that the modeling unit is specifically used to:

According to the BOM and the manufacturing cycle, calculate the new processing items and processing consumption items of the target factory node;

According to the transfer path and the transfer cycle, calculate the transferred-in new items and transferred-out consumption items of the target factory node and the target warehouse node;

The inventory input is calculated based on the new processing items and the transferred-in new items, and the inventory output is calculated based on the processing consumption items and the transferred-out consumption items;

The inventory input items and the inventory output items are substituted into the preset inventory relationship equation, and the material inventory equation is established as the supply constraint condition.
The device according to any one of claims 8 to 10, characterized in that the modeling unit is specifically used for:

The objective function is established based on the demand priority information.
The device according to claim 11, characterized in that the input data also includes business goals; the modeling unit is specifically used to:

Establish an initial function according to the business goal, and the initial function is a function that indicates the optimization goal of the production schedule of the target product without considering the demand priority of the target product;

Determine a penalty coefficient based on the demand priority information, and the penalty coefficient is used to adjust the priority of each decision variable in the initial function under the business goal;

The penalty coefficient is assigned to the corresponding decision variable in the initial function to obtain the objective function.
The device according to claim 12, characterized in that the device further includes:

A calculation unit configured to calculate the feasible region of a preset constraint element function based on the target constraint element corresponding to the decision variable. The target constraint element affects the decision variable in the business goal when solving the initial function. Priority constraint elements under;

The modeling unit is specifically used for:

The penalty coefficient is determined according to the demand priority of the decision variable and the feasible region of the constraint factor function.
The device according to any one of claims 8 to 13, characterized in that the acquisition unit is specifically used to:

The input data is obtained from a production scheduling computer network constructed based on an enterprise resource planning ERP system or a manufacturing execution system MES of each node in the supply system.
A computer device, characterized in that it includes:

processor, memory;

Instruction operations or codes are stored in the memory;

The processor is configured to communicate with the memory and execute instruction operations or code in the memory to perform the method of any one of claims 1 to 7.
A computer-readable storage medium, characterized in that the computer-readable storage medium includes instructions that, when run on a computer, cause the computer to perform the method according to any one of claims 1 to 7.
A computer program product, characterized by comprising computer-readable instructions, which when run on a computer device, cause the computer device to perform the method according to any one of claims 1 to 7.