CN116976576A

CN116976576A - Data processing method and related device

Info

Publication number: CN116976576A
Application number: CN202210405762.4A
Authority: CN
Inventors: 艾又琼; 孙磊; 刘复兴; 金啸宇; 王亮; 明威
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2022-04-18
Filing date: 2022-04-18
Publication date: 2023-10-31
Also published as: WO2023202326A1

Abstract

The application discloses a data processing method and a related device, which are used for processing input data to generate a scheduling plan under a multi-factory scene, so that the scheduling can be performed by adopting the scheduling plan to reduce production conflict, reduce inventory cost and improve inventory sleeve rate. In the scheme, input data are acquired firstly, a target model is constructed based on the input data, supply constraint conditions in the target model are built based on a manufacturing period and a bill of materials of a target product and transfer data among a plurality of nodes in a supply system of the target product, and a target function considers the production priority of the target product and traction production of the target product based on a business target; and finally, solving the target model representing the overall production scheduling problem of the plurality of factories under the constraint condition and the traction of the target function to obtain the production scheduling plan of the plurality of factories.

Description

Data processing method and related device

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a data processing method and a related device.

Background

Scheduling, namely, making a production plan, is the work of an enterprise for comprehensively arranging production tasks and particularly planning the variety, quantity and progress of production products. Scheduling is an important component of enterprise operation planning, is an important basis for enterprise production management, is an important means for realizing enterprise operation targets, and is a basis for organizing and guiding enterprise production activities to be performed in a planned manner. At the same time, the reasonable arrangement of the production plan is also beneficial to improving the production organization.

In recent years, the manufacturing networks of many manufacturing enterprises have increasingly covered the world, consisting of global suppliers, manufacturing and assembly plants, and outsourcing entities. This requires consideration of multiple factory factors in production scheduling; in the context of multi-factory production, a production schedule requires that all production jobs for different factories be scheduled simultaneously in each cycle. In the prior art, a manufacturing enterprise decouples a scheduling problem of multiple factories into sub-problems of multiple single factories, then solves the sub-problems respectively, and finally merges and generates a final scheduling plan according to the solving results of the multiple sub-problems.

Under the scene of multiple factories, the method of the prior art obtains corresponding solutions on all sub problems, but in the actual production process, the production activities of all factories have interdependence, so that the production conflict easily exists in the production scheduling obtained according to the prior art, thereby leading to the improvement of the inventory cost and the low inventory matching rate.

Disclosure of Invention

The application provides a data processing method and a related device, which are used for processing data to generate a scheduling plan, so that production conflict can be reduced when the scheduling plan is adopted for production, inventory cost is reduced, and inventory rate is improved.

The first aspect of the present application provides a data processing method applied to a computer device, where the computer device may be a terminal, a server, or other computer devices with data processing capability, and the following description will take application to a server as an example.

And the server can acquire the input data firstly when the scheduling is performed in a multi-factory scene. The input data is related data of a plurality of factories for producing target products, and the input data comprises requirements of the target products, transfer data among a plurality of nodes in a supply system of the target products and a manufacturing cycle of bill of materials (BOM) required by processing the target products; the diversion data is used to indicate a path and time of material transport, and the plurality of nodes includes a factory node and a warehouse node.

The target product refers to a product finally delivered to a demand party, and the BOM required by processing the target product comprises the BOM of the target product and the BOM of each level of sub-components of the target product. It will be appreciated that the material for direct processing into the target product is a primary sub-component, the material for direct processing into the primary sub-component is a secondary sub-component, and so on.

The supply system refers to a supply network formed by subordinate nodes related to the production of target products by manufacturing enterprises and a unified coding system of materials transported among the subordinate nodes. The subordinate nodes comprise factory nodes, warehouse nodes and sales nodes; the materials include products, intermediates, raw materials, devices and consumables.

The manufacturing cycle required by the target product processing refers to a processing cycle corresponding to each BOM required by the target product processing.

The path of material transportation refers to the sequence of nodes through which the material is transported, and the actual path between two nodes in adjacent sequence, such as nodes from the A ground to the B ground and then from the B ground to the C ground.

After obtaining the input data, the server may construct a target model based on the input data, the target model including supply constraints based on decision variables and an objective function based on the decision variables.

Wherein a supply constraint is established based on the BOM, the manufacturing cycle, and the diversion data, the supply constraint being used to constrain an inventory status of the plant node and the warehouse node to remain stable, the inventory status being determined based on the manufacturing cycle and the diversion data, the decision variable being derived based on a demand of the target product, the objective function being used to indicate an optimization objective of a production plan of the target product.

The stable stock state means that the stock retention rate of all coded materials in the stock is kept at the lowest level as possible, so that the process of producing target products reaches the supply-demand balance of production as much as possible, that is, the materials obtained by the production of the production line of the factory in the current period meet the production requirement and the market requirement as accurately as possible.

It can be known that the supply constraint condition actually constrains the stock state of the materials in all nodes of the whole supply system, so that the materials in all nodes are kept in a stable state as much as possible, and the dead backlog of the materials is reduced, thereby reducing the stock cost and the production risk.

Finally, the server can call a solver to solve the target model to obtain a scheduling plan.

The production scheduling plan comprises the processing amount of the target product processed by the factory node and the transportation amount of the materials corresponding to the target product transferred among the nodes.

In the application, a server constructs a target model by acquiring input data and based on the input data under the scene of multi-factory scheduling, wherein the supply constraint condition in the target model is based on the manufacturing period of a target product and the transfer data among a plurality of nodes in a supply system of the target product; the supply constraint condition enables the supply of the factories to be combined into a whole, and finally, the target model representing the whole production scheduling problem of the factories is solved to obtain a production scheduling plan of the factories, so that production conflicts among the factories can be reduced, inventory cost is reduced, and inventory matching rate is improved.

In one possible implementation, the input data further includes a line material relationship for the plant node; the diversion data includes a diversion path and a diversion period; the constructing a target model based on the input data includes: based on the BOM, the production line material relation and the transfer path, a target factory node for processing the target product and a target warehouse node for storing materials corresponding to the target product are obtained from the nodes; a material inventory equation for each of the target factory node and target warehouse node is established as the supply constraint based on the manufacturing cycle and the diversion cycle.

The transfer path refers to a path for transporting materials, and the transfer period refers to the time for transporting the materials between two nodes.

The material relation of the production line is used for indicating materials required by production of the production line and materials obtained by production.

Wherein the stock inventory equation is used to restrict each item of material in the factory node or the warehouse node from remaining stable in the current stock state.

In one possible implementation, the server may first establish a stock inventory equation for all nodes in the supply system based on the input data, and then screen the stock inventory equation for all nodes to obtain a target stock inventory equation with complete equation data as the supply constraint condition.

In one possible implementation, the establishing a stock of materials equation for each of the target plant node and target warehouse node based on the manufacturing cycle and the diversion cycle as the supply constraint includes: calculating to obtain a processing newly added item and a processing consumption item of the target factory node according to the BOM network and the manufacturing period; according to the transit network and the transit period, calculating to obtain a transfer-in newly-added item and a transfer-out consumption item of the target factory node and the target warehouse node; calculating to obtain an inventory entry according to the processing newly added item and the transfer newly added item, and calculating to obtain an inventory sales item according to the processing consumption item and the transfer consumption item; substituting the stock entry and the stock pin into a preset stock relation, and establishing the material stock equation as the supply constraint condition.

According to the BOM, the production line material relation and the manufacturing period, a new processing item and a new processing consumption item of each target factory node about the production line of the target product can be obtained; according to the target factory node, the target warehouse node, the transfer path and the transfer period, a transfer-in newly-added item of the material corresponding to the target product transferred in by each target factory node or target warehouse node and a transfer-out consumption item of the transferred material corresponding to the target product can be obtained.

The inventory relation can be obtained according to a preset supply matching business rule, and the inventory relation is one of stock equations of materials.

In one possible implementation, the input data includes demand priority information for the target product; the building of the object model based on the input data includes: an objective function is established based on the demand priority information.

The demand priority information is used for indicating the priority of the target product in production scheduling and production.

In one possible implementation, the input data further includes a business objective; the establishing the objective function based on the demand priority information includes: establishing an initial function according to the business objective, wherein the initial function is a function indicating an optimization objective of the production scheduling plan of the objective product when the demand priority of the objective product is not considered; determining a penalty coefficient according to the demand priority information, wherein the penalty coefficient is used for adjusting the priority of each decision variable in the initial function under the service target; and giving the punishment coefficient to a corresponding decision variable in the initial function to obtain the objective function.

The service objective is used for indicating the optimization direction of the scheduling, so it can be understood that the server can obtain the optimal scheduling plan under the service objective by solving the values of the decision variables in the initial function according to the decision variables in the initial function established by the service objective, without considering the demand priority of the target product.

The decision variable corresponds to the demand of the target product, and the punishment coefficient corresponds to the demand priority of the target product, so that the decision variable in the initial function has a corresponding relation with the punishment coefficient.

In the application, the requirement priority information is combined with the initial function, so that priority factors can be considered when the objective function is defined, and the server can preferentially meet the requirement of high priority when scheduling.

In the application, the priority of the decision variable is quantized in a mode of determining the punishment coefficient according to the demand priority information, so that the server can perform priority related operation when scheduling according to the objective function, and the finally obtained scheduling plan meets the demand priority requirement as much as possible.

In one possible implementation, before determining the penalty factor based on the demand priority information, the method further comprises: calculating a feasible domain of a preset constraint element function according to a target constraint element corresponding to the decision variable, wherein the target constraint element is a constraint element which influences the priority of the decision variable under the business target when the initial function is solved; the determining a penalty factor based on the demand priority information includes: and determining the penalty coefficient according to the demand priority of the decision variable and the feasible domain of the constraint element function.

The constraint element refers to a constraint factor of a decision variable when the optimization calculation of the business objective is performed.

Wherein the constraint element function is used to calculate a penalty coefficient.

According to the application, the penalty coefficient is determined according to the demand priority and the feasible region of the constraint element function, so that compared with a method for directly determining the penalty coefficient according to the demand priority, the penalty coefficient can be more accurate, and a more reasonable scheduling plan can be obtained.

In another possible implementation, the server may determine a penalty coefficient corresponding to each priority according to the requirement priority information, and then establish an objective function according to the penalty coefficient, the objective product requirement, and the business objective.

In another possible implementation, the demand priority information includes a demand priority for each of the decision variables; determining a penalty factor based on the demand priority information, comprising: and determining a corresponding preset punishment coefficient according to the demand priority.

In one possible implementation, the acquiring input data includes: the input data is obtained from a production-scheduling computer network built based on an enterprise resource planning (enterprise resource planning, ERP) system or a manufacturing execution system (manufacturing execution system, MES) of each node in the provisioning hierarchy.

The server can automatically collect the data of each node in the supply system through a production scheduling computer network, and can also obtain the data from a data file input by a planning and scheduling special person of each node through the production scheduling computer network; the data are preprocessed to obtain the data in a standard format required by the server to execute the data processing method provided by the application.

In one possible implementation, the goal model further includes a demand matching constraint and a capacity occupancy constraint.

The demand matching constraint conditions are obtained based on demand matching rules in the business rules and are used for constraining matching of newly-added demands in the current period and the satisfied demands in the current period; the capacity occupation rule in the capacity occupation constraint condition business rule is obtained and is used for constraining the matching of the current processing amount and the actual occupied capacity.

In one possible implementation, the goal model further includes holiday constraints.

The holiday constraint condition is obtained based on holiday rules in the business rules and is used for constraining the relative values of production, transfer and purchase to be 0 when the date is the holiday.

A second aspect of the present application provides a data processing apparatus comprising:

An obtaining unit, configured to obtain input data, where the input data is related data of a target product produced by a plurality of factories, the input data includes a requirement of the target product, transfer data between a plurality of nodes in a supply system of the target product, and a bill of materials BOM and a manufacturing cycle required for processing the target product, the transfer data is used to indicate a path and a time of material transportation, and the plurality of 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 a supply constraint condition based on a decision variable and an objective function, wherein the supply constraint condition is established based on the manufacturing cycle and the transfer data, the supply constraint condition is used to constrain inventory states of the plant node and the warehouse node to remain stable, the inventory states are determined based on the manufacturing cycle and the transfer data, the decision variable is obtained based on a demand of the target product, and the objective function is used to indicate an optimization target of a production plan of the target product;

and the solving unit is used for calling a solver to solve the target model to obtain the scheduling plan.

In one possible implementation, the input data further includes a line material relationship for the plant node; the diversion data includes a diversion path and a diversion period; the modeling unit is specifically used for: based on the BOM, the production line material relation and the transfer path, a target factory node for processing the target product and a target warehouse node for storing materials corresponding to the target product are obtained from the nodes; a material inventory equation for each of the target plant node and the warehouse node is established as the supply constraint based on the manufacturing cycle and the diversion cycle.

In one possible implementation, the modeling unit is specifically configured to: according to the BOM network and the manufacturing period, calculating to obtain a processing newly-added item and a processing consumption item of the target factory node; according to the transit network and the transit period, calculating to obtain a transfer-in newly-added item and a transfer-out consumption item of the target factory node and the target warehouse node; calculating to obtain an inventory entry according to the processing newly added item and the transfer newly added item, and calculating to obtain an inventory sales item according to the processing consumption item and the transfer consumption item; substituting the stock entry and the stock pin into a preset stock relation, and establishing the material stock equation as the supply constraint condition.

In one possible implementation, the modeling unit is specifically configured to: the objective function is established based on the demand priority information.

In one possible implementation, the input data further includes a business objective; the modeling unit is specifically used for: establishing an initial function according to the business objective, wherein the initial function is a function indicating an optimization objective of the production scheduling plan of the objective product when the demand priority of the objective product is not considered; determining a penalty coefficient according to the demand priority information, wherein the penalty coefficient is used for adjusting the priority of each decision variable in the initial function under the service target; and giving the punishment coefficient to a corresponding decision variable in the initial function to obtain the objective function.

In one possible implementation, the apparatus further includes: the calculating unit is used for calculating the feasible domain of a preset constraint element function according to a target constraint element corresponding to the decision variable, wherein the target constraint element is a constraint element which influences the priority of the decision variable under the business target when the initial function is solved; the modeling unit is specifically used for: and determining the penalty coefficient according to the demand priority of the decision variable and the feasible domain of the constraint element function.

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

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

A fourth aspect of the application provides a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect described above.

A fifth aspect of the application provides a computer program product comprising computer readable instructions which, when run on a computer device, cause the computer device to perform the method of the first aspect described above.

A sixth aspect of the application provides a chip system comprising a processor for supporting the data processing apparatus of the second aspect to perform the functions referred to in the aspects above, such as generating or processing data and/or information referred to in the methods of the first aspect above. In a possible implementation, the chip system further includes a memory, where the memory is configured to store program instructions and data necessary for the data processing apparatus to implement the functions of the first aspect. The chip system may be formed of a chip or may include a chip and other discrete devices.

The solutions provided in the second aspect to the sixth aspect are used to implement or cooperate to implement the method provided in the first aspect, so that the same or corresponding beneficial effects as those in the first aspect can be achieved, and no further description is given here.

Drawings

FIG. 1 is a schematic diagram of an application architecture of a data processing method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a deployment environment of a data processing apparatus according to an embodiment of the present application;

FIG. 3 is a schematic diagram of another deployment environment of a data processing apparatus according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

FIG. 5 is a schematic flow chart of a data processing method according to an embodiment of the present application;

FIG. 6 is a schematic diagram of input data processing according to a data processing method according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a network architecture of a production-scheduling computer network according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a supply system according to an embodiment of the present application;

FIG. 9 is a graph of cumulative inventory matching rate versus results provided by an embodiment of the present application;

FIG. 10 is a schematic diagram of another data processing apparatus according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of a computer network according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described below with reference to the accompanying drawings. It will be apparent that the described embodiments are merely some, but not all embodiments of the application. As a person skilled in the art can know, with the appearance of a new application scenario, the technical scheme provided by the embodiment of the application is also applicable to similar technical problems.

The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the description so used is interchangeable under appropriate circumstances such that the embodiments are capable of operation in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules that are expressly listed or inherent to such process, method, article, or apparatus. The naming or numbering of the steps in the present application does not mean that the steps in the method flow must be executed according to the time/logic sequence indicated by the naming or numbering, and the execution sequence of the steps in the flow that are named or numbered may be changed according to the technical purpose to be achieved, so long as the same or similar technical effects can be achieved. The division of the units in the present application is a logical division, and may be implemented in another manner in practical application, for example, a plurality of units may be combined or integrated in another system, or some features may be omitted or not implemented, and in addition, coupling or direct coupling or communication connection between the units shown or discussed may be through some interfaces, and indirect coupling or communication connection between the units may be electrical or other similar manners, which are not limited in the present application. The units or sub-units described as separate components may be physically separated or not, may be physical units or not, or may be distributed in a plurality of circuit units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present application.

As the economic level increases, the manufacturing networks of many manufacturing enterprises continue to expand and, for cost reasons, corresponding plants are often established near the origin of the raw materials, such that the various plants of the manufacturing enterprises are in different geographical locations. The factories of the manufacturing enterprise can process and produce products independently, or can acquire semi-finished products processed by other regional factories according to local raw materials by means of transfer paths among the factories and reprocess the semi-finished products to obtain the products.

In the context of the above-described joint production of multiple factories of a manufacturing enterprise, the production campaign is also characterized by multiple cycles, multiple priorities, and shareability.

Specifically, the multicycle feature means that the schedule of one production campaign may take into account multiple cycles, such as daily or weekly, future daily or weekly scheduling.

The multi-priority feature means that the product demand has multiple priorities, and the priority is determined based on the comprehensive determination of the product demand source, the demand type, the demand time, the demand quantity, the product feature and other attributes. The scheduling plan needs to determine the production sequence of the products according to the demand priority.

The shareability features include materials sharing, supply network sharing and production line sharing. The material sharing refers to that a plurality of products share the same material; supply network sharing means that one warehouse can be simultaneously supplied to a plurality of different factories and materials can be shared among the different factories; line sharing means that the same line of a factory can produce and process multiple products.

It is appreciated that in a multi-plant setting, the connections and dependencies between the various plants are greatly enhanced. In the prior art, the problem of joint production activity arrangement of a plurality of factories is split into sub-problems of a single factory and solved, and finally, the production conflict is increased, the production activity cannot be smoothly carried out due to the mode of combining the production plans of the single factories into an integral production plan, so that the inventory cost is high, the inventory rate is low, and the benefits of manufacturing enterprises are influenced.

Therefore, a method for generating a scheduling plan capable of solving the above-mentioned problems is needed.

The embodiment of the application provides a data processing method and a related device, which are used for processing data to generate a scheduling plan, so that production conflict can be reduced when the scheduling plan is adopted for production, inventory cost is reduced, and inventory sleeve rate is improved.

In a supply chain plan management system, a market makes a to-be-delivered plan according to sales prediction, and a product department of a producer makes demand prediction of a main production plan according to market demands and current material supply capacity, so that the production planning department of the producer further considers various constraints such as materials, productivity, demand priority, a supply network and the like on the basis, and overall optimization is performed on the production plan in a future period of time. In the management system, the production planning department needs to comprehensively consider the supply and demand balance of the upstream and the downstream, and meanwhile, consider the capacity arrangement of the factory to carry out production scheduling, output the production scheduling plan and lock materials and capacity in advance. Based on the scheduling plan, the product department can output the supply plan of the specific product to make a supply commitment.

Referring to fig. 1, fig. 1 is a schematic diagram of an application architecture 100 of a data processing method according to an embodiment of the application. As shown in FIG. 1, the application architecture 100 includes a data processing apparatus 110, a master production plan (master production schedule, MPS) module 120, an ERP system or MES130 that supplies system nodes (e.g., plant nodes, warehouse nodes, and sales nodes), an allocation commitment module 140, a supplier collaboration module 150, an order commitment 160, an inventory optimization (inventory optimization, IO) module 170, a materials demand plan (material requirement planning, MRP) module 180, a demand management module 190, a demand reduction module 191, an ERP order management module 192, and an ERP inventory or purchase management module 193.

The data processing device 110 is configured to execute the data processing method provided by the present application, and is specifically configured to receive a demand prediction of a main production plan made by the MPS module 120, and schedule production of production data provided by the ERP system or the MES130 of the supply system node, so as to obtain a scheduling plan; and also for sending the scheduling plan to MPS module 120.

The data processing apparatus 110 is further configured to update an available-to-process (ATP) of the product to the allocation commitment module 140 according to the resulting scheduling plan, send a corresponding call charge dispatch request to the vendor collaboration module 150, and make a delivery commitment to the existing order to the order commitment module 160.

The MPS120 module is configured to obtain the demand data provided by the demand subtraction module, the material inventory data provided by the IO module 170, and the procurement commitment provided by the supplier collaboration module, and further make a demand forecast of the main production plan according to the current market demand and the material supply capability, and send the demand forecast to the data processing apparatus 110.MPS120 module is also configured to promise the available amount of ATP of the product to the allocation promise module.

The ERP system or MES130 of the provisioning architecture node is used to provide production data, inventory data, and diversion data for the corresponding node, as well as other nodes.

The allocation commitment module 140 is configured to feed back ATP of the product to the demand management module 190 and to send the net forecast ATP obtained by removing the supply of items such as safety stock and stock plan to the order commitment module 160.

The supplier collaboration module 150 is configured to obtain inventory information and purchase information from the ERP inventory management module or the purchase management module 193, provide the inventory information and the purchase information to the IO module 170, and provide a purchase commitment to the MPS module 120 according to the inventory information and the purchase information.

The order promise module 160 is configured to receive market polling orders and make promises to the market according to the obtained net forecast ATP and promise period of the existing orders; and also for storing the committed orders to the ERP order management module 192.

The IO module 170 is configured to perform inventory management according to the material demand information provided by the MRP module, the purchase information and the inventory information provided by the vendor cooperation module 150, and the scheduling plan or the production plan provided by the MPS module 120.

The MRP module 180 is configured to calculate a required amount and a required time of a material required for product production according to the subtracted required data provided by the required subtraction module 191 and the inventory information provided by the IO module, and determine a processing progress and a purchasing schedule of the product.

The demand management module 190 is configured to calculate and manage the demand of the product according to the order information provided by the ERP order management module 192, the demand forecast obtained from the sales and operation forecast process model (S & OP), the product demand of the projects such as the safety stock or the stock plan in the producer, and the available supply ATP provided by the allocation commitment module 140; and provides the allocation commitment module 140 with the available amount of ATP that the non-order items need to occupy so that the allocation commitment module calculates the net predicted ATP.

The demand deduction module 191 is configured to perform demand deduction according to the demand data provided by the demand management module 190 and the available amount ATP of the current product, so as to obtain net predicted demand data.

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

The ERP inventory management module or the purchase management module is used for providing inventory information and purchase information of the producer.

It will be appreciated that due to the shared nature of the production activities in the multi-plant context, the same material may play a different role in different nodes or different production lines, for example material a as a processing raw material in line 1 and as a consumable in auxiliary production in line 2. Therefore, in the embodiment of the application, when the data processing method provided by the application is executed, a unified coding system is adopted to carry out unified coding on various materials, so that the data of each node in a supply system can be processed in a unified way.

It should be noted that fig. 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, apparatuses, modules, etc. shown in fig. 1 is not limited in any way.

The modules or apparatuses shown in fig. 1 may be each independently disposed on a computer device, or may be disposed together on the same computer device, which is not particularly limited herein. The computer device may be a terminal, server, or other computer device having data processing capabilities.

Referring to fig. 2 and 3, the data processing device 110 may be deployed on a server or on a cloud.

Specifically, as shown in fig. 2, in an embodiment, the data processing apparatus 110 provided by the present application may also be deployed on a network node in a centralized deployment manner or a distributed deployment manner, for example, a machine room of an enterprise, a research institute server or a supply chain office server, and all data are transmitted through the internet. After the server acquires the data, a target model is established according to the data, then a solver is adopted to solve the target model to obtain a scheduling plan, and finally the scheduling plan is output to a man-machine interaction using interface of the server.

In another embodiment, the data processing device 110 may be deployed at a cloud end, after the provisioning system node uploads data to the cloud end, the data processing device 110 completes the establishment and solution of the target model at the cloud end by using a solver and related software services provided by the cloud end, and finally returns the scheduling plan obtained by the solution to the corresponding provisioning system node.

The application architecture and the deployment environment of the data processing method provided by the embodiment of the application are described above, and the internal structure of the data processing device provided by the embodiment of the application is described below. Referring to fig. 4, fig. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application, where the data processing apparatus includes an input unit 401, a data preprocessing unit 402, a modeling solving unit 403, a data post-processing unit 404, and an output unit 405.

The input unit 401 is used to input data from each node of the provisioning hierarchy, and from other devices or modules in the application hierarchy, to the data preprocessing unit 402.

The data preprocessing unit 402 is configured to convert the input data input by the input unit 401 into unified preset input format and standard, and specifically may include unified material coding data, planning time and working calendar, supply and productivity data, transfer path and offset data.

The modeling and solving unit 403 is configured to perform mathematical modeling and solving according to the preprocessed input data, so as to obtain a scheduling plan.

The data post-processing unit 404 is configured to calculate a recommended manufacturing table, a material transfer table, an end-of-period inventory table, and a capacity usage list according to the scheduling plan analysis, where the list is configured to reflect the demand satisfaction, the supply usage, and the shortage. The data post-processing unit 404 is further configured to obtain a definition standard (weighting) table according to the foregoing table.

Based on the above table, the contents that the data post-processing unit 404 finally outputs through the output unit 405 include: suggested task orders, demand satisfaction and delay conditions, material consumption details, capacity utilization details, material transfer details, terminal inventory, supply and demand matching details.

It will be appreciated that the production plan may be adjusted by the producer's staff based on these output content.

The embodiment of the application also provides a data processing method based on the application system architecture shown in fig. 1. Referring to fig. 5, fig. 5 is a flow chart illustrating a data processing method according to an embodiment. As shown in fig. 5, the data processing method includes the following steps 501-503.

501. The data processing device acquires input data.

The input data are related data of target products produced by a plurality of factories, and the input data comprise requirements of the target products, transfer data among a plurality of nodes in a supply system of the target products, BOM (boil off gas) required by processing of the target products and manufacturing cycle; the diversion data is used to indicate a path and time of material transport, and the plurality of nodes includes a factory node and a warehouse node.

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

The BOM required by the target product processing comprises parent item codes, child item codes, the consumption of the BOM, the effective date and the expiration date of the BOM and the like of the material.

The input data further includes work calendar and capacity information of each of the plurality of factories, specifically including a factory location ID, a standard date, a time, whether it is holiday, a product series, a capacity code, and capacity common information.

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

Referring to FIG. 7, in one possible implementation, a data processing apparatus obtains the input data from a production computer network built based on ERP systems of multiple factories. The data processing device can be connected with factory nodes, assembly factory nodes, ERP systems of warehouse nodes and sales nodes, MES or other software for managing input data in the whole supply system through the production scheduling computer network, and acquire corresponding data.

In one possible implementation, the data processing apparatus performs preprocessing to convert the input data into a unified preset input format and standard, and then performs step 502.

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

Referring to fig. 6, fig. 6 is a schematic diagram of input data processing when constructing a target model based on the input data in one embodiment, and specifically includes steps 601 to 604.

601. The data processing means define decision variables from the input data.

The data processing device can define decision variables according to the demands of target products and codes of target products in the input data, such as the processing amount of each target product, the processing amount can be defined as X, and if the factory for processing A product comprises a factory 1 and a factory 2, the main decision variables can be defined as X _A1 、X _A2 。

In addition, before the objective function is established, the data processing apparatus may define auxiliary decision variables, such as a demand meeting amount, which is a meeting amount of a demand at a certain time point or a certain time period, and a delay amount, which is an unmet amount of a target product produced after the end of the period, with respect to a certain demand, according to the target product demand and the business objective.

602. The data processing device establishes a constraint condition according to the input data.

The data processing device can establish constraint conditions according to preset business rules and decision variables, and transfer data, production line information and a target product BOM in input data.

Wherein, the business rule refers to the description of business definition and constraint for maintaining business structure, controlling and influencing production behavior.

Wherein the diversion data includes a diversion period and a diversion path; the line information includes line material relationships and manufacturing cycles for each of the plurality of plants.

And establishing a supply constraint condition according to the supply matching rule in the business rule, and the BOM, the production line information, the transfer path and the transfer period in the target product information, wherein the supply constraint condition is used for constraining the stock states of the factory node and the warehouse node to be kept stable.

By stable inventory status is meant that the inventory retention of all coded materials in inventory is maintained at as low a level as possible, knowing that the supply constraints actually constrain the inventory status of the materials in all nodes of the overall supply system.

In one possible implementation, the data processing apparatus may first obtain, from the plurality of nodes, a target factory node for processing the target product and a target warehouse node for storing a material corresponding to the target product based on the BOM, the line-of-product relationship, and the transfer path; and establishing a stock of materials equation for each of the target factory node and the target warehouse node as the supply constraint based on the manufacturing cycle and the diversion cycle.

In one possible embodiment, the BOM of the target product E is processed as a semi-finished product D from the raw materials a and B, and the semi-finished product D and the raw materials C are processed as the target product E; moreover, it is known from the line material relationships of the plurality of factories that the factory 1 can process a and B into D, and the factory 2 can process D and C into E; meanwhile, the A, B, C raw materials are all located in the central warehouse, and the central warehouse can be directly transferred with the factories 1 and 2 according to transfer path data, and the factories 1 and 2 can also be directly transferred.

Referring specifically to fig. 8, fig. 8 is a schematic diagram of a supply system in this embodiment. Wherein each node plane represents a corresponding BOM, the points in the node planes are encodings of a material, the arrows in the node planes are used to indicate BOM relationships, the arrows between different node planes are used to indicate diversion relationships, bi-directional diversion may exist, and the unidirectional arrows shown in FIG. 8 represent unidirectional diversion.

Referring to fig. 8, the target warehouse node is a central warehouse, and the target plant nodes are plant 1 and plant 2. The central warehouse transfers a and B to plant 1 and C to plant 2; plant 1 processes a and B into semi-finished product D and transfers D to plant 2; the plant 2 processes the raw material C and the semi-finished product D into a target product E.

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

Wherein, the inventory relation corresponding to the supply matching rule is:

end-of-period inventory = initial-period inventory + inventory entry-inventory exit

End-of-period inventory = initial-period inventory + newly added provisioning of the node + transfer-consumption-transfer-out of the node to other nodes

The stock equation obtained by further expanding the stock relation is combined with the codes of various materials in the unified coding system, and the stock equation can be expressed as:

wherein i represents a material code, s represents a factory node or a warehouse node, p represents a demand priority, t represents time, L represents a processing period, and PL represents a purchasing period. The variables are specifically defined as follows: supply quantity Supplyist, demand meet quantity D _istp Production quantity X _ist End of period inventory N _ist Transfer quantity Y _is1s2t Advice of purchase quantity DummyPO _ist Parent item. BOD represents the supply system. I is an indication function, outputting 1 when the input is True, and outputting 0 when the input is False.

In one possible implementation, the establishing a stock of materials equation for each of the target plant node and target warehouse node based on the manufacturing cycle and the diversion cycle as the supply constraint includes: calculating to obtain a machining newly-added item and a machining consumption item of the target factory node according to the manufacturing period; according to the transfer period, calculating to obtain a transfer-in newly-added item and a transfer-out consumption item of the target factory node and the target warehouse node; calculating to obtain an inventory entry according to the processing newly added item and the transfer newly added item, and calculating to obtain an inventory sales item according to the processing consumption item and the transfer consumption item; substituting the stock entry and the stock pin into a preset stock relation, and establishing the material stock equation as the supply constraint condition.

According to the material relation and the manufacturing period, a new processing item and a new processing consumption item of each target factory node about the production line of the target product can be obtained; according to the target factory node, the target warehouse node and the transfer period, a new transfer-in item of the raw material or semi-finished product corresponding to the target product transferred in by each target factory node or target warehouse node and a transfer-out consumption item of the transferred raw material or semi-finished product corresponding to the target product can be obtained.

For example, the complete machine (FG) and the lower bare Metal (MF) are processed at plant a for 2 days, respectively; however, the structural parts required for machining the bare metal are in the central warehouse in addition to the factory a, and the transfer period from the central warehouse to the factory a is 2 days. Accordingly, with the date on which the structure is turned in as of date, an inventory balance equation as shown below can be constructed.

For a central warehouse:

structural Part: initial inventory + committed supply quantity-warehouse out (T-2) =end inventory

For plant a:

(1) Structural Part:

initial inventory + purchased quantity + warehouse transfer into (T) -demand met (Part) -quantity of manufactured MF (T-2) BOM quantity = end inventory

(2) Bare metal MF:

initial inventory + work-in-process quantity (MF) +newly added manufacturing quantity (MF, T) -demand Met (MF) -quantity of manufactured FG (T-2) BOM quantity = end-of-period inventory

(3) Complete machine FG:

initial inventory + work-in-process quantity (FG) +newly manufactured quantity (FG, T) -demand Met (MF) =end-of-period inventory

Wherein, the whole machine and the bare metal belong to workpieces, the stock entry comprises the prior unfinished manufacturing and the current newly added manufacturing, and the stock exit comprises the self-demand meeting; stock entries for bare metal are similar to complete machines, but parent (complete) processing consumes bare metal stock in addition to its own needs (independent orders, etc.) being met with stock. The structural member belongs to the purchasing member, wherein the stock entry comprises newly-added purchase arrival of the factory and supply transferred from a central warehouse, and the stock exit comprises self demand satisfaction and father (bare metal) consumption. The entry into the central warehouse is the supply promised to arrive by the suppliers at each stage, and the exit is the number of supplies diverted from the warehouse to the factory.

By introducing a manufacturing period and a transfer period into a material inventory equation, and associating the supply of different materials in different factories through a unified coding system, the target model constructed based on the supply constraint condition can be guided to realize the linkage production of a plurality of factories.

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

The requirement matching rule is as follows: newly added demand at current period + history unmet demand = demand at current period + accumulated unmet demand at current period; the capacity occupation rule is as follows: current process amount = current utilization capacity × yield, current utilization capacity + current empty capacity = current total capacity; holiday rules are: holiday production, transportation, procurement = 0.

Wherein the demand matches the mathematical equation of the constraint:

Demand _istp +GAP _is(t-1)p ＝D _istp +GAP _istp

the mathematical equation for capacity occupancy constraint is:

the equation for holiday constraint conditions is:

wherein i represents a code, s represents a factory or warehouse, p represents a demand priority, t represents time, and g represents an energy production sharing group. The variables are specifically defined as follows: demand meeting quantity D _istp Cumulative demand delay amount GAP _itsp Production quantity X _itsp Transfer quantity Y _is1s2t Advice of purchase quantity DummyPO _ist Capacity occupied number of capacity occupied _isgt Demand newly increased in current period _istp Yield characterization _isg Capacity empty quantity CapLeft _gst Total Capacity capability _gst 。

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

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

In one possible implementation, the data processing apparatus may first establish an initial function based on the business objective; determining a punishment coefficient according to the demand priority information; and finally, giving the punishment coefficient to a corresponding decision variable in the initial function to obtain the objective function.

Wherein the penalty factor is used to adjust the priority of each of the decision variables in the initial function under the business objective.

Wherein the initial function is a function that indicates an optimization objective of the scheduling plan for the target product without regard to the demand priority of the target product.

The demand priority information is used for indicating the priority of the target product in scheduling.

Specifically, the method for determining the penalty coefficient may be: the data processing device calculates the feasible region of a preset constraint element function according to the target constraint element corresponding to the decision variable; and determining the penalty coefficient according to the demand priority of the decision variable and the feasible domain of the constraint element function.

The target constraint element is a constraint element which influences the priority of the decision variable under the business target when the initial function is solved.

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 the demand priorities, the constraint element function may be:

F(obj ₁ ,obj ₂ ,...,obj _n )＞Max(obj _x /obj _y )(x＞y，x,y∈n)

wherein obj is _n And the requirement priority is n levels altogether for the target constraint element corresponding to the decision variable with the requirement priority of n.

The corresponding penalty coefficient calculation equation is:

Penalty _p ＝F(obj ₁ ,obj ₂ ,...,obj _n ) ^n-p

wherein Penalty _p And the penalty coefficient corresponding to the decision variable with the requirement priority of P is used.

In another possible implementation of calculating the penalty coefficient, when the penalty coefficient is calculated with reference to the target constraint element corresponding to the adjacent demand priority, the constraint element function may be:

f(obj _n )＝Penalty _p /Penalty _p+1

the meaning of the parameters in the constraint element function is consistent with the foregoing equation, and will not be described in detail here.

The corresponding penalty coefficient calculation equation is:

Penalty _p ＝Penalty _p+1 *f(obj _n )

Penalty _n ＝BasicPenalty

wherein BasicPenalty is a preset penalty radix.

For example, in a certain order, there are three demands 100, A, B and C for the target product A, B and C, and the priorities of the three demands are sequentially reduced to 1,2 and 3, respectively. A. B and C share material D, and the corresponding BOM consumption is respectively 10,5 and 1. The business objective is to maximize the alignment of the target products, i.e. minimize the delay amount of the target products, and an initial function can be constructed as follows:

Min GAP _A +GAP _B +GAP _C

Wherein GAP _n Is the amount of delay for the target product.

When the production objective is to meet the maximum demand, the 100 pieces D are completely allocated to the use of C, and the maximum number of the 100 pieces C can be assembled. But the result is against the demand priority principle that neither the a nor B demands with higher priority than C are satisfied. The key factor that leads to this result is the amount of BOM involved in the scheduling objective, and the unit demand for necked material D for low demand priority C is less than for high demand priority A and B. Thus, the target constraint element can be determined as BOM consumption to obtain obj ₁ ＝10，obj ₂ ＝5，obj ₃ ＝1。

When the penalty factor is calculated using the first possible implementation, F (obj) can be calculated ₁ ,obj ₂ ,...,obj _n ) The data processing means can take any value from the feasible domain for penalty factor calculation, e.g. 11. When F (obj) ₁ ,obj ₂ ,...,obj _n ) When=11, the corresponding Penalty coefficient can be calculated to obtain Penalty ₁ ＝121，Penalty ₂ ＝11，Penalty ₃ ＝1。

When calculating penalty coefficients using the second possible implementation, penalty costs may be constructed first.

Scheme 1.1: if D is used completely to make A, A may be 10 pieces, GAP _A ＝90，GAP _B ＝100，GAP _C ＝100。

Scheme 1.2: if D is fully used to make B, B may be 20 pieces, GAP _A ＝100，GAP _B ＝80，GAP _C ＝100。

Scheme 1.3: if D is used completely to make C, C may satisfy 100 pieces, GAP _A ＝100，GAP _B ＝100，GAP _C ＝0。

By guiding the data processing apparatus to meet the requirement of high priority in priority when scheduling in a manner of minimizing penalty cost, the penalty cost of scheme 1.1 can be made smaller than the penalty cost of scheme 1.2, and the penalty cost of scheme 1.2 is smaller than the penalty cost of scheme 1.3. The formula can be established:

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

Calculating to obtain P ₁ ＞2P ₂ ，P ₂ ＞5P ₃ Further f (obj) can be obtained ₁ )＞2，f(obj ₂ )＞5。

Wherein P is _n N is the demand priority, which is the penalty coefficient.

Can take f (obj) ₁ )＝2.1，f(obj ₂ ) =5.1, penalty radix basic=1, and P is calculated ₁ ＝1，P ₂ ＝5.1，P ₃ ＝10.71。

It can be understood that the above calculation of the penalty coefficient is a calculation equation adopted for the initial function being the Min function, so that the penalty coefficient is gradually increased, and the data processing apparatus can be guided to preferentially meet the requirement of high priority when scheduling. When the initial function is a Max function, the above calculation equation may be changed, so that the penalty coefficient is reduced step by step, and the data processing apparatus can be guided to preferentially meet the requirement of high priority when the data processing apparatus is scheduled.

According to the embodiment of the application, the priority of the decision variable can be quantized in a mode of determining the punishment coefficient according to the demand priority information, so that the server can perform priority related operation when scheduling according to the objective function, and the finally obtained scheduling plan meets the demand priority requirement as much as possible.

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

The data processing device can establish an initial function according to the requirements of the target product and the business target, then determine a corresponding preset punishment coefficient according to the priority of the requirements, and finally endow the punishment coefficient to a corresponding decision variable in the initial function to obtain the target function.

For example, after determining the change direction of the penalty coefficient according to the initial function, the data processing device sets the penalty coefficient with the highest or lowest priority as the base, and determines that the penalty coefficient of the decision variable with the requirement priority of N is 100 times or 100 times of the penalty coefficient of the decision variable with the requirement priority of n+1 according to the requirement priority information, so as to guide the decision variable with the requirement priority of N to be preferentially satisfied during scheduling.

In another possible implementation, the data processing apparatus may further determine a penalty coefficient corresponding to each priority according to the requirement priority information, and then establish an objective function according to the penalty coefficient, the objective product requirement, and the business objective.

It will be appreciated that step 602 and step 603 are not in a fixed order of execution, and that both steps may be performed simultaneously, or in a specific or random order.

604. The data processing device constructs a target model according to the decision variable, the constraint condition and the target function.

Wherein the object model is a mathematical model.

503. The data processing device solves the target model to obtain the scheduling plan.

In the multi-factory scheduling scene, the data processing device acquires input data and builds a target model based on the input data, wherein the supply constraint condition in the target model is based on the manufacturing period of a target product and the transfer data among a plurality of nodes in a supply system of the target product; the inventory of the plurality of factories is combined into a whole through the supply constraint condition, and finally the target model representing the whole production scheduling problem of the plurality of factories is solved to obtain a production scheduling plan of the plurality of factories, so that production conflicts among the factories can be reduced, inventory cost is reduced, and inventory matching rate is improved.

The following will further describe the beneficial effects of the embodiment of the present application with reference to fig. 9 and table 1, and the cumulative inventory matching rate versus result chart provided by the embodiment of the present application.

According to the application, 3 pairs of similar terminal product orders are used as comparison objects, each pair of orders is respectively subjected to scheduling by adopting the data processing method provided by the application and the method for decoupling the multi-factory problem into scheduling of a plurality of single-factory problems in the prior art, the cumulative nesting rate of 4 weeks in total in one period is counted, and the compared statistical results are shown in fig. 9 and table 1.

TABLE 1

Compared with the prior art, the average accumulated inventory requirement rate can be improved by more than 5%, the accumulated inventory rate of 80% of products is improved, and the inventory rate in all product series is improved by nearly 80%.

The data processing method in the embodiment of the present application is described above, and the data processing apparatus in the embodiment of the present application is described below, referring to fig. 10, an embodiment of a data processing apparatus 1000 in the embodiment of the present application includes:

an obtaining unit 1001, configured to obtain input data, where the input data is related data of a target product produced by a plurality of factories, the input data includes a requirement of the target product, transfer data between a plurality of nodes in a supply system of the target product, and a bill of materials BOM and a manufacturing cycle required for processing the target product, the transfer data is used to indicate a path and a time of material transportation, and the plurality of nodes includes a factory node and a warehouse node;

A modeling unit 1002 configured to construct a target model based on the input data, the target model including a supply constraint condition based on a decision variable and an objective function, wherein the supply constraint condition is established based on the manufacturing cycle and the transfer data, the supply constraint condition is used to constrain an inventory state of the plant node and the warehouse node to remain stable, the inventory state is determined based on the manufacturing cycle and the transfer data, the decision variable is obtained based on a demand of the target product, and the objective function is used to indicate an optimization target of a production plan of the target product;

and the solving unit 1003 is used for calling a solver to solve the target model to obtain the scheduling plan.

In the multi-factory scheduling scene, the data processing device acquires input data through an acquisition unit 1001, and constructs a target model based on the input data through a modeling unit 1002, wherein the supply constraint condition in the target model is based on the manufacturing period of a target product and the transfer data among a plurality of nodes in a supply system of the target product is established; the supply constraint conditions enable the supply of the factories to be combined into a whole, and finally the solving unit 1003 solves the target model representing the whole production scheduling problem of the factories to obtain a production scheduling plan of the factories, so that production conflicts among the factories can be reduced, inventory cost can be reduced, and inventory matching rate can be improved.

In one possible implementation, the input data further includes a line material relationship for the plant node; the diversion data includes a diversion path and a diversion period; the modeling unit 1002 is specifically configured to: based on the BOM, the production line material relation and the transfer path, a target factory node for processing the target product and a target warehouse node for storing materials corresponding to the target product are obtained from the nodes; a material inventory equation for each of the target plant node and the warehouse node is established as the supply constraint based on the manufacturing cycle and the diversion cycle.

In one possible implementation, the modeling unit 1002 is specifically configured to: calculating to obtain a machining new item and a machining consumption item of the target factory node according to the BOM and the manufacturing period; according to the transfer path and the transfer period, calculating to obtain a transfer-in newly-added item and a transfer-out consumption item of the target factory node and the target warehouse node; calculating to obtain an inventory entry according to the processing newly added item and the transfer newly added item, and calculating to obtain an inventory sales item according to the processing consumption item and the transfer consumption item; substituting the stock entry and the stock pin into a preset stock relation, and establishing the material stock equation as the supply constraint condition.

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

In one possible implementation, the input data further includes a business objective; the modeling unit 1002 is specifically configured to: establishing an initial function according to the business objective, wherein the initial function is a function indicating an optimization objective of the production scheduling plan of the objective product when the demand priority of the objective product is not considered; determining a penalty coefficient according to the demand priority information, wherein the penalty coefficient is used for adjusting the priority of each decision variable in the initial function under the service target; and giving the punishment coefficient to a corresponding decision variable in the initial function to obtain the objective function.

In one possible implementation, the apparatus 1000 further includes: a calculating unit 1004, configured to calculate a feasible region of a preset constraint element function according to a target constraint element corresponding to the decision variable, where the target constraint element is a constraint element that affects a priority of the decision variable under the business objective when the initial function is solved; the modeling unit 1002 is specifically configured to: and determining the penalty coefficient according to the demand priority of the decision variable and the feasible domain of the constraint element function.

In one possible implementation, the obtaining unit 1001 is specifically configured to: the input data is obtained from a production-scheduling computer network built based on the ERP system or MES of each node in the provisioning system.

The data processing apparatus 1000 provided in the embodiment of the present application may be understood by referring to the corresponding content of the foregoing data processing method embodiment, and the detailed description is not repeated here.

As shown in fig. 11, a schematic diagram of a possible logic structure of a computer device 1100 according to an embodiment of the present application is provided. The computer device 1100 includes: the processor 1101, communication interface 1102, memory 1103 and bus 1104, the processor 1101 may comprise a CPU or at least one of a CPU with a GPU and 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 an embodiment of the application, the processor 1101 is used to control and manage the actions of the computer device 1100, e.g., the processor 1101 is used to perform the steps in fig. 5 and/or other processes for the techniques described herein. The communication interface 1102 is used to support communication by the computer device 1100. The memory 1103 is used to store program codes and data for the computer device 800.

The processor 1101 may be a central processor unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor may also be a combination that performs the function of a computation, e.g., a combination comprising one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so forth. Bus 1104 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in FIG. 11, but not only one bus or one type of bus.

In another embodiment of the present application, there is further provided a computer-readable storage medium having stored therein computer-executable instructions which, when executed by at least one processor of a device, perform the data processing method described in the above-described embodiment of fig. 5.

In another embodiment of the present application, there is also provided a computer program product comprising computer-executable instructions stored in a computer-readable storage medium; the at least one processor of the device may read the computer-executable instructions from a computer-readable storage medium, the at least one processor executing the computer-executable instructions causing the device to perform the data processing method described above in connection with the partial embodiment of fig. 5.

In another embodiment of the present application, there is also provided a chip system including a processor for supporting the above-mentioned data processing apparatus to implement the functions involved in the above-mentioned data processing method. In a possible implementation, the chip system further includes a memory, where the memory is configured to store program instructions and data necessary for the data processing apparatus to implement the functions of the data processing method described above. The chip system may be formed of a chip or may include a chip and other discrete devices.

The above embodiments of the present application are described in detail, and steps in the method of the embodiments of the present application may be sequentially scheduled, combined or pruned according to actual needs; the modules in the device of the embodiment of the application can be divided, combined or deleted according to actual needs.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all 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 various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, which should not constitute any limitation on the implementation process of the embodiments of the present application.

The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

It should be understood that in embodiments of the present application, "B corresponding to a" means that B is associated with a, from which B may be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may 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 may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be embodied in essence or a part contributing to the prior art or a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Claims

1. A method of data processing, the method comprising:

Acquiring input data, wherein the input data is related data of target products produced by a plurality of factories, the input data comprises the requirements of the target products, transfer data among a plurality of nodes in a supply system of the target products, and a bill of materials BOM and a manufacturing period required by processing the target products, the transfer data is used for indicating the path and time of material transportation, and the plurality of nodes comprise factory nodes and warehouse nodes;

constructing a target model based on the input data, wherein the target model comprises a supply constraint condition and an objective function based on decision variables, the supply constraint condition is established based on the bill of materials, the manufacturing cycle and the transferring data, the supply constraint condition is used for constraining the stock states of the factory node and the warehouse node to be stable, the stock states are determined based on the manufacturing cycle and the transferring data, the decision variables are obtained based on the requirements of the target product, and the objective function is used for indicating the optimization target of the production plan of the target product;

and calling a solver to solve the target model to obtain the scheduling plan.

2. The method of claim 1, wherein the input data further comprises a line material relationship of the plant node; the transfer data includes a transfer path and a transfer period; the constructing a target model based on the input data includes:

acquiring a target factory node for processing the target product and a target warehouse node for storing materials corresponding to the target product from the nodes based on the BOM, the production line material relation and the transfer path;

a material inventory equation for each of the target plant node and the target warehouse node is established as the supply constraint based on the manufacturing cycle and the diversion cycle.

3. The method of claim 2, wherein the establishing a stock of materials equation for each of the target plant node and the target warehouse node based on the manufacturing cycle and the diversion cycle as the supply constraint comprises:

calculating to obtain a machining new item and a machining consumption item of the target factory node according to the BOM and the manufacturing period;

calculating to obtain a transfer-in newly-added item and a transfer-out consumption item of the target factory node and the target warehouse node according to the transfer path and the transfer period;

Calculating to obtain an inventory entry according to the processing new entry and the transfer new entry, and calculating to obtain an inventory sales entry according to the processing consumption entry and the transfer consumption entry;

substituting the stock entry and the stock sales term into a preset stock relation, and establishing the material stock equation as the supply constraint condition.

4. A method according to any one of claims 1 to 3, wherein the input data comprises demand priority information of the target product; the constructing a target model based on the input data includes:

and establishing the objective function based on the demand priority information.

5. The method of claim 4, wherein the input data further comprises a business objective; the establishing the objective function based on the demand priority information includes:

establishing an initial function according to the business objective, wherein the initial function is a function indicating an optimization objective of a production scheduling plan of the objective product when the demand priority of the objective product is not considered;

determining a penalty coefficient according to the demand priority information, wherein the penalty coefficient is used for adjusting the priority of each decision variable in the initial function under the service target;

And giving the punishment coefficient to a corresponding decision variable in the initial function to obtain the objective function.

6. The method of claim 5, wherein prior to said determining a penalty factor based on said demand priority information, said method further comprises:

calculating a feasible domain of a preset constraint element function according to a target constraint element corresponding to the decision variable, wherein the target constraint element is a constraint element which influences the priority of the decision variable under the business target when solving the initial function;

the determining a penalty coefficient according to the demand priority information comprises the following steps:

and determining the punishment coefficient according to the demand priority of the decision variable and the feasible domain of the constraint element function.

7. The method according to any one of claims 1 to 6, wherein the acquiring input data comprises:

the input data is obtained from an enterprise resource planning ERP system or manufacturing execution system MES constructed scheduling computer network based on the various nodes in the provisioning hierarchy.

8. A data processing apparatus, the apparatus comprising:

A modeling unit configured to construct a target model based on the input data, the target model including a supply constraint condition and an objective function based on decision variables, wherein the supply constraint condition is established based on the manufacturing cycle and the diversion data, the supply constraint condition is used to constrain an inventory state of the plant node and the warehouse node to remain stable, the inventory state is decided based on the manufacturing cycle and the diversion data, the decision variables are obtained based on requirements of the target product, and the objective function is used to indicate an optimization target of a production plan of the target product;

9. The apparatus of claim 8, wherein the input data further comprises a line material relationship of the plant node; the transfer data includes a transfer path and a transfer period; the modeling unit is specifically configured to:

10. The apparatus according to claim 9, wherein the modeling unit is specifically configured to:

11. The apparatus according to any one of claims 8 to 10, wherein the modeling unit is specifically configured to:

12. The apparatus of claim 11, wherein the input data further comprises a business objective; the modeling unit is specifically configured to:

13. The apparatus of claim 12, wherein the apparatus further comprises:

the calculating unit is used for calculating the feasible domain of a preset constraint element function according to a target constraint element corresponding to the decision variable, wherein the target constraint element is a constraint element which influences the priority of the decision variable under the business target when the initial function is solved;

the modeling unit is specifically configured to:

14. The apparatus according to any one of claims 8 to 13, wherein the acquisition unit is specifically configured to:

15. A computer device, comprising:

a processor, a memory;

the memory stores instruction operations or codes;

the processor is configured to communicate with the memory and execute instruction operations or code in the memory to perform the method of any of claims 1 to 7.

16. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 7.

17. A computer program product comprising computer readable instructions which, when run on a computer device, cause the computer device to perform the method of any of claims 1 to 7.