WO2024065407A1 - Production scheduling method, electronic device and storage medium - Google Patents

Abstract

Provided in the present disclosure are a production scheduling method, an electronic device and a storage medium. The production scheduling method comprises: acquiring scheduling data for performing production scheduling on a first product; in response to production data of the first product being missing in the scheduling data, supplementing the scheduling data with production data of a second product associated with the first product; and performing production scheduling on the first product by using the supplemented scheduling data.

Description

Production scheduling method, electronic device and storage medium Technical Field

The present disclosure relates to the technical field of production scheduling, and in particular to a production scheduling method, electronic equipment, storage medium, and computer program product.

Background technique

In the field of production scheduling, due to the diversity and complexity of product manufacturing processes, product models, materials, and production sequences, the collection and processing of scheduling data is particularly important. However, in the process of product production scheduling, the scheduling data obtained from various business systems is usually complex and diverse, which affects the accuracy of production scheduling.

Summary of the invention

The present disclosure provides a production scheduling method, an electronic device, a storage medium, and a computer program product.

According to one aspect of the present disclosure, a production scheduling method is provided, comprising: acquiring scheduling data for performing production scheduling on a first product; in response to the fact that production data of the first product is missing from the scheduling data, supplementing the scheduling data with production data of a second product associated with the first product; and performing production scheduling on the first product using the supplemented scheduling data.

According to an embodiment of the present disclosure, supplementing scheduling data with production data of a second product associated with a first product includes: determining the second product associated with the first product; generating production data of the first product based on the production data of the second product; and adding the generated production data of the first product to the scheduling data.

According to an embodiment of the present disclosure, determining the second product associated with the first product includes: determining a research and development product for the first product as the second product associated with the first product.

According to an embodiment of the present disclosure, the production data includes a bill of materials, and generating the production data of the first product based on the production data of the second product includes: changing the product identification in the bill of materials of the second product to the product identification of the first product to obtain a changed bill of materials; and storing the changed bill of materials as the bill of materials of the first product.

According to an embodiment of the present disclosure, determining a second product associated with a first product includes: determining a similar product of the first product from a plurality of candidate products as the second product associated with the first product, wherein a product identifier of the similar product at least partially matches a product identifier of the first product.

According to an embodiment of the present disclosure, the production data includes production process information, and generating the production data of the first product based on the production data of the second product includes: storing the production process information of a similar product as the production process information of the first product.

According to an embodiment of the present disclosure, determining similar products of a first product among multiple candidate products includes: comparing the first field of the product identification of each candidate product with the first field of the product identification of the first product; if the first field of the product identification of the candidate product matches the first field of the product identification of the first product and the candidate product has production process information, then determining the candidate product as a similar product of the first product.

According to an embodiment of the present disclosure, determining similar products of the first product among multiple candidate products also includes: if the first field of the product identification of each candidate product does not match the first field of the product identification of the first product or the matching candidate product does not have production process information, then comparing the second field of the product identification of each candidate product with the second field of the product identification of the first product, wherein the second field partially overlaps with the first field; if the second field of the product identification of the candidate product matches the second field of the product identification of the first product and the candidate product has production process information, then determining the candidate product as a similar product of the first product.

According to an embodiment of the present disclosure, the method further includes: receiving user configuration information, and determining the first field and the second field for comparison according to the user configuration information.

According to an embodiment of the present disclosure, the product identification is an N-bit string, each bit in the N-bit string has a corresponding priority, wherein the priority of at least one bit not covered by the first field is lower than the priority of at least one bit not covered by the second field, and N is an integer greater than 1.

According to an embodiment of the present disclosure, the method further includes: determining the inventory of the first product; and using the inventory of the first product to offset the demand for the first product in the scheduling data.

According to an embodiment of the present disclosure, using the supplemented scheduling data to schedule production of the first product includes: using the supplemented scheduling data to establish a data snapshot; using the established data snapshot to generate a data model; and using the data model to execute production scheduling for the first product.

According to an embodiment of the present disclosure, the data model includes an association between a semi-finished product and production resources, materials and processes for producing the semi-finished product, an association between a finished product and production resources, materials and processes for producing the finished product, and an association between the finished product and the semi-finished product; and using the data model to execute production scheduling for the first product includes:

Performing production scheduling for the first product based on the linear programming solution model to obtain a first scheduling result for the first product, wherein the first scheduling result includes a plurality of first items, each of which includes a production date, a production quantity, and corresponding production resources of the product; the linear programming solution model includes restriction conditions for the bottleneck process and an objective function;

Merge multiple first entries corresponding to the same product whose production date falls within the same time range into a second entry to obtain multiple second entries, each of which includes the production quantity of the same product within a time range;

For each product in the second entry, obtain orders corresponding to the product and sort the orders of the product according to at least one of order delivery date and order priority to obtain an order sorting result;

Extracting the production requirements of each process from each order, where the production requirements of each process include the quantity of finished products or semi-finished products planned to be produced by the process; and

Based on the data model and the order sorting result, the production demand of each order is allocated to the corresponding production resources and production time periods to obtain a second scheduling result, which includes the production demand corresponding to each production resource in each production time period.

According to another aspect of the present disclosure, there is provided a production scheduling system, comprising:

a stored procedure engine, configured to obtain scheduling data for scheduling production of a first product, and in response to the production data of the first product being missing from the scheduling data, supplement the scheduling data with production data of a second product associated with the first product;

A temporary database for storing the scheduling data;

The scheduling engine is used to schedule production of the first product using the supplemented scheduling data.

According to an embodiment of the present disclosure, the stored process engine is further used to establish a data snapshot using the supplemented scheduling data, and to generate a data model using the established data snapshot; the scheduling engine is used to execute production scheduling for the first product using the data model;

The production scheduling system also includes:

A basic database, which is in communication with the temporary database and is used to store the data snapshot; and

The modeling conversion database is connected to the basic database for storing the data model.

According to an embodiment of the present disclosure, the production scheduling system further includes: a scheduling database, which is in communication connection with the modeling conversion database and is used to store data used by the scheduling engine.

According to an embodiment of the present disclosure, the production scheduling system further includes an optimizer engine and an optimizer database, wherein the optimizer database is communicatively connected to the modeling conversion database and is used to store data used by the optimizer engine.

According to an embodiment of the present disclosure, the production scheduling system further includes:

A user interaction engine, used to receive configuration information input by a user;

A user database, which is in communication with the user interaction engine and the basic database and is used to store user information and user permissions; and

A manager is used to perform operations with the highest authority in the production scheduling system.

According to another aspect of the present disclosure, there is provided an electronic device, comprising:

A memory and a processor, wherein the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor executes the method as described above.

According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute and implement the method as described above.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method described above when executed by a processor.

It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

FIG1 is a flow chart of a production scheduling method according to an embodiment of the present disclosure;

FIG2 is a flow chart of a production scheduling method according to another embodiment of the present disclosure;

FIG3 is a schematic diagram of a scheduling data processing method according to an embodiment of the present disclosure;

4 is a flowchart of a method for performing production scheduling on a first product using supplemented scheduling data according to an embodiment of the present disclosure;

FIG5 is a block diagram of a production scheduling system according to an embodiment of the present disclosure; and

FIG. 6 is a block diagram of an electronic device for implementing the production scheduling method according to an embodiment of the present disclosure.

Detailed ways

The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

It should be noted that the sequence numbers of the operations in the following method are only used as representations of the operations for the purpose of description, and should not be regarded as representing the execution order of the operations. Unless explicitly stated, the method does not need to be executed completely in the order shown.

FIG1 is a flow chart of a production scheduling method according to an embodiment of the present disclosure.

As shown in FIG1 , the production scheduling method includes operations S110 to S130. The method of the embodiment of the present disclosure may be a computer-implemented method. For example, a computer may be used to implement the production scheduling method of the embodiment of the present disclosure based on an Advanced Planning and Scheduling System (APS). The APS system is a system that automatically generates factory production plans and production schedules using an optimization algorithm, taking into account resource constraints such as materials, equipment, personnel, production capacity, customer demand, and transportation.

In operation S110 , scheduling data for production scheduling of a first product is acquired.

In operation S120 , in response to the fact that production data of the first product is missing from the scheduling data, the scheduling data is supplemented with production data of a second product associated with the first product.

In operation S130, production scheduling is performed on the first product using the supplemented scheduling data.

According to an embodiment of the present disclosure, the scheduling data may include, for example, sales demand data and production information data. The sales demand data may include, for example, sales demand information related to the product (such as order delivery date, sales and inventory plan, etc.). The production information data may include, for example, material demand and supply situation, bill of materials (BOM, Bill of Material), production process information, production cycle, semi-finished product inventory information, process yield rate, etc. In an embodiment of the present disclosure, when the scheduling data is complete, the production schedule of the first product can be performed according to the acquired scheduling data.

According to an embodiment of the present disclosure, the above-mentioned scheduling data can be obtained from other business systems using the APS system. For example, the APS system can be used to obtain sales demand information (such as order delivery, sales and inventory plans, etc.) from the Order Management System (OMS), obtain data related to product or semi-finished product inventory information from the Enterprise Resource Planning (ERP) system, obtain data related to material demand and supply plan from the Material Requirement Planning (MRP) system, obtain data related to production process from the PLM (Product Lifecycle Management)/MDS (Master Data Management) integrated system, obtain data related to production performance or working conditions (such as process yield) from the Manufacturing Execution System (MES), or obtain data related to revenue, profit or cost planning from the Business Planning and Consolidation (BPC) system. Data interaction can be achieved between the above systems. For example, for the MRP system, in addition to providing the APS system with data related to material demand and supply plans, the MPS system can, for example, obtain the corresponding production plan from the APS system, obtain the Bill of Materials (BOM) information from the PLM/MDM integrated system, obtain data related to material inventory from the ERP system, and provide material procurement information (such as purchase requisition PR) to the ERP system. Based on the data interaction function between the above systems, the APS system can regularly (or irregularly) obtain scheduling data for production scheduling of the first product.

According to an embodiment of the present disclosure, the scheduling data of the first product refers to data related to the production schedule of the first product, and may include, for example, production data and/or sales demand information, etc., which may be determined according to actual conditions. In one example, the above-mentioned production data may include, but is not limited to, production process information, material demand and supply information, a bill of materials, etc.

It should be noted that the "missing" mentioned in the present disclosure refers to partial missing or complete missing. For example, the so-called "the production data of the first product is missing in the scheduling data" may mean that the production data of the first product exists in the scheduling data, but the production data is incomplete; or it means that the production data of the first product does not exist in the scheduling data. For ease of understanding, the following example takes the production data of the first product including the bill of materials and production process information as an example to illustrate the situation where the production data of the first product is missing in the scheduling data.

In an example, the fact that the production data of the first product is missing from the scheduling data may mean that the scheduling data includes a bill of materials and production process information, but both the bill of materials and the production process information are incomplete.

In another example, the missing production data of the first product in the scheduling data may mean that the scheduling data includes a complete or incomplete bill of materials but no production process information; or the scheduling data includes complete or incomplete production process information but no bill of materials.

In another example, the lack of production data of the first product in the scheduling data may mean that the scheduling data does not include both the bill of materials and the production process information.

In view of the importance of scheduling data for product production scheduling, before using the scheduling data to schedule the production of the first product, the integrity of the production data in the scheduling data can be confirmed. In the case where the production data of the first product is missing in the scheduling data, the production data of the second product associated with the first product can be used to supplement the scheduling data, and then the supplemented scheduling data is used to schedule the production of the first product, thereby avoiding the failure of the production scheduling of the first product due to the lack of corresponding production data in the scheduling data, and improving the efficiency and accuracy of the production scheduling of the first product.

FIG. 2 is a flow chart of a production scheduling method according to another embodiment of the present disclosure.

As shown in FIG2 , according to an embodiment of the present disclosure, the production scheduling method includes operations S210 to S240. Operation S230 may include operations S231 to S233. Operations S210 and S240 may be implemented in the same or similar manner as operations S110 and S130, respectively, and the repeated parts will not be described in detail.

In operation S210, scheduling data for production scheduling of a first product is acquired.

In operation S220, it is determined whether the production data of the first product is missing in the scheduling data, and if so, operation S231 is performed; if not, operation S240 is performed.

In operation S231 , a second product associated with the first product is determined.

In operation S232, production data of the first product is generated based on the production data of the second product.

In operation S233, the generated production data of the first product is added to the scheduling data.

In operation S240, production scheduling is performed on the first product using the scheduling data.

According to an embodiment of the present disclosure, after obtaining the scheduling data for production scheduling of the first product, it can be determined whether the production data of the first product is missing in the scheduling data. When it is determined that the production data of the first product is not missing in the scheduling data, that is, the production data in the scheduling data is sufficient for production scheduling of the first product, the scheduling data can be directly used to schedule the production of the first product. When it is determined that the production data of the first product is missing in the scheduling data, the second product associated with the first product can be determined, and then the production data of the first product can be generated based on the production data of the second product, so that the generated production data of the first product is added to the scheduling data, and the supplemented scheduling data is used to schedule the production of the first product. In this way, accurate supplementation of the scheduling data is achieved, thereby improving the efficiency and accuracy of production scheduling for the first product.

It is understandable that for some new products that have been developed but not yet mass-produced, in the process of scheduling their production, there may be a situation where the production demand has been issued, but the corresponding bill of materials has not yet been released. In view of the above situation, it will be impossible to use the APS system to schedule their production. Therefore, when it is determined that the production data of the first product is missing in the scheduling data, and the production data is the bill of materials of the first product, the R&D product of the first product can be used as the second product associated with the first product.

According to an embodiment of the present disclosure, when a research and development product for a first product is determined as a second product associated with the first product, generating production data for the first product based on production data of the second product may include the following operations: changing the product identification in the bill of materials of the second product to the product identification of the first product, obtaining a changed bill of materials, and storing the changed bill of materials as the bill of materials of the first product.

The bill of materials is a technical document that describes which parts and raw materials a product is made of, as well as information such as the quantity and property description of these raw materials and parts. For the same product, there are two types: mass products and R&D products. The mass product is the product manufactured in the mass production stage, and the R&D product is the product manufactured in the R&D stage. In the actual production process, the bill of materials may include a design bill of materials for manufacturing the R&D product and a manufacturing bill of materials for manufacturing the mass product. Since the R&D product and the mass product correspond to the same product, the material information in the design bill of materials is basically the same as the material information in the manufacturing bill of materials. According to an embodiment of the present disclosure, the R&D product corresponding to the mass product can be determined, and the design bill of materials corresponding to the R&D product can be converted as the manufacturing bill of materials corresponding to the mass product, so that the factory can produce the mass product according to the manufacturing bill of materials. In an embodiment of the present disclosure, the product identification in the design bill of materials is different from the product identification in the manufacturing bill of materials. The conversion of the design bill of materials to obtain the manufacturing bill of materials is essentially the conversion between the materials of the design bill of materials and the materials of the manufacturing bill of materials. Therefore, the conversion of the design bill of materials to the manufacturing bill of materials is essentially the conversion of the product identification.

Based on the above mechanism, the R&D product of the first product can be used as the second product associated with the first product, so that when the manufacturing bill of materials of the first product is missing in the scheduling data, the manufacturing bill of materials of the first product can be determined according to the design bill of materials of the R&D product of the first product. Take the first product with a product identification of P1234567_123_8110 as an example, the product identification of the R&D product corresponding to the first product is P1234567_123_D110. The materials used in the manufacturing of the same product in the R&D stage and the mass production stage are highly similar in terms of material type, dosage, etc., that is, the material information of the R&D product and the material information of the mass product corresponding to the R&D product are highly similar. In the embodiment of the present disclosure, in the case of a known mass product product identification, if the material information of the mass product is partially or completely missing due to various reasons (for example, the mass product is a new product that is mass-produced for the first time), the R&D product corresponding to the mass product can be found, and the material information of the R&D product is used to generate the material information of the mass product, thereby completing the missing part.

According to an embodiment of the present disclosure, when a research and development product for a first product is determined as a second product associated with the first product, the product identifier in the bill of materials of the research and development product of the first product can be changed to the product identifier of the first product, thereby obtaining the bill of materials of the first product. The generated bill of materials of the first product is then added to the scheduling data. In this way, the scheduling data is supplemented with the production data of the second product associated with the first product.

The following is an exemplary description of the process of supplementing the scheduling data with the production data of the second product associated with the first product in combination with Table 1 and Table 2. Those skilled in the art should understand that the information shown in Table 1 and Table 2 is only used to illustrate the technical solution of the present disclosure, and does not represent the content of the bill of materials. The content in the bill of materials is not limited to the content shown in Table 1 and Table 2, and the content in the bill of materials can be set in any other way as needed.

Table 1

Table 2

Table 1 schematically shows the bill of materials of some R&D products for the first product. As shown in Table 1, the bill of materials of the R&D products of the first product includes multiple data entries, each of which includes but is not limited to product source identification, product identification, product demand, material source identification, material usage, etc. Taking the first product with product identification P1234567_123_8110 as an example, the product identification of the R&D product corresponding to the first product is P1234567_123_D110. As shown in Table 1, in the bill of materials of the R&D product (i.e., the product with product identification P1234567_123_D110), the product demand is 1000, the material source identification is A1, and the material usage is 1000 (the unit depends on the type of material).

For example, for the first product with a product identification of P1234567_123_8110, when it is determined that the bill of materials of the first product is missing in the scheduling data, the research and development product of the first product (with a product identification of P1234567_123_D110) can be used as the second product associated with the first product. Then, the product identification P1234567_123_D110 in the bill of materials of the research and development product in Table 1 can be changed to the product identification P1234567_123_8110 of the first product, that is, the symbol "D" representing the research and development product in the product identification is changed to the symbol "8" representing the mass product (only as an example), thereby obtaining the changed bill of materials as shown in Table 2. The bill of materials in Table 2 can be stored as the bill of materials of the first product. Thus, the scheduling data is supplemented by the production data of the second product associated with the first product. Of course, the embodiments of the present disclosure are not limited to this. In some embodiments, considering that there are certain differences in the material information used by the product in the research and development and mass production stages, the information of the bill of materials in Table 2 can be deleted, added and changed.

According to the embodiments of the present disclosure, for some products that lack production process information, it is also impossible to use the APS system to schedule them. Therefore, when it is determined that the production data of the first product is missing in the scheduling data, and the production data is the production process information of the first product, a similar product of the first product can be determined from multiple candidate products as the second product associated with the first product, and the production process information of the similar product can be stored as the production process information of the first product, thereby realizing the use of the production data of the second product associated with the first product to supplement the scheduling data.

According to an embodiment of the present disclosure, a similar product of a first product refers to a product identification of a similar product that at least partially matches the product identification of the first product. It is understood that each product has a unique product identification (ITEM_ID). The product identification can be an N-bit string, where N is an integer greater than 1, and each bit in the N-bit string can represent a different meaning. Taking the product as a display panel as an example, the N bits of the product identification can respectively represent various information related to the display panel, such as but not limited to the category, display mode, size, resolution, production plant, finished product material number, etc. of the display panel. Therefore, the correlation between the product identifications of different products can reflect the similarity of these products in the production process to some extent. According to an embodiment of the present disclosure, the affinity between products can be identified by product identification. For example, if some fields of the product identifications of two products are matched, it can be considered that the two products have similarities in the production process, and the production process information (also called route information) of one of the products can be considered as the production process information of the other product. On the contrary, if some or all fields of the product identifications of the two products do not match, it can be considered that there are large differences in the production processes of the two products and they cannot be replaced by each other.

Taking the first product as an example, the display panel with a 17-bit product identification, assuming that the product identification of the first product is P1234567_123_8110, if the product identification of another product (the second product) matches the product identification of the first product in some fields, then the second product and the first product have similarities in the production process. For example, assuming that the product is a display panel with a 17-bit product identification as described above, wherein the 17th bit is usually empty (i.e., it has no meaning and is only for backup or other purposes), then if the 1st to 16th bits of the product identification of the two products are matched, it indicates that the two products are consistent in various aspects such as product category, display mode, size, resolution, etc. The above is only an example of a product identification, and the embodiments of the present disclosure are not limited thereto. The number of digits of the product identification and the meaning represented by each bit can be set according to actual needs.

Similar products of the first product can be found based on the product identifier, and the similar product can be used as the second product associated with the first product, so that when it is determined that the production process information of the first product is missing in the scheduling data, the production process information of the first product can be obtained based on the production process information of the similar product. The similar product of the first product means that the product identifier of the similar product at least partially matches the product identifier of the first product. In this way, it can be ensured that the production process information of the similar product is as close as possible to the production process information actually required to be adopted by the first product, so as to improve the accuracy of the production process information of the first product.

According to an embodiment of the present disclosure, determining similar products of a first product among a plurality of candidate products may include the following operations.

The first field of the product identification of each candidate product is compared with the first field of the product identification of the first product; if the first field of the product identification of the candidate product matches the first field of the product identification of the first product and the candidate product has production process information, the candidate product is determined to be a similar product of the first product.

According to an embodiment of the present disclosure, after comparing the first field of the product identification of each candidate product with the first field of the product identification of the first product, if there is at least one candidate product whose first field of the product identification matches the first field of the product identification of the first product and the candidate product has production process information, a similar product to the first product can be determined from the at least one candidate product. In some embodiments, the first field of the product identification of at least one candidate product can be further compared with the first field of the product identification of the first product, until a candidate product that is closest to the first field of the product identification of the first product is determined as a similar product to the first product.

According to an embodiment of the present disclosure, determining similar products of the first product among multiple candidate products may further include the following operations.

If the first field of the product identification of each candidate product does not match the first field of the product identification of the first product or the matching candidate product does not have production process information, the second field of the product identification of each candidate product is compared with the second field of the product identification of the first product, where the second field partially overlaps with the first field; if the second field of the product identification of the candidate product matches the second field of the product identification of the first product and the candidate product has production process information, the candidate product is determined to be a similar product of the first product.

According to an embodiment of the present disclosure, multiple second fields may be set to perform multiple rounds of matching. For example, when a similar product to the first product is not matched using the first field, a second field partially overlapping the first field may be used to perform matching determination. When a similar product to the first product is not matched using one second field, another second field may be used to perform matching determination; the operation is repeated until at least one candidate product is determined to be a similar product to the first product, or it is determined that all candidate products are not similar products.

The following takes the example of the first product whose product identifier ITEM_X is the 17-digit string (eg, P1234567_123_8110) as an example to illustrate the example process of finding similar products.

First, identify the 17-digit product identifier of the product in the current demand that has bill of materials data but no production process information. Next, one or more matching operations can be performed. In each matching operation, a specified field of the product identifier is matched to find similar products of the first product; if the match fails, the next matching operation is performed to match another specified field of the product identifier. Here, each matching operation involves the field that failed to match last time and the field to be matched currently. The former is called the first field and the latter is called the second field. In other words, each matching operation has a corresponding first field and a second field. If the second field of the current matching operation fails to match, then the second field is the first field for the next matching operation. The following will illustrate multiple matching operations with examples.

In the matching operation S1, a product identifier in the process site table is searched for a product identifier whose 1st to 16th character string (first field) is the same as ITEM_X. If such a product identifier is found, it means that the product corresponding to the product identifier is the same as the first product except for the last digit. The last digit is usually empty, and it can be considered that the production process of the product corresponding to the found product identifier is basically the same as that of the first product, and the production process information of the product can be used as a first choice to be converted into the production process information of the first product. For example, each production plant corresponding to the product identifier can be mapped to ITEM_X, thereby generating the production process information of the first product. For example, when the product identification ITEM_X of the first product is P1234567_123_8110, if a product identification whose 1st to 16th digits match P1234567_123_8110 is found in the process site table, for example, P1234567_123_8112, then the product with the product identification P1234567_123_8112 can be used as a similar product of the first product, and each production plant corresponding to the product identification P1234567_123_8112 of the similar product can be mapped to the product identification P1234567_123_8110 of the first product, thereby obtaining the production process information of the first product.

Next, in matching operation S2, if a product whose 1st-16th character string (the first field in S2) is the same as ITEM_X is not found in matching operation S1, the product identification whose 1st-11th and 13th-17th character strings (the second field in S2) in the process site table match ITEM_X can be searched. If such a product identification is found (for example, P1234567_124_8110), it means that the product corresponding to the product identification is the same as the first product except for the production plant. It can be considered that the product corresponding to the product identification found is the same product as the first product produced in different factories, and it is feasible to use the production process of the former to produce the latter. The factory information corresponding to the found product identification can be mapped to ITEM_X to obtain the production process information of the first product, otherwise proceed to the next step. The matching and mapping method in this step is similar to the above steps and will not be repeated here.

Next, in the matching operation S3, if the product with the same 1st-11th and 13th-17th bits as ITEM_X (the first field in S3) is not found in the matching operation S2, the product identification of the 2nd-17th bit string (the second field in S3) in the process site table that matches ITEM_X can be found. Assume that the first bit of the 17-bit product identification that is not involved in the match represents the product category, for example, it indicates whether the display panel is used for mobile phones, televisions, or display panels in vehicle-mounted equipment. Then, if a matching product identification is found, it means that although the product corresponding to the product identification is a different type of product from the first product (for example, the display panel of a laptop computer and the display panel of a vehicle-mounted device, respectively), the two have the same size, resolution, thickness, IC factory, etc., and it is feasible to apply the production process information of the former to the production and manufacturing of the latter. The factory information corresponding to the found production identification can be mapped to ITEM_X to obtain the production process information of the first product.

Next, in matching operation S4, if the product with the same 2-17th bit as ITEM_X (the first field in S4) is not found in matching operation S3, the product identification of the 1-11th and 13-16th bit strings (the second field in S4) in the process site table that matches ITEM_X can be found. Assuming that the 12th bit of the 17-bit product identification that is not involved in the match represents the production plant, if a matching product identification is found, it means that although the product corresponding to the product identification is produced by a different factory than the first product, the two have the same size, resolution, thickness, IC factory, etc., and it is feasible to apply the former's production process information to the latter's production. The factory information corresponding to the found product identification can be mapped to ITEM_X, thereby obtaining the production process information of the first product.

Next, in the matching operation S5, if the product with the same 1-11 and 13-16 bits (i.e., the first field in S5) as ITEM_X is not found in the above matching operation S4, the product identification of the 2-12 and 13-16 bit strings (the second field in S5) in the process site table that matches ITEM_X can be found. Assume that the first bit that is not involved in the match in the 17-bit product identification represents the product category, for example, it indicates whether the display panel is used in mobile phones, televisions, or display panels in vehicle-mounted equipment. Then, if a matching product identification is found, it means that the product corresponding to the product identification is different from the product category of the first product, but the two are the same in terms of size, resolution, thickness, IC factory, etc., and it is feasible to apply the production process information of the former to the production and manufacturing of the latter. Map the factory information corresponding to the found product identification to ITEM_X to obtain the production process information of the first product.

Next, in matching operation S6, if the product whose 2nd-12th and 13th-16th bits (the first field in S6) are the same as ITEM_X is not found in matching operation S5, the product identification whose 2nd-11th and 13th-16th bit strings (the second field in S6) in the process site table match ITEM_X can be found. If such a product identification is found, it means that the product corresponding to the product identification is different from the product category and factory of the first product, but the two have the same size, resolution, thickness, IC factory, etc., and it is feasible to apply the production process information of the former to the production of the latter. The factory information corresponding to the found product identification is mapped to ITEM_X, thereby obtaining the production process information of the first product.

Next, in the matching operation S7, if the product with the same 2-12 and 13-16 bits as ITEM_X is not found in the matching operation S6, the product identifier of the 1-15 bit string (the second field in S7) in the process site table that matches ITEM_X can be found. Assuming that the 16th and 17th bits that are not involved in the matching are usually empty, if a matching product identifier is found, it means that the product corresponding to the product identifier has basically the same production process as the first product, and its corresponding factory information can be mapped to ITEM_X to obtain the production process information of the first product, otherwise proceed to the next step.

Next, in the matching operation S8, if the product with the same 1st-15th bit (the first field in S8) as ITEM_X is not found in the matching operation S7, the product identification of the 1st-14th bit string (the second field in S8) in the process site table that matches ITEM_X can be found. Assuming that the 15th bit that is not involved in the match represents the material number of the product, and the 16th-17th bits are usually empty, then if a matching product identification is found, it means that the product corresponding to the product identification has a different product material number from the first product, but the two have the same size, resolution, thickness, IC factory, etc., and it is feasible to apply the production process information of the former to the production of the latter. Map the factory information corresponding to the found product identification to ITEM_X to obtain the production process information of the first product, otherwise proceed to the next step.

Next, in matching operation S9, if the product with the same 1st-14th bits (the first field in S9) as ITEM_X is not found in matching operation S8, the product identification whose 14th bit string (the second field in S9) matches ITEM_X in the process site table can be found. Assuming that the 14th bit represents the supplier of the timing drive circuit in the display panel, if a matching product identification is found, it means that the product corresponding to the product identification uses the same timing drive circuit supplier as the first product, but is different in other aspects. This can be used as a lower limit standard for alternative production processes. The property factory corresponding to the found product identification is used as the production factory of the first product, thereby obtaining the production process information of the first product.

It should be noted that, in the examples given above, the number of matching operations, the execution order, and the use of the first field and the second field in each match are all exemplary and are only used to illustrate the technical solution of the present disclosure and may be set in any other manner as needed.

According to an embodiment of the present disclosure, the first field, the second field, and the execution order of the matching operation for each field can be configured by the user. For example, the user configuration information can be received through the user interaction engine, and the execution order of the first field and the second field for comparison and the matching operation for each field can be determined according to the user configuration information. According to an embodiment of the present disclosure, the length of the first field and the length of the second field can be set according to the actual needs of the user, as long as the similar products of the first product can be determined from multiple candidate products, and the present disclosure does not limit this. For example, a corresponding priority can be assigned to the N-bit string of the product identification, and when performing field matching, one or more bits can be discarded in order from low to high priority for matching, that is, in each matching operation, it is ensured that the priority of at least one bit not covered by the first field is lower than the priority of at least one bit not covered by the second field. The priority of the N-bit string can be set according to the degree of influence of the indicator or parameter represented by each character on the scheduling. Taking the 17-bit product identification as described above as an example, the 16th-17th bit is usually empty, and its influence on the scheduling is basically negligible, so it can have the lowest priority, followed by the 12th bit representing the production plant, and then the first bit representing the product category, and so on. The bits corresponding to some parameters (such as size, resolution, etc.) that have a substantial impact on the scheduling results can have a relatively high priority. In the above matching step, the 17th bit can be discarded first, and the 1st to 16th bits can be matched; then the 12th bit can be discarded, and the 1st to 11th and 13th to 17th bits can be matched; then the 1st bit can be discarded, and the 2nd to 17th bits can be matched, and so on. In this way, the requirements for the similarity between the production process of the product to be found and the first product to be scheduled can be gradually relaxed, so as to find the product with the production process most similar to the first product to be scheduled.

According to an embodiment of the present disclosure, before using the supplemented scheduling data to schedule the production of the first product, the demand for the first product can be offset to deduct the quantity of the first product currently produced, thereby obtaining more accurate scheduling data and achieving more accurate production scheduling. For example, the inventory of the first product can be determined, and the inventory of the first product can be used to offset the demand for the first product in the scheduling data.

FIG. 3 is a schematic diagram of a scheduling data processing method according to an embodiment of the present disclosure.

As shown in FIG. 3 , according to an embodiment of the present disclosure, the scheduling data processing method includes operations S301 to S313 .

In operation S301, the scheduling data for production scheduling of the current product is obtained. According to an embodiment of the present disclosure, the scheduling data of the current product refers to data related to the production scheduling of the current product, and may include, for example, production data and/or sales demand data, etc. In the example where the current product is a new product, the production data includes, but is not limited to, production process information, material requirements and supply status information, bill of materials, etc. According to an embodiment of the present disclosure, the above-mentioned scheduling data can be obtained from various business systems, and the acquisition method is the same or similar to the process described above, which will not be repeated here. The obtained scheduling data can be stored in a database so that the scheduling data can be used later to schedule the production of the current product.

In operation S302, the product identification of the current product is stored. According to an embodiment of the present disclosure, the product identification of the current product can be obtained and stored in a database.

In operation S303, it is determined whether the bill of materials of the current product is missing in the scheduling data. If so, operations S304 to S305 are performed; if not, operation S306 is performed.

In operation S304, it is determined whether there is a R&D product for the current product, and if so, operation S305 is performed.

In operation S305 , the bill of materials of the current product is replaced with the bill of materials of the R&D product of the current product.

In operation S306, the bill of materials of the current product is stored.

For example, if it is determined that the bill of materials of the current product is missing in the scheduling data, and it is determined that the current product has a research and development product, the product identifier in the bill of materials of the research and development product of the current product can be changed to the product identifier of the current product to obtain the bill of materials of the current product. The bill of materials of the current product is then stored in the database.

In operation S307, it is determined whether the production process information of the current product is missing in the scheduling data. If so, operations S308 to S309 are performed; if not, operation S310 is performed.

In operation S308 , it is determined whether there is a similar product with production process information associated with the current product, and if so, operation S309 is performed.

In operation S309, the production process information of the current product is replaced with the production process information of a similar product.

In operation S310, production process information is stored.

According to an embodiment of the present disclosure, when it is determined that the production process information of the current product is missing in the scheduling data, it is determined whether there is a similar product with production process information associated with the current product. If so, the production process information of the similar product is stored as the production process information of the current product, that is, the production process information of the current product is replaced with the production process information of the similar product. Subsequently, the production process information of the current product is stored in a database.

In operation S311, WIP (Working in Process) data is stored.

In operation S312 , yield data is stored.

In operation S313, the sales demand data is stored.

According to the embodiments of the present disclosure, in order to ensure more accurate scheduling data and achieve more accurate production scheduling, the demand for the current product can also be offset to reduce the quantity of the current product that has been produced. Therefore, after obtaining WIP data, yield data, and sales demand data, the production demand for the current product can be determined based on these data and stored in the database.

In some embodiments, the scheduling data stored in the database can also be modified through the user interface so that the modified scheduling data is more in line with production reality, thereby improving the accuracy of the scheduling data of the current product, and further improving the efficiency and accuracy of product scheduling.

Fig. 4 is a flow chart of a method for performing production scheduling on a first product using scheduling data according to an embodiment of the present disclosure. An exemplary implementation of the above operation S240 is described below with reference to Fig. 4 .

As shown in FIG. 4 , the method for performing production scheduling on a first product using scheduling data includes operations S441 - S443 .

In operation S441 , a data snapshot is created using the scheduling data.

In operation S442, a data model is generated using the established data snapshot; and

In operation S443 , production scheduling for the first product is performed using the data model.

According to an embodiment of the present disclosure, the scheduling data can be obtained according to the method of the above embodiment and the scheduling data can be supplemented as needed. Then, a data snapshot can be established based on the scheduling data, and the data snapshot can be stored in a database. The data snapshot can be a fully usable copy of a specified data set, which includes an image of the corresponding data at a certain point in time. The data snapshot can be stored in a snapshot table, which is distinguished from the source data table, so as to achieve simultaneous access to the snapshot table and the source data table without affecting the continuous update of the business data in the source data.

According to an embodiment of the present disclosure, since the data snapshot stores the most original data, in order to facilitate the processing of the scheduling logic, the original data can be modeled and converted so as to be processed into a data structure that can be recognized by the background engine. The user's production scheduling requirements, such as production condition constraints, order priority sorting, etc., can be reflected in the constructed data model. In some embodiments, data not required for scheduling, such as non-critical material data, etc., can also be filtered out during the modeling process. In some embodiments, the bill of materials can also be streamlined. In this way, the model size can be reduced so that the modeling efficiency can be improved and the modeling time can be shortened. After the data modeling is completed, the data model can be temporarily stored in the modeling table and transferred to the corresponding target table when needed. At this point, the data preparation work is completed. The production schedule for the first product can be executed based on the established data model.

The established data model may, for example, include the association between semi-finished products and production resources, materials and processes used to produce the semi-finished products, the association between finished products and production resources, materials and processes used to produce the finished products, and the association between finished products and semi-finished products.

According to an embodiment of the present disclosure, based on a data model, various methods can be used to perform a production scheduling task for a first product. For example, the production scheduling of the first product can be performed based on a linear programming solution model to obtain a first scheduling result of the first product, the first scheduling result includes a plurality of first entries, each of which includes a production date, a production quantity, and corresponding production resources of the product, etc. The restriction conditions and the objective function for the bottleneck process in the linear programming solution model can be determined according to user configuration information. Then, the plurality of first entries corresponding to the same product whose production date falls within the same time range can be merged into a second entry to obtain a plurality of second entries, each of which includes the production quantity of the same product in a time range. For each product in each second entry, the order corresponding to the product is obtained and the order of the product is sorted according to at least one of the order delivery date and the order priority to obtain an order sorting result. The production requirements of each process can be extracted from each order, and the production requirements of each process include the number of finished products or semi-finished products planned to be produced by the process. Based on the data model and the order sorting result, the production requirements of each order can be allocated to the corresponding production resources and production time periods to obtain a second scheduling result, and the second scheduling result includes the production requirements corresponding to each production resource in each production time period.

According to an embodiment of the present disclosure, the production scheduling method described above is used to schedule the production of the first product, which can reduce the requirements for scheduling data. In other words, the production scheduling method in the present disclosure has a certain tolerance for missing or inaccurate data. Therefore, the production data of the product associated with the first product is used to complete the scheduling data of the first product, and the supplemented scheduling data is used to schedule the production of the first product, and the desired scheduling accuracy can still be achieved.

The following is an example of a method for executing production scheduling of a product according to an embodiment of the present disclosure.

In some embodiments, the bottleneck process can be first determined among many processes, and then the objective function can be linearly programmed according to the constraints of the bottleneck process to obtain the first scheduling result of the product. The linear programming solution of the objective function for the constraints of the bottleneck process can be implemented, for example, by an optimizer. Before the basic data (i.e., the above-mentioned production data) is input into the optimizer, the data can be preprocessed to convert the data into a preset format (e.g., TXT format) to facilitate the subsequent linear programming solution. After the optimizer reads the converted data, the objective function is linearly programmed according to the constraints of each bottleneck process to obtain the first scheduling result of the product. The first scheduling result includes multiple first entries, each of which includes the production date, production quantity and corresponding production resources of the product.

In the embodiments of the present disclosure, the bottleneck process involves related equipment, production lines, factories, materials, etc., and the restriction conditions of the bottleneck process may include at least one of the following: a first restriction condition on equipment capacity, a second restriction condition on production line priority, a third restriction condition on factory operating time, a fourth restriction condition on materials, and a fifth restriction condition on the number of line changes.

The first constraint indicates that the sum of the planned production volume of each device for the day * cycle time < equipment available time * equipment utilization rate. The second constraint indicates that the priority of the internal factory is the first priority, the priority of the external foundry is the second priority, and the first priority is lower than the second priority. The third constraint indicates that the factory transit time is within the preset range; the fourth constraint indicates that the amount of semi-finished products and materials used to manufacture display modules is within the preset range; the fifth constraint indicates that the number of display module models produced by each device per day is less than the preset value.

The objective function may, for example, include at least one of the following: a first objective function for maximizing the satisfaction of demand for display modules, a second objective function for minimizing the number of display modules with delayed delivery, a third objective function for maximizing the capacity utilization of equipment for producing display modules, a fourth objective function for minimizing factory operating time, and a fifth objective function for maximizing the time of continuous production of display modules on the same production line.

The first objective function is: Max (demand satisfaction), where demand satisfaction = the cumulative number of display modules that need to be delivered in multiple orders/the total demand for display modules. The second objective function is: Min (delayed delivery number), where the delayed delivery number = the cumulative number of display modules that need to be delivered outside the delivery period in multiple orders/the total demand for display modules. The third objective function is: Max (equipment capacity utilization), where equipment capacity utilization = the cumulative equipment usage time in one day/the cumulative (equipment available time * equipment utilization rate) in one day. The fourth objective function is: Min (inter-factory transit time), where the factory operating time is the cumulative inter-factory transportation time in multiple orders. The fifth objective function is: Max (the time for each model of display module to be continuously produced on the same production line). Max() represents maximization calculation, and Min() represents minimization calculation. In some embodiments, weights can be set for each objective function as needed. For example, the weights of the first objective function to the fifth objective function can be set to 1, 0.1, 0.001, 0.001, and 0.001, respectively.

In the embodiment of the present disclosure, the objective function is linearly programmed and solved according to the basic data corresponding to each bottleneck process and the restriction conditions of each bottleneck process, and the first scheduling result of the display module is obtained. This process can be understood as finding an optimal solution that meets the product production scheduling restriction parameters and the objective function based on the basic data corresponding to the bottleneck process of the product, using an optimizer (such as Xpress-Optimizer), that is, the first scheduling result of the product. Xpress-Optimizer is a solution engine in the Xpress-MP toolkit. Xpress-MP is a mathematical modeling and optimization toolkit for solving linear, integer, quadratic and random programming problems. The Xpress-MP toolkit can be used on a computer platform and has versions with different performances to solve problems of various scales. The algorithms contained in the Xpress-Optimizer in the Xpress-MP toolkit enable solving linear programming problems, mixed integer programming problems, quadratic programming problems and mixed integer quadratic programming problems. The present disclosure predicts the bottleneck process in advance and uses the basic data of the bottleneck process of the product process as the input of the linear programming result, thereby obtaining the first scheduling result that meets the product production scheduling restriction conditions and the objective function. Based on the above method, the mismatch in production rhythm between the previous and next processes and the drift of process bottlenecks can be alleviated or even avoided, thereby improving the capacity utilization rate of the production line.

Next, we will give an example to illustrate the process of product sorting and order sorting based on the first scheduling result of the product.

Table 3 schematically shows some of the first scheduling results for products. As shown in Table 3, the first scheduling result includes multiple first entries, each of which includes the production date, production quantity and corresponding production resources of the product. For example, in the entry with sequence number 1, the production date of the product with model X1 is 2021-9-14, the production quantity is 100, and the production resource is production line 4. In Table 3, the production date is calculated in days, but the embodiments of the present disclosure are not limited thereto, and the production date can be calculated in other units, such as weeks. The first scheduling result of the product is a production schedule under an ideal state given by comprehensively considering factors such as equipment capacity, production line priority, factory operation time, materials, and the number of production line changes. However, the production scheduling results of the product obtained based on the linear programming solution method have certain limitations, and it is difficult to express production continuity and production order. For example, in Table 3, product X1 is put into use at intervals between September 18 and September 21, and such a result deviates from the actual production demand. The so-called product model here can be represented by the product identification as described above, and different product identifications correspond to different product models.

Serial number Production Plant Production equipment Product number Production time quantity 1 Factory 1 Production Line 4 X1 2021-9-14 100 2 Factory 1 Production Line 4 X1 2021-9-15 100 3 Factory 1 Production Line 5 X1 2021-9-18 100 4 Factory 1 Production Line 5 X1 2021-9-21 50 5 Factory 2 Production Line 1 X2 2021-9-14 80 6 Factory 2 Production Line 2 X2 2021-9-14 50

table 3

In the disclosed embodiment, the difference between the first scheduling result of the product obtained by the linear programming solution method and the actual scheduling process is considered, such as the inability to express the production continuity and production order, and the first scheduling result of the product is sorted by product and order to obtain the order sorting result. For example, multiple first entries corresponding to the same product whose production date falls within the same time range can be merged into a second entry to obtain multiple second entries, each of which includes the production quantity of the same product in a time range.

After obtaining the first scheduling result of the product, the production order of various models of products can be adjusted so that multiple first entries corresponding to the same product whose production date falls within the same time range are merged into a second entry to obtain multiple second entries, each of which includes the production quantity of the same product in a time range. The above process can be understood as integrating the same products whose production dates fall within the same time range in different orders to obtain a sorting result based on the product (or product model) as the dimension. Based on the above method, the same products whose production dates fall within the same time range can be sorted together, thereby avoiding the situation of discontinuous production. According to an embodiment of the present disclosure, the above production date can be, for example, in units of days or weeks, and the time range can be, for example, in units of months or quarters. It can be set specifically according to the actual production scheduling situation and is not limited here.

In some embodiments, the integrated results can be sorted based on the product sorting dimension to obtain sorting results for various products (or product models). The above product sorting dimension can, for example, consider at least one of the following factors: the demand for the product within a time range, the number of available production lines corresponding to the product, and the production cycle of the product. Based on the above sorting method, when the production capacity is limited, the allocation of production capacity resources can be determined according to the adjusted product production order, thereby realizing the hierarchical utilization of production capacity and further improving the utilization rate of production capacity.

Table 4 schematically shows the scheduling results of the above-mentioned adjusted products. For example, within the time range of months, after the first scheduling result in Table 3 is adjusted, the first entry of all products with model X1 whose production date falls on 2021/8 is integrated into a second entry, and the second entry indicates that the planned production quantity of the product with model X1 in 2021/8 is 44523. Similarly, the first entry of all products with model X4 planned to be produced in 2021/8 is integrated into another second entry. And so on, multiple second entries as shown in Table 4 are obtained.

After integrating the above first items, the integrated results (i.e., the obtained multiple second items) can also be sorted to obtain the corresponding product production sorting (see the product sorting column in Table 4). As shown in Table 4, for multiple second items involving the same time range (for example, August 2021), the second items can be sorted according to the demand for the product within the time range, so as to determine the product sorting as: X1>X4>X2>X5>X6. In this way, a more realistic sorting result can be obtained.

Based on the above adjustment method, different orders can be combined. When the production capacity is limited, the allocation of production capacity resources can be determined according to the adjusted product production sequence, so as to achieve hierarchical utilization of production capacity and further improve the utilization rate of production capacity. In addition, based on the above method, discontinuous production can also be avoided.

Table 4

Next, for each product in the second entry, the orders corresponding to the product may be obtained and the orders for the product may be sorted according to at least one of the order delivery date and the order priority to obtain an order sorting result.

In the embodiment of the present disclosure, on the basis of the above-mentioned sorting result, the second item can be further sorted according to the order delivery date or order priority, and the above-mentioned sorting result is adjusted more finely, so as to obtain a more accurate product scheduling result. The order sorting dimension may include, for example, the order delivery date or order priority of the order to be sorted. Among them, the earlier the order delivery date, the higher the priority; the priority of the order can be defined with reference to the order delivery date and the cost, and the order priority order can be set to five levels (only as an example), for example, the red line>L1>L2, where the red line indicates that the order sorting must be at the front considering the comprehensive order delivery date and cost (such as production orders in emergency situations), and its priority is the highest. After the product dimension is sorted, for each model of product, the orders corresponding to each model of product are obtained, and for each model of product, the orders of the product model are sorted according to at least one of the order delivery date and the order priority, so as to obtain the order sorting result under the product model. In the above manner, on the basis of product sorting, the result of product sorting dimension sorting can be further refined from the order sorting dimension, so as to make the product sorting result more accurate.

Table 5 schematically shows the order sorting result of the product model X1 in Table 4. Please refer to Table 4 and Table 5 together. In Table 4, within the time range of months, all the first items of the product model X1 planned to be produced in 2021/8 are integrated into the second item as shown in Table 4, where the orders corresponding to the product model X1 include three (only for example), such as orders A1-001 to A1-003.

It is understandable that if the sorting is performed only from the product sorting dimension, it may not be possible to accurately sort the multiple orders corresponding to each product, which may result in the order that should be sorted first being actually sorted last. In order to obtain more accurate product scheduling results, the orders A1-001 to A1-003 corresponding to the product model X1 can be sorted to obtain the order sorting results (as shown in Table 5).

table 5

Next, the production data can be used to build a production data model for the product. Based on the production data model and order sorting results, the production resource situation of each process, the order sorting situation, the order delivery date, etc. can be obtained. Therefore, the production demand of each order can be allocated to the corresponding production resources and production time period according to the order sorting results, order delivery date, etc., so as to obtain the second scheduling result.

In the embodiments of the present disclosure, for example, the production requirements of each order can be allocated to the corresponding production resources and production time periods based on the production data model and the order sorting results through the forward scheduling method to obtain the second scheduling result. The forward scheduling method generally refers to the order of preference in the order sorting results, starting from the order in the front order, and deriving the schedule for the order in the back order until all orders are scheduled. In this process, orders can usually be arranged in a timely manner according to the capacity utilization of production resources to consume the remaining capacity, so as to improve the capacity utilization of production resources. It should be noted that in the embodiments of the present disclosure, the allocation of the production requirements of each order to the corresponding production resources and production time periods is not limited to the forward scheduling method. In other embodiments, other appropriate methods can be selected according to actual conditions. For example, the backward scheduling method can be used, and the present disclosure does not limit this.

For example, the order of each process of a product and the production resources involved in each process can be determined based on the production data model. As mentioned above, the production data model can include the association between semi-finished products and the production resources used to produce the semi-finished products, materials and processes, the association between finished products and the production resources used to produce the finished products, materials and processes, and the association between finished products and semi-finished products. Therefore, the order of each process of a product and the production resources involved in each process can be determined based on the constructed production data model.

Then, the production demand allocation can be performed for each order according to the order sequence in the order sorting result, so as to obtain the second scheduling result. The so-called allocation of production demand here includes allocating the production demand of each process extracted from the order to the corresponding production resources and production time periods, wherein the production time period corresponding to the production demand of the process ranked first is located before the production time period corresponding to the production demand of the process ranked later. In the process of allocating production demand for each order, if the production resources allocated to each process are sufficient, the production demand of each process extracted from each order can be flexibly allocated to the corresponding production resources and production time periods according to the order sorting results and the delivery date of the order. In this way, a more reasonable scheduling result can be obtained, and production efficiency and capacity utilization can be improved.

The embodiment of the present disclosure also provides a production scheduling system. The production scheduling system will be described below with reference to FIG5 .

FIG5 is a block diagram of a production scheduling system according to an embodiment of the present disclosure.

As shown in FIG. 5 , the production scheduling system includes a stored process engine 501 , a scheduling engine 502 , a temporary database 503 , a basic database 504 , and a modeling conversion database 505 .

The stored process engine 501 can obtain the scheduling data for scheduling the production of the first product, and in response to the lack of production data of the first product in the scheduling data, supplement the scheduling data with production data of a second product associated with the first product. In some embodiments, the stored process engine 501 can also use the supplemented scheduling data to create a data snapshot, and use the created data snapshot to generate a data model.

The scheduling engine 502 is connected to the stored process engine 501 through the gateway 508. The scheduling engine 502 can use the supplemented scheduling data to schedule the production of the first product, for example, using the above data model to perform production scheduling for the first product. The scheduling engine 502 can be a T3SupplyNet scheduling engine, which is a background program for production scheduling, which can be used for example but not limited to basic data verification, production plan simulation, plan report analysis, factory capacity utilization analysis, etc.

The temporary database 503 can be used to store scheduling data obtained from data sources such as legacy systems. The stored process engine 501 can supplement the scheduling data in the temporary database 503 in the manner described above. In some embodiments, at least part of the scheduling data can be obtained in real time. In some embodiments, at least part of the scheduling data can be obtained manually. The scheduling data in the temporary database 503 may not have data version information. The basic database 504 is communicated with the temporary database 503 for storing data snapshots. The modeling transformation database 505 is communicated with the basic database 504 for storing data models.

According to an embodiment of the present disclosure, the above-mentioned production scheduling system may further include a scheduling database 506. The scheduling database 506 is in communication with the modeling conversion database 505 and is used to store data used by the scheduling engine 502. In some embodiments, the scheduling database 506 may also be used to store data from the scheduling engine 502, such as various data generated by the scheduling engine 502 during processing.

According to an embodiment of the present disclosure, the above-mentioned production scheduling system may further include an optimizer engine 509 and an optimizer database 510. The optimizer database 510 is in communication with the modeling conversion database 505 and is used to store data used by the optimizer engine 509. In some embodiments, the optimizer database 510 may also be used to store data from the optimizer engine 509, such as various data generated by the optimizer engine 509 during processing.

According to an embodiment of the present disclosure, the above-mentioned production scheduling system may further include a manager 507, a user database 511 and a user interaction engine 512. The user interaction engine 512 is used to implement human-computer interaction, and in some embodiments may be implemented as a user interaction interface (UI, User Interface) or a terminal device with a user interaction interface display function, such as a client. For example, the user interaction engine 512 is used to receive configuration information input by a user. The user database 511 is communicated with the user interaction engine 512 and the basic database 504, and is used to store user information and user permissions. The manager 507 is used to perform the highest authority operation in the production scheduling system.

For example, when a user creates a scheduling task for a first product, the stored process engine 501 can obtain the scheduling data from the business system and store it in the temporary database 503. In this process, if the production data of the first product is missing in the scheduling data, the production data of the second product associated with the first product can be used to supplement the scheduling data. For example, if the bill of materials is missing, the bill of materials of the corresponding R&D product of the first product can be used to generate the bill of materials of the first product. If the production process information (also called route) is missing, the production process information of a product similar to the first product can be used to generate the production process information of the first product. The stored process engine 501 can use the supplemented scheduling data to establish a data snapshot and store it in the basic database 504, and use the established data snapshot to generate a data model and store it in the modeling conversion database 505. The data model in the modeling conversion database 505 can be converted into data suitable for the optimizer engine 509 and stored in the optimizer database 510, and can also be converted into data suitable for the scheduling engine 502 and stored in the scheduling database 506. The scheduling engine 502 can obtain the data model of the scheduling task for the first product from the scheduling database 506 to execute the scheduling task for the first product. In this way, the execution of the scheduling task for the first product is completed.

In some embodiments, during the execution of the scheduling task, the optimizer engine 509 and the scheduling engine 502 can work together. For example, the optimizer engine 509 can be used to perform preliminary scheduling, and then the scheduling engine 502 can be used to perform fine scheduling, so as to improve the accuracy of the scheduling results. Taking the above scheduling method as an example, the optimizer engine 509 can be used to perform preliminary scheduling on the product based on the linear programming solution model to obtain multiple first entries, each of which includes the production date, production quantity and corresponding production resources of the product, etc. The restriction conditions and objective functions for the bottleneck process in the linear programming solution model can be determined according to the configuration information. Then, the multiple first entries can be merged into the second entry to achieve product sorting, and the orders of the product can be sorted according to at least one of the order delivery date and the order priority. Thereafter, the scheduling engine 502 can be used to perform fine scheduling on the order sorting results, for example, the production requirements of each process can be extracted from each order, and the production requirements of each order can be allocated to the corresponding production resources and production time periods based on the data model and the order sorting results, so as to determine the production requirements corresponding to each production resource in each production time period. Through the cooperation of the optimizer engine 509 and the scheduling engine 502, it is possible to implement rough scheduling first and then fine scheduling, which improves the scheduling system's tolerance for the accuracy of scheduling data. For example, in the early stage of scheduling, the production data of the first product to be scheduled can be supplemented with the production data of the R&D product or similar products as described above, which to some extent makes the supplemented scheduling data different from the data required in actual production; and in the later scheduling process, the two-level scheduling method of rough scheduling and fine scheduling compensates for the impact of the difference in scheduling data on the scheduling results, so that the scheduling system can still achieve scheduling with the expected accuracy even when the scheduling data is incomplete, which greatly improves the scheduling efficiency.

It should be noted that the implementation methods, technical problems solved, functions realized, and technical effects achieved of each module/unit/sub-unit in the device part embodiment are the same or similar to the implementation methods, technical problems solved, functions realized, and technical effects achieved of each corresponding step in the method part embodiment, and will not be repeated here.

In the technical solution of this disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals. In addition, in the technical solution of this disclosure, the user's authorization or consent is obtained before obtaining or collecting user personal information.

According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

FIG6 schematically shows a block diagram of an electronic device suitable for implementing a production scheduling method according to an embodiment of the present disclosure.

As shown in Figure 6, the electronic device 600 according to the embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 to the random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and/or a related chipset and/or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

In RAM 603, various programs and data required for the operation of the electronic device 600 are stored. The processor 601, ROM 602 and RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 602 and/or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. The processor 601 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

According to an embodiment of the present disclosure, the electronic device 600 may further include an input/output (I/O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 606 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I/O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

The present disclosure also provides a computer-readable storage medium, which may be included in the device/apparatus/system described in the above embodiments; or may exist independently without being assembled into the device/apparatus/system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than ROM 602 and RAM 603.

The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the production scheduling method provided by the embodiment of the present disclosure.

The above functions defined in the system/device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 601. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and/or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and/or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

It will be appreciated by those skilled in the art that the features described in the various embodiments and/or claims of the present disclosure may be combined and/or combined in a variety of ways, even if such combinations and/or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and/or claims of the present disclosure may be combined and/or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. However, these embodiments are only for the purpose of illustration and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims (21)

A production scheduling method, comprising: Acquiring scheduling data for production scheduling of a first product; In response to the production data of the first product being missing from the scheduling data, supplementing the scheduling data with production data of a second product associated with the first product; and The production of the first product is scheduled using the supplemented scheduling data. The method of claim 1, wherein the supplementing the scheduling data with production data of a second product associated with the first product comprises: determining a second product associated with the first product; generating production data of the first product based on the production data of the second product; The generated production data of the first product is added to the scheduling data. The method of claim 2, wherein determining a second product associated with the first product comprises: A research and development product for the first product is determined as a second product associated with the first product. The method according to claim 3, wherein the production data includes a bill of materials, and generating the production data of the first product based on the production data of the second product includes: Changing the product identification in the bill of materials of the second product to the product identification of the first product to obtain a changed bill of materials; The modified bill of materials is stored as the bill of materials for the first product. The method of claim 2, wherein determining a second product associated with the first product comprises: A similar product to the first product is determined from a plurality of candidate products as a second product associated with the first product, wherein a product identifier of the similar product at least partially matches a product identifier of the first product. The method according to claim 5, wherein the production data includes production process information, and generating the production data of the first product based on the production data of the second product includes: The production process information of the similar product is stored as the production process information of the first product. The method according to claim 6, wherein determining a similar product of the first product among a plurality of candidate products comprises: comparing the first field of the product identification of each candidate product with the first field of the product identification of the first product; If the first field of the product identification of the candidate product matches the first field of the product identification of the first product and the candidate product has production process information, the candidate product is determined to be a similar product of the first product. The method according to claim 7, wherein determining similar products of the first product from a plurality of candidate products further comprises: If the first field of the product identification of each candidate product does not match the first field of the product identification of the first product or the matching candidate product does not have production process information, then compare the second field of the product identification of each candidate product with the second field of the product identification of the first product, wherein the second field partially overlaps with the first field; If the second field of the product identification of the candidate product matches the second field of the product identification of the first product and the candidate product has production process information, the candidate product is determined to be a similar product of the first product. The method according to claim 8, further comprising: receiving user configuration information, and determining the first field and the second field for comparison according to the user configuration information. The method according to claim 8, wherein the product identification is an N-bit string, each bit in the N-bit string has a corresponding priority, wherein the priority of at least one bit not covered by the first field is lower than the priority of at least one bit not covered by the second field, and N is an integer greater than 1. The method according to any one of claims 1 to 10, further comprising: determining the inventory level of the first product; The demand quantity of the first product in the scheduling data is offset using the inventory quantity of the first product. The method according to any one of claims 1 to 11, wherein using the supplemented scheduling data to schedule production of the first product comprises: Use supplemented scheduling data to create data snapshots; generating a data model using the established data snapshot; and Production scheduling for the first product is performed using the data model. The method according to claim 12, wherein the data model includes the association between semi-finished products and production resources, materials and processes used to produce the semi-finished products, the association between finished products and production resources, materials and processes used to produce the finished products, and the association between finished products and semi-finished products; and the using the data model to perform production scheduling for the first product comprises: Performing production scheduling for the first product based on the linear programming solution model to obtain a first scheduling result for the first product, wherein the first scheduling result includes a plurality of first items, each of which includes a production date, a production quantity, and corresponding production resources of the product; the linear programming solution model includes restriction conditions for the bottleneck process and an objective function; Merge multiple first entries corresponding to the same product whose production date falls within the same time range into a second entry to obtain multiple second entries, each of which includes the production quantity of the same product within a time range; For each product in the second entry, obtain orders corresponding to the product and sort the orders of the product according to at least one of order delivery date and order priority to obtain an order sorting result; Extracting the production requirements of each process from each order, where the production requirements of each process include the quantity of finished products or semi-finished products planned to be produced by the process; and Based on the data model and the order sorting result, the production demand of each order is allocated to the corresponding production resources and production time periods to obtain a second scheduling result, which includes the production demand corresponding to each production resource in each production time period. A production scheduling system, comprising: a stored procedure engine, configured to obtain scheduling data for scheduling production of a first product, and in response to the fact that production data of the first product is missing from the scheduling data, supplement the scheduling data with production data of a second product associated with the first product; A temporary database for storing the scheduling data; The scheduling engine is used to schedule production of the first product using the supplemented scheduling data. The production scheduling system according to claim 14, wherein the stored process engine is further used to create a data snapshot using the supplemented scheduling data, and to generate a data model using the created data snapshot; the scheduling engine is used to execute production scheduling for the first product using the data model; The production scheduling system also includes: A basic database, which is in communication with the temporary database and is used to store the data snapshot; and The modeling conversion database is connected to the basic database for storing the data model. The production scheduling system according to claim 14 or 15 further includes: a scheduling database, which is communicatively connected to the modeling conversion database and is used to store data for use by the scheduling engine. The production scheduling system according to any one of claims 14 to 16 further includes an optimizer engine and an optimizer database, wherein the optimizer database is communicatively connected to the modeling conversion database and is used to store data for use by the optimizer engine. The production scheduling system according to any one of claims 14 to 17, further comprising: A user interaction engine, used to receive configuration information input by a user; A user database, which is in communication with the user interaction engine and the basic database and is used to store user information and user permissions; and A manager is used to perform operations with the highest authority in the production scheduling system. An electronic device comprises a memory and a processor, wherein the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor executes the method as claimed in any one of claims 1 to 13. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 13. A computer program product comprises a computer program, which implements the method according to any one of claims 1 to 13 when executed by a processor.

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