WO2023159344A1 - 产品的生产排程方法、电子设备和存储介质 - Google Patents

产品的生产排程方法、电子设备和存储介质 Download PDF

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WO2023159344A1
WO2023159344A1 PCT/CN2022/077231 CN2022077231W WO2023159344A1 WO 2023159344 A1 WO2023159344 A1 WO 2023159344A1 CN 2022077231 W CN2022077231 W CN 2022077231W WO 2023159344 A1 WO2023159344 A1 WO 2023159344A1
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Prior art keywords
production
product
order
demand
scheduling
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PCT/CN2022/077231
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English (en)
French (fr)
Inventor
苏谢明
林雪梅
李洋
刘希迅
刘楠
王川
鲁斋
刘贵豪
张华�
王洪
吴建民
Original Assignee
京东方科技集团股份有限公司
北京中祥英科技有限公司
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Priority to CN202280000239.1A priority Critical patent/CN116940952A/zh
Priority to US18/246,891 priority patent/US20240028983A1/en
Priority to PCT/CN2022/077231 priority patent/WO2023159344A1/zh
Publication of WO2023159344A1 publication Critical patent/WO2023159344A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the disclosure relates to the technical field of production scheduling, in particular to a production scheduling method for a product, electronic equipment, storage media and computer program products.
  • the disclosure provides a product production scheduling method, electronic equipment, storage media and computer program products.
  • a production scheduling method for a product including:
  • the linear programming solution model includes constraints and objective functions for the bottleneck process, and the first scheduling result includes a plurality of first items , each first entry includes the production date, production quantity and corresponding production resources of the product;
  • the order corresponding to the product is obtained and the order of the product is sorted according to at least one of order delivery date and order priority to obtain an order sorting result.
  • the method further includes:
  • the production data model includes the association between semi-finished products and production resources used to produce semi-finished products, materials and processes, the association between finished products and production resources used to produce finished products, materials and processes, and finished products and semi-finished products the relationship between
  • Extract the production demand of each process from each order, and the production demand of each process includes the quantity of finished or semi-finished products planned to be produced through this process;
  • the production demand of each order is assigned to the corresponding production resources and production periods, and the second scheduling result is obtained.
  • the second scheduling result includes the corresponding production resources of each production resource in each production period production needs.
  • allocating the production demand of each order to the corresponding production resource and production period includes:
  • the allocation of production demand is carried out for each order.
  • the allocation of production demand includes allocating the production demand of each process extracted from the order to the corresponding production resource and production period.
  • the production period corresponding to the production demand is located after the production period corresponding to the production demand of the sequenced process.
  • the allocating the production requirements of each process extracted from the order to the corresponding production resources and production time slots includes: through the forward scheduling method or the backward scheduling method, allocating the The production demand of each process extracted from the order is allocated to the corresponding production resource and production period.
  • the restriction conditions include at least one of the following: a first restriction condition for equipment capacity, a second restriction condition for production line priority, a third restriction condition for factory running time, and a first restriction condition for materials.
  • a first restriction condition for equipment capacity for equipment capacity
  • a second restriction condition for production line priority for production line priority
  • a third restriction condition for factory running time for factory running time
  • a first restriction condition for materials for materials.
  • the first constraint condition indicates that the sum of planned production volumes of each device for the day*tact time ⁇ equipment availability time*equipment utilization rate;
  • the second constraint condition indicates that the priority of the internal factory is first Priority, the priority of the external foundry is the second priority, and the first priority is lower than the second priority;
  • the third restriction indicates that the factory's transit time is within the preset range;
  • the fourth restriction indicates that it is used for manufacturing display The quantity of semi-finished products and materials of the module is within a preset range;
  • the fifth constraint condition indicates that the quantity of the model of the display module produced by each device per day is less than the preset value.
  • the objective function includes at least one of the following: a first objective function for maximizing demand satisfaction for a product, and a second objective function for minimizing the quantity of delayed delivery products , the third objective function for maximizing the capacity utilization of equipment for producing products, the fourth objective function for minimizing the running time of the factory, and the fifth objective function for maximizing the continuous production time of products on the same production line objective function.
  • the fourth objective function is Min (inter-factory transit time), where The running time of the factory is the accumulated inter-factory transportation time in multiple orders;
  • the fifth objective function is Max (the continuous production time of each type of display module on the same production line); where Max() represents the maximum calculation, and Min ( ) indicates the minimization calculation.
  • the method further includes: after obtaining the order sorting result, removing the inventory quantity from each order in the order sorting result.
  • the production date is in units of days or weeks, and the time range is in units of months or quarters.
  • the product is a display module
  • the semi-finished product includes an array substrate and a display unit including the array substrate
  • the finished product is a display module including the display unit
  • determining at least one production process as a bottleneck process from a plurality of production processes of a product includes:
  • At least one process is selected from the multiple processes involved in the back-stage core process of the product as the bottleneck process of the product.
  • an electronic device including:
  • a memory and a processor wherein instructions executable by the processor are stored in the memory, and the instructions, when executed by the processor, cause the processor to implement the method as described above.
  • a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method described above.
  • a computer program product comprising a computer program which, when executed by a processor, implements the method as described above.
  • FIG. 1 is a flow chart of a production scheduling method for a product according to an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of a method for acquiring basic data according to an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of a method for determining a bottleneck process according to an embodiment of the present disclosure
  • FIG. 4 is a flowchart of a production scheduling method for a product according to another embodiment of the present disclosure.
  • Fig. 5 is a flowchart of a method for obtaining a second scheduling result based on a production data model and an order sorting result according to an embodiment of the present disclosure
  • FIG. 6 is a schematic diagram of obtaining a second scheduling result based on a production data model and order sorting results according to an embodiment of the present disclosure
  • FIG. 7 is a block diagram of an electronic device used to implement the production scheduling method of a product according to an embodiment of the present disclosure.
  • FIG. 1 is a flow chart of a production scheduling method for a product according to an embodiment of the disclosure.
  • the product production scheduling method 100 includes operations S110 - S150 .
  • the methods of the embodiments of the present disclosure may be computer-implemented methods.
  • a computer can be used to implement the production scheduling method of the embodiment of the present disclosure based on an Advanced Planning and Scheduling System (Advanced Planning and Scheduling, APS).
  • the APS system is a system that comprehensively considers resource constraints such as materials, equipment, personnel, production capacity, customer needs, and transportation, and uses optimization algorithms to automatically generate factory production plans and production schedules.
  • the so-called basic data here may include sales demand data and production information data.
  • the sales demand data may include, for example, product-related sales demand information (such as order delivery date, sales and inventory plan, etc.).
  • Production information data may include, for example, material demand and supply, production process information, production cycle, semi-finished product inventory information, process yield rate, and the like. In the embodiment of the present disclosure, production scheduling can be performed on products according to the acquired basic data.
  • the APS system 201 can be used to obtain sales demand information (such as order delivery date, sales and inventory plan, etc.)
  • the data related to product or semi-finished product inventory information is obtained from Planning (ERP) system 203
  • the data related to material demand and supply planning is obtained from Material Requirement Planning (Material Requirement Planning, MRP) system 204
  • the data related to material demand and supply planning is obtained from PLM (Product Lifecycle Management, product life cycle management)/MDS (Master Data Management, Master Data Management) integrated system 205 to obtain data related to the production process, and from the Manufacturing Execution System (Manufacturing Execution System, MES) 206 to obtain production performance or work status ( For example, data related to process yield rate), or data related to revenue, profit or cost plan, etc.
  • ERP Planning
  • MRP Material Requirement Planning
  • MRP Material Requirement Planning
  • PLM Product Lifecycle Management, product life cycle management
  • MDS Master Data Management, Master Data Management
  • MES Manufacturing Execution System
  • MPS system 204 can obtain corresponding information from APS system 201, for example. production plan, obtain Bill of Material (Bill of Material, BOM) information from the PLM/MDM integrated system 205, obtain data related to material inventory from the ERP system 203, and provide material procurement information (such as purchase requisitions) to the ERP system 203 single PR).
  • BOM Bill of Material
  • the APS system 201 can regularly (or irregularly) acquire basic data for production scheduling of products. It should be noted that what is shown in FIG. 2 is only an example of acquiring basic data, and is not intended to limit the scope of the present disclosure.
  • the products described above may be, for example, any one or more finished products processed through multiple production processes, which may be determined according to actual conditions.
  • the product mentioned may be, for example, a display panel or a display module in a display panel, and the following description mainly takes the display module as an example.
  • the display module is mainly obtained through processing of the array substrate (Array) process, the display unit (Cell) process and the display module (Module) process.
  • Array array substrate
  • Cell display unit
  • Mode display module
  • the basic data for production scheduling of the display module may be acquired according to the method described above, and the production scheduling of the display module may be subsequently performed according to the acquired basic data, which will not be repeated here.
  • At least one production process is determined as a bottleneck process from a plurality of production processes of a product according to basic data.
  • Bottleneck Process usually refers to one or more production processes or processes that restrict the output of the entire production line.
  • the bottleneck process is mainly defined for the production process, and the resources of the bottleneck process determine the output volume and inventory level.
  • the link with the slowest production tempo (Take Time, TT) in a process is called "bottleneck". Since the production tempo of multiple production processes of the product may be different, it will affect the production tempo of each production process.
  • at least one process can be determined from multiple processes of the product as the bottleneck process, so that the bottleneck process can be predicted in advance, and the bottleneck process can be improved in the future to avoid the accumulation of materials and work in progress, thereby improving the production capacity of the process utilization rate.
  • the bottleneck process can be determined according to the production capacity of each process, for example, the process with the smallest production capacity is selected as the bottleneck process, and the production capacity of each process can be obtained according to the basic data obtained in operation S110.
  • the production process of the display module mainly includes the process section of manufacturing the array substrate (Array), the process section of manufacturing the display unit (Cell), and the process of manufacturing the display module (Module). part.
  • the production plan for the display module (Module) process segment usually needs to consider the supply of the display unit process segment, and the production plan for the display unit process segment usually needs to consider the supply of the array substrate process segment.
  • the sales demand information related to the display module (such as the sales demand of various types of products), the yield rate of each process, and the process yield of each process can be extracted from the basic data.
  • the production demand of the factory for producing array substrates, the production demand of the factory for producing display units, and the production demand of the factory for producing display modules can be determined according to the product sales volume and the process yield rate of each process.
  • the bottleneck process of the array substrate based on the production demand of the factory for producing the array substrate, determine the bottleneck process of the array substrate according to the production capacity of each process of producing the array substrate; Determine the bottleneck process of the display unit based on the production capacity of the display unit; determine the bottleneck process of the display module according to the production capacity of each process of producing the display module based on the production demand of the factory used to produce the display module.
  • FIG. 3 is a schematic diagram of a method for determining a bottleneck process according to an embodiment of the present disclosure. An exemplary implementation of the method for determining a bottleneck process will be described below with reference to FIG. 3 . It should be noted that the process of determining the bottleneck process of each production process is the same or similar, and the determination of the bottleneck process of the display module will be used as an example for introduction. In addition, since each process may involve multiple product models, materials, manufacturing processes, etc., in order to save space, unless otherwise specified, the following process-related data (such as production capacity, production demand) can refer to, for example, including There are various product models, which will not be described in detail below.
  • the monthly sales demand of various types of display modules, the production cycle of each process, the production line or equipment resources, inventory data, and process quality products of each process can be extracted from the basic data rate etc.
  • the monthly production demand 330 of each type of display module can be calculated according to the monthly sales demand 300 of various types of display modules and the process yield rate of the display module process.
  • the process section of manufacturing an array substrate includes, for example, process A 1 , process A 2 , ..., process A N
  • the process segment of manufacturing a display unit includes, for example, process B 1 , process B 2 , ..., process BN
  • the process section of manufacturing a display module includes, for example, a process C 1 , a process C 2 , ..., a process C N , and each process corresponds to its own production cycle.
  • the production capacity of each process 331 (including various models) is calculated.
  • the number of processing layers of the finished product or semi-finished product corresponding to the process can also be considered.
  • the array substrate can include multiple layers.
  • the number of layers of the array substrate is considered when calculating the throughput of the array substrate process.
  • the number of processing layers is 1.
  • Production capacity 331 of each process equipment available time x number of equipment x overall equipment efficiency (OEE)/( ⁇ (monthly production demand of various types of products x TT)/total monthly production demand).
  • OEE equipment available time
  • n1 the number of available equipment
  • t1 the usable time of each equipment
  • the overall efficiency of each equipment is ⁇ 1
  • the process can be calculated based on the above data C 1 capacity.
  • the capacity of other processes can be obtained by adopting the above method.
  • the bottleneck process 332 is determined by comparing the production capacity of each process, for example, the process with the smallest production capacity can be selected as the bottleneck process.
  • the production capacity corresponding to each process is m 1 ⁇ m 2 ⁇ ... ⁇ m N
  • the process C 1 is determined to be the bottleneck process 332 of the display module.
  • the bottleneck process of the array substrate and the bottleneck process of the display unit can also be calculated, which will not be repeated here.
  • the number of processing times (such as the number of layers (layer)) can also be considered.
  • the process C 1 production capacity (equipment available time x number of equipment x overall equipment efficiency (OEE))/weighted processing times/( ⁇ (monthly production demand of various types of products x TT)/total monthly production demand).
  • OEE overall equipment efficiency
  • the production capacity of other processes can be determined by using the above method, so as to determine the bottleneck process of the display module process.
  • At least one production process can be determined from the multiple production processes of the display module as the bottleneck process, and these bottleneck processes will be used as the object of production scheduling for production scheduling of the display module.
  • Procedure. For example, at least one bottleneck process 312 of the array substrate, at least one bottleneck process 322 of the display unit, and at least one bottleneck process 332 of the display module are respectively determined from the array substrate process, the display unit process, and the display module process of the display module.
  • determining at least one production process as a bottleneck process from multiple production processes of the product includes: determining the bottleneck process of the product based on the production demand of the factory used to produce the product, and according to the production capacity of the back-end core process of the product .
  • the core process can be determined from the above-identified multiple bottleneck processes.
  • the bottleneck process is used to schedule the subsequent production of the product.
  • a more targeted production plan can be obtained, thereby improving the capacity utilization rate of the entire process.
  • a product can be manufactured sequentially through a multi-stage process (also referred to as a process stage), each process stage including multiple processes. For products that involve multiple stages in the production process and span a long span, controlling the core process at the back stage can ensure the final order is fulfilled.
  • the manufacturing process of display panels needs to go through multiple process stages of Array, Cell, and Module, and the production process spans a long time. Due to the production characteristics of the panel, the production process of the display panel is mainly limited by the back-end core process.
  • the bottleneck process can be determined from the multiple processes involved in the display module process section, and then the production schedule can be arranged according to the bottleneck process in the display module process section. For example, the bottleneck process can be determined from the multiple processes involved in the display module process segment according to the above method, and details will not be repeated here.
  • the objective function is solved by linear programming according to the constraints of the bottleneck process, and the first scheduling result of the product is obtained.
  • the first scheduling result includes a plurality of first items, and each first item The items include the production date, production quantity and corresponding production resources of the product.
  • the objective function can be solved by linear programming according to the constraints of the bottleneck process, so as to obtain the first scheduling result of the product.
  • the linear programming solution is performed on the objective function according to the constraints of the bottleneck process, for example, an optimizer (optimizer) can be used to implement.
  • an optimizer optically
  • the data can be preprocessed to convert the data into a preset format (such as TXT format) for the subsequent linear programming solution.
  • Table 1 schematically shows some basic An example of data organized into a preset format.
  • the basic data corresponding to each bottleneck process is preprocessed to convert it into a preset format.
  • the optimizer reads the converted data, it performs linear programming on the objective function in combination with the constraints of each bottleneck process. Solve to obtain the first scheduling result of the product.
  • the first scheduling result includes a plurality of first entries, and each first entry includes the production date, production quantity and corresponding production resources of the product.
  • the bottleneck process involves related equipment, production lines, factories, materials, etc.
  • the constraints of the bottleneck process may include at least one of the following: the first constraint condition for equipment capacity, the second constraint condition for production line priority Constraints, a third constraint on plant operating time, a fourth constraint on materials, and a fifth constraint on the number of line changes.
  • the first constraint condition indicates the sum of the planned production volume of each device for the day * takt time ⁇ equipment availability time * equipment utilization rate;
  • the second constraint condition indicates that the priority of the internal factory is the first priority, and that of the external foundry The priority is the second priority, and the first priority is lower than the second priority;
  • the third restriction indicates that the factory transit time is within the preset range;
  • the fourth restriction indicates the semi-finished products and materials used to manufacture the display module The quantity is within the preset range;
  • the fifth constraint condition indicates that the quantity of the model of the display module produced by each device every day is less than the preset value.
  • the objective function described above may include at least one of the following, for example: a first objective function for maximizing demand satisfaction for display modules, and a second objective for minimizing the number of delayed delivery display modules function, a third objective function for maximizing equipment capacity utilization for producing display modules, a fourth objective function for minimizing factory run time, and time for continuous production of display modules on the same production line Maximize the fifth objective function.
  • the fourth objective function is: Min (inter-factory transit time), where the factory The running time is the accumulated inter-factory transportation time in multiple orders;
  • the fifth objective function is: Max (the continuous production time of each type of display module on the same production line), where Max() represents the maximum calculation; where Max() means to maximize the calculation, and Min() means to minimize the calculation.
  • weights can be set separately for each objective function as required. For example, the weights of
  • linear programming is performed to solve the objective function according to the basic data corresponding to each bottleneck process and the constraints of each bottleneck process, and the first scheduling result of the display module is obtained.
  • This process can be understood as, according to the product
  • an optimizer such as Xpress-Optimizer
  • Xpress-Optimizer is a solver engine in the Xpress-MP toolkit.
  • XPress-MP is a mathematical modeling and optimization toolkit for solving linear, integer, quadratic, and stochastic programming problems.
  • the XPress-MP toolkit can be used on computer platforms, and has different performance versions to solve problems of various scales.
  • the Xpress-Optimizer in the XPress-MP toolkit contains algorithms that enable the solution of linear programming problems, mixed integer programming problems, quadratic programming problems, and mixed integer quadratic programming problems.
  • the 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, so as to obtain the first scheduling result that meets the product production scheduling constraint conditions and the objective function. Based on the above method, it is possible to alleviate or even avoid the mismatch of the production rhythm of the preceding and following processes and the drift of the bottleneck of the process, thereby improving the capacity utilization rate of the production line capacity.
  • Table 2 schematically shows some first scheduling results for products.
  • the first scheduling result includes multiple first entries, and each first entry includes the production date, production quantity and corresponding production resources of the product.
  • the model is The production date of product A1 is 2021-9-14, the production quantity is 100, and the production resource is production line 4.
  • the production date is calculated in days, but the embodiments of the present disclosure are not limited thereto, and the production date may be calculated in other calculation units, such as weeks.
  • the first scheduling result of the product based on operation S130 is the production scheduling in an ideal state given comprehensive consideration of factors such as equipment capacity, production line priority, factory running time, materials, and production line change times.
  • the difference between the first scheduling result of the product obtained by the linear programming solution method and the actual scheduling process will be considered, for example, the continuity of production and the production sequence cannot be expressed, and the first scheduling of the product Perform product sorting and order sorting based on the process results to obtain the order sorting result, which will be described in detail below with reference to operation S140 and operation S150.
  • a plurality of first entries corresponding to the same product whose production dates fall within the same time range are merged into second entries to obtain a plurality of second entries, each of which includes the same product at a time range of production quantities.
  • the production sequence of various types 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 the second entry , to obtain multiple second entries, each second entry includes the production quantity of the same product in a time range.
  • the above process can be understood as integrating the same products in different orders whose production dates fall within the same time range, so as to obtain a sorting result with 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.
  • the above-mentioned production date may be in units of days or weeks, for example, and the time range may be in units of months or quarters, which may be set according to actual production scheduling conditions, and are not limited here.
  • the integrated results can be sorted based on the product sorting dimension to obtain the sorting results of various products (or product models).
  • the product sorting dimension may consider at least one of the following factors: the demand of the product within a time range, the number of available production lines corresponding to the product, and the production cycle of the product.
  • the allocation of production capacity resources can be determined according to the adjusted product production sequence, so as to realize the hierarchical utilization of production capacity and further improve the utilization rate of production capacity
  • Table 3 schematically shows the scheduling results of the above adjusted products. For example, within the time frame in units of months, after adjusting the results of the first schedule in Table 2, all the first entries with product model A1 whose production date falls in 2021/8 are integrated into a second entry, the second entry indicates that the planned production quantity of the model A1 product in 2021/8 is 44523. Similarly, consolidate the first entry for all product models A4 planned for production in 2021/8 into another second entry. By analogy, multiple second entries as shown in Table 3 are obtained.
  • the integrated results (that is, the obtained multiple second items) can also be sorted to obtain the corresponding product production ranking (see the column of product ranking in Table 3).
  • the second entries can be sorted according to the demand of the product within the time range, so as to determine the The ordering is: A1>A4>A2>A5>A6. In this way, a more realistic sorting result can be obtained.
  • the combination of different orders can be realized based on the above adjustment method, and the allocation of production capacity resources can be determined according to the adjusted product production sequence under the condition of limited production capacity, so as to realize the hierarchical utilization of production capacity and further improve the utilization rate of production capacity.
  • discontinuous production can also be avoided.
  • the second item on the basis of the above sorting results, can be further sorted according to the order delivery date or order priority, and the above sorting results can be adjusted more finely, so as to obtain more accurate product scheduling result.
  • the order sorting dimension may include, for example, the order delivery date or order priority of the orders to be sorted.
  • the priority of the order can be defined by referring to the delivery date and cost of the order.
  • the priority order of the order can be set to five levels (just an example), for example, the red line > A >B>C>D, where the red line indicates the situation where the comprehensive order delivery date and cost considerations must be ranked first (such as production orders in emergency situations), and its priority is the highest.
  • the product sorting result can be further refined from the order sorting dimension, so that the product sorting result is more accurate.
  • Table 4 schematically shows the order sorting results of the product model A1 in Table 3. Please refer to Table 3 and Table 4 together.
  • Table 3 within the time frame in months, the first entry of all product models A1 planned to be produced in 2021/8 is integrated as shown in Table 3
  • the orders A1-001 to A1-003 corresponding to the product model A1 can be sorted according to the method in operation S150 to obtain the order sorting result (as shown in Table 4).
  • FIG. 4 is a flowchart of a production scheduling method for a product according to another embodiment of the present disclosure.
  • the product production scheduling method 400 includes operations S410 - S480 .
  • operation S410 to operation S450 are respectively implemented in the same manner as operation S110 to operation S150, and the repeated parts will not be described in detail.
  • At least one production process is determined from a plurality of production processes of the product as a bottleneck process according to the basic data.
  • the linear programming solution model is used to perform production scheduling on the product, and the first scheduling result of the product is obtained.
  • the linear programming solution model includes constraints and objective functions for the bottleneck process, and the first scheduling result includes multiple first scheduling results. entries, each first entry includes the production date, production quantity and corresponding production resources of the product.
  • a plurality of first entries corresponding to the same product whose production dates fall within the same time range are merged into second entries to obtain a plurality of second entries, each of which includes the same product at a time range of production quantities.
  • the order corresponding to the product is acquired and the order of the product is sorted according to at least one of order delivery date and order priority to obtain an order sorting result.
  • the inventory quantity of the product can also be removed from each order in the obtained order sorting result, so that each order includes the net production demand.
  • the production path, raw materials, semi-finished products/finished products, production capacity, and inventory information for each product model can be obtained based on the basic data obtained above.
  • the SupplyNet engine can be used to build a production data model.
  • the SupplyNet engine is a background program for production scheduling, which can be used for example but not limited to basic data inspection production plan simulation, plan report analysis, factory capacity utilization analysis, etc.
  • the so-called production data model here may include, for example, the association between semi-finished products and the production resources used to produce semi-finished products, materials and processes, the relationship between finished products and production resources used to produce finished products, materials and processes, and the relationship between finished products and semi-finished products. association.
  • the production data model includes semi-finished array substrates and The relationship between the display unit including the array substrate and the production resources, materials and processes for producing the above-mentioned semi-finished products, the relationship between the finished product (including the display module of the display unit) and the production resources, materials and processes used to produce the finished product, and the finished product (the display module including the display unit) and the semi-finished product (the array substrate and the display unit including the array substrate).
  • the production demand of each process is extracted from each order, and the production demand of each process includes the quantity of finished or semi-finished products planned to be produced through the process.
  • the production requirements of each process include through The quantity of finished or semi-finished products that the operation plans to produce.
  • converting the production demand of the finished product into the production demand of each process is the same as or similar to the process of confirming the production demand of each process described above, and will not be repeated here.
  • the production resources of each process, the sorting of orders, and the delivery date of orders can be obtained.
  • the production requirements of the order are allocated to the corresponding production resources and production time slots, so as to obtain the second scheduling result.
  • the production demand of each order can be assigned to the corresponding production resource and production period based on the production data model and the order sorting result through the forward scheduling (Forward Scheduling) method to obtain the second scheduling result.
  • forward scheduling Forward Scheduling
  • the forward scheduling method usually means that according to the preferred order in the order sorting results, starting from the previous order, the schedule is derived from the subsequent order until all orders are exhausted. During this process, it is usually possible to arrange orders 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.
  • the allocation of the production requirements of each order to the corresponding production resources and production periods is not limited to the forward scheduling method. In other embodiments, other appropriate methods can be selected according to the actual situation. For example, a backward scheduling (Backward Scheduling) method may be used, which is not limited in the present disclosure.
  • a backward scheduling (Backward Scheduling) method may be used, which is not limited in the present disclosure.
  • a relatively more reasonable second scheduling result is provided based on the production data model and order sorting results, which improves capacity utilization and production efficiency.
  • Fig. 5 is a flowchart of a method for obtaining a second scheduling result based on a production data model and an order sorting result according to an embodiment of the present disclosure. An example implementation of the above operation S480 will be described below with reference to FIG. 5 .
  • the method for obtaining the second scheduling result based on the production data model and the order sorting result includes operations S581-S582.
  • the production data model can include the association between semi-finished products and production resources used to produce semi-finished products, materials and processes, the association between finished products and production resources used to produce finished products, materials and processes, and the relationship between finished products and semi-finished products association. Therefore, according to the constructed production data model, the sequence of each process of the product and the production resources involved in each process can be determined.
  • the allocation of production demand is performed on each order, and the allocation of production demand includes allocating the production demand of each process extracted from the order to the corresponding production resource and production period, wherein the order is in The production period corresponding to the production demand of the preceding process is located before the production period corresponding to the production demand of the sequenced process.
  • the production demand of each process extracted from each order can be flexibly allocated according to the order sorting result and order delivery date Give the corresponding production resource and production period. As a result, more reasonable scheduling results can be obtained, and production efficiency and capacity utilization can be improved.
  • FIG. 6 is a schematic diagram of obtaining a second scheduling result based on a production data model and an order sorting result according to an embodiment of the present disclosure.
  • the solution of the present disclosure will be described below with reference to FIG. 6 and taking determining the second scheduling result of the display module as an example.
  • Figure 6 schematically shows the constructed production data model 601 of the display module, multiple orders (for example 602 and 603) in the order sorting result, and multiple orders (such as 602 and 603) in the order sorting result based on the production data model 601 ( For example, 602 and 603 ) are allocated to each production resource used to produce the display module to obtain the second scheduling result 604 .
  • FIG. 6 is only an example, intended to help those skilled in the art understand the solution of the present disclosure, and is not intended to limit the protection scope of the present disclosure.
  • the production data model 601 it is possible to determine the relationship between semi-finished products and equipment, materials and processes used to generate semi-finished products, the relationship between finished products and equipment, materials and processes used to generate finished products, and the relationship between finished products and semi-finished products connection between. For example, according to the production data model 601, it can be determined that the raw material R passes through the process 1 to obtain the semi-finished product Z1, the semi-finished product Z1 passes through the process 2 to obtain the semi-finished product Z2, and the semi-finished product Z2 passes through the process 3 to obtain the finished product P.
  • the production resources available for process 1 include production line SB1
  • the production resources available for process 2 include production lines SB2 and SB3
  • the production resources available for process 3 include production lines SB4 and SB5.
  • the above information determined according to the production data model 601 can be subsequently used for the second scheduling to maximize the utilization of production line capacity, thereby further improving the capacity utilization rate of the production line.
  • the order of the two orders may be determined by the above order sorting result, for example, the first order PO1 is ranked before the second order PO2.
  • Production requirements for each operation can be extracted from each order.
  • the production information contained in the first order PO1 includes, for example, a product model (eg, product model A1), a planned production quantity (eg, 60 pieces), and a planned delivery time of the product (eg, D2).
  • a product model eg, product model A1
  • a planned production quantity eg, 60 pieces
  • a planned delivery time of the product eg, D2
  • the production demand of process 1, process 2 and process 3 of product model A1 can be determined as ACT1, ACT2 and ACT3 respectively, where production Demand ACT1 indicates that 60 semi-finished products Z1 need to be produced, production demand ACT2 indicates that 60 semi-finished products Z2 need to be produced, and production demand ACT3 indicates that 60 finished products P need to be produced.
  • the production demands of process 1, process 2 and process 3 of product model A2 are ACT4, ACT5 and ACT6, the production demand ACT4 indicates the need to produce 80 semi-finished products Z1, the production demand ACT5 indicates the need to produce 80 Z2, and the production demand ACT6 indicates the need to produce 80 finished products P.
  • D1 to D4 in the second scheduling result 604 represent consecutive dates, for example, respectively representing January 01, January 02, January 03 and January 04 in 2021.
  • the embodiments of the present disclosure are not limited thereto, and the dates can be expressed in various other ways as needed, for example, D1 to D4 can also represent the first week, the second week, the third week and the fourth week of January 2021, etc.
  • resource allocation is performed on the production requirements of each process extracted from the first order PO1.
  • the production resource available for process 1 is the production line SB1
  • the production demand ACT1 for process 1 of the product model A1 in the first order PO1 (need to produce 60 semi-finished products Z1) Assigned to the production line SB1 and the corresponding production period.
  • the time required to realize the production requirement ACT1 is less than a whole day, so a part of the period of the date D1 may be occupied, for example, 00:00 to 18:00 on January 1, 2021).
  • the production resources available for process 2 include production lines SB2 and SB3, and the production demand ACT2 (need to produce 60 semi-finished products Z2) for process 2 of product model A1 in the first order PO1 can be allocated to at least one of the production lines SB2 and SB3 and corresponding production period.
  • the production demand ACT2 is assigned to the production line SB2 and the corresponding production time slot. Since the process 2 is arranged after the process 1 in the production sequence, the production period corresponding to the production demand ACT2 is after the production period corresponding to the production demand ACT1. In this embodiment, as shown in FIG.
  • the production time period to which the production demand ACT2 is allocated includes the latter part of the time period of the date D1 and the former part of the time period of the date D2.
  • the production resources available for process 3 include production lines SB4 and SB5, and the production demand ACT3 for process 3 of product model A1 in the first order PO1 (need to produce 60 finished products P) can be allocated to at least one of the production lines SB4 and SB5 and corresponding production period.
  • the production demand ACT3 is assigned to the production line SB4 and the corresponding production time slot. Since the process 3 is arranged after the process 2 in the production sequence, the production period corresponding to the production demand ACT3 is after the production period corresponding to the production demand ACT2.
  • the production time period to which the production demand ACT3 is allocated includes a part of the time period after the date D2 and a part of the time period before the date D3.
  • the production capacity of the production resources available in each process may also be different, so the production situation of each process can be adjusted according to the actual situation. For example, for the first order PO1, if the production demand ACT3 of process 3 of product model A1 is completed on the production line SB4, the actual delivery time of the finished product is D3, which will lead to a delay in the delivery of the order (its planned delivery time is D2); If the production demand ACT3 of process 3 is completed in the production line SB5, and the production capacity of the production line SB5 is higher than that of the production line SB4, the finished product may be completed before the planned delivery time.
  • the production demand of each process extracted from the first order PO1 is allocated to each production line for producing display modules
  • the production demand extracted from the second order PO2 can be extracted according to the remaining capacity and the planned delivery time of the second order PO2
  • the production requirements of each process are allocated to the remaining production resources.
  • the production resource available for process 1 is the production line SB1
  • the production demand ACT4 for process 1 of the product model A2 in the second order PO2 is allocated to the production line SB1
  • the production period of ACT4 is after the production period of the production demand ACT1 of operation 1 of the first order PO1.
  • the production resources available for process 2 include production lines SB2 and SB3. If the production line SB2 has spare capacity, the production demand ACT5 of process 2 for product model A2 in the second order PO2 can be allocated to the production line SB2, or allocated to the production line SB3, the details can be selected according to the actual situation, and the details are not limited.
  • the production demand ACT5 is allocated to the corresponding production period, after the production period corresponding to the production demand ACT4.
  • the production time period corresponding to the production demand ACT5 includes the latter part of the time period of the date D2 and the first part of the time period of the date D3.
  • the production demand ACT6 is allocated to the production line SB4 or SB5 and the corresponding production time period (the later part of the period of the date D3 and the first part of the period of the date D4), which will not be repeated here.
  • the maximum utilization of production capacity can be realized on the premise of meeting the order delivery deadline, thereby further improving the capacity utilization of production resources Rate.
  • the second scheduling is described above by taking two orders as an example, the embodiments of the present disclosure are not limited thereto, and the second scheduling can be performed on any number of orders in the above manner.
  • the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.
  • Fig. 7 schematically shows a block diagram of an electronic device suitable for implementing an information identification method according to an embodiment of the present disclosure.
  • an electronic device 700 includes a processor 701, which can be loaded into a random access memory (RAM) 703 according to a program stored in a read-only memory (ROM) 702 or from a storage section 708.
  • processor 701 may include, for example, a general-purpose microprocessor (eg, a CPU), an instruction set processor and/or related chipsets, and/or a special-purpose microprocessor (eg, an application-specific integrated circuit (ASIC)), and the like.
  • Processor 701 may also include on-board memory for caching purposes.
  • the processor 701 may include a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiments of the present disclosure.
  • the processor 701, ROM 702, and RAM 703 are connected to each other through a bus 704.
  • the processor 701 executes various operations according to the method flow of the embodiment of the present disclosure by executing programs in the ROM 702 and/or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703.
  • the processor 701 may also perform various operations according to the method flow of the embodiments of the present disclosure by executing programs stored in the one or more memories.
  • the electronic device 700 may further include an input/output (I/O) interface 705 which is also connected to the bus 704 .
  • the electronic device 700 may also include one or more of the following components connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, etc.; An output section 707 of a speaker or the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like.
  • the communication section 709 performs communication processing via a network such as the Internet.
  • a drive 710 is also connected to the I/O interface 705 as needed.
  • a removable medium 711 such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc. is mounted on the drive 710 as necessary so that a computer program read therefrom is installed into the storage section 708 as necessary.
  • the present disclosure also provides a computer-readable storage medium.
  • the computer-readable storage medium may be included in the device/apparatus/system described in the above embodiments; it may also exist independently without being assembled into the device/system device/system.
  • the above-mentioned computer-readable storage medium carries one or more programs, and when the above-mentioned one or more programs are executed, the method according to the embodiment of the present disclosure is realized.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as may include but not limited to: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM) , erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable storage medium may include one or more memories other than the above-described ROM 702 and/or RAM 703 and/or ROM 702 and RAM 703.
  • Embodiments of the present disclosure also include a computer program product, which includes a computer program including program codes for executing the methods shown in the flowcharts.
  • the program code is used to make the computer system realize the production scheduling method of the product provided by the embodiment of the present disclosure.
  • the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices.
  • the computer program can also be transmitted and distributed in the form of a signal on a network medium, downloaded and installed through the communication part 709, and/or installed from the removable medium 711.
  • the program code contained in the computer program can be transmitted by any appropriate network medium, including but not limited to: wireless, wired, etc., or any appropriate combination of the above.
  • the computer program may be downloaded and installed from a network via communication portion 709 and/or installed from removable media 711 .
  • the computer program is executed by the processor 701, the above-mentioned functions defined in the system of the embodiment of the present disclosure are performed.
  • the above-described systems, devices, devices, modules, units, etc. may be implemented by computer program modules.
  • the program codes for executing the computer programs provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, specifically, high-level procedural and/or object-oriented programming language, and/or assembly/machine language to implement these computing programs.
  • Programming languages include, but are not limited to, programming languages such as Java, C++, python, "C" or similar programming languages.
  • the program code can execute entirely on the user computing device, partly on the user device, partly on the remote computing device, or entirely on the remote computing device or server.
  • the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (for example, using an Internet service provider). business to connect via the Internet).
  • LAN local area network
  • WAN wide area network
  • Internet service provider for example, using an Internet service provider
  • each block in a flowchart or block diagram may represent a module, program segment, or portion of code that includes one or more logical functions for implementing specified executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block in the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a A combination of dedicated hardware and computer instructions.

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Abstract

一种产品的生产排程方法（100）、电子设备（700）、存储介质和计算机程序产品。方法包括：获取用于对产品进行生产排程的基础数据（S110）；根据基础数据，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序（S120）；利用线性规划求解模型对产品进行生产排程，得到产品的第一排程结果，线性规划求解模型包括针对瓶颈工序的限制条件以及目标函数，第一排程结果包括多个第一条目，每个第一条目包括产品的生产日期、生产数量和对应的生产资源（S130）；将生产日期落入相同时间范围的相同产品对应的多个第一条目合并为第二条目，得到多个第二条目，每个第二条目包括同一产品在一个时间范围的生产数量（S140）；获取每个产品对应的订单并按照订单交期和订单优先级中的至少之一对产品的订单进行排序，得到订单排序结果（S150）。

Description

产品的生产排程方法、电子设备和存储介质 技术领域
本公开涉及生产排程技术领域，具体涉及一种产品的生产排程方法、电子设备、存储介质和计算机程序产品。
背景技术
在面板制造过程中，通常需要经过阵列基板(Array)的制造、显示单元(Cell)的制造以及显示模组(Module)的制造，不仅生产周期较长，且各个制造过程之间先后关联顺序较强，一旦前后制造过程生产节奏不匹配，不仅导致工厂产能利用率降低，而且由于面板制造工艺、产品型号、物料以及投产顺序等的多样性，可能导致制造工序瓶颈漂移，进而导致在制品(Workingin Process，WIP)堆积。
发明内容
本公开提供了一种产品的生产排程方法、电子设备、存储介质和计算机程序产品。
根据本公开的一方面，提供了一种产品的生产排程方法，包括：
获取用于对产品进行生产排程的基础数据；
根据基础数据，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序；
利用线性规划求解模型对产品进行生产排程，得到产品的第一排程结果，所述线性规划求解模型包括针对瓶颈工序的限制条件以及目标函数，第一排程结果包括多个第一条目，每个第一条目包括产品的生产日期、生产数量和对应的生产资源；
将生产日期落入相同时间范围的相同产品对应的多个第一条目合并为第二条目，得到多个第二条目，每个第二条目包括同一产品在一个时间范围的生产数量；
针对每个第二条目中的产品，获取产品对应的订单并按照订单交期和订单优先级中的至少之一对该产品的订单进行排序，得到订单排序结果。
根据本公开的实施例，所述方法还包括：
构建产品的生产数据模型，生产数据模型包括半成品与用于生产半成品的生产资源、物料和工序之间的关联、成品与用于生产成品的生产资源、物料和工序之间的关联以及成品与半成品之间的关联；
从每个订单提取各个工序的生产需求，每个工序的生产需求包括通过该工序计划生 产的成品或半成品的数量；
基于生产数据模型和订单排序结果，将各个订单的生产需求分配给对应的生产资源和生产时段，得到第二排程结果，所述第二排程结果包括每个生产资源在各个生产时段对应的生产需求。
根据本公开的实施例，其中，基于生产数据模型和订单排序结果，将各个订单的生产需求分配给对应的生产资源和生产时段包括：
基于生产数据模型来确定产品的各个工序的顺序，以及每个工序涉及的生产资源；
按照订单排序结果中的订单顺序对各个订单执行生产需求的分配，生产需求的分配包括将从订单提取出的各个工序的生产需求分配给对应的生产资源和生产时段，其中排序在前的工序的生产需求所对应的生产时段位于排序在后的工序的生产需求所对应的生产时段之后。
根据本公开的实施例，所述将从所述订单提取出的各个工序的生产需求分配给对应的生产资源和生产时段包括：通过前向排程法或后向排程法，将从所述订单提取出的各个工序的生产需求分配给对应的生产资源和生产时段。
根据本公开的实施例，其中，限制条件包括以下至少之一：针对设备产能的第一限制条件、针对生产线优先级的第二限制条件、针对工厂运转时间的第三限制条件、针对物料的第四限制条件以及针对换线次数的第五限制条件。
根据本公开的实施例，所述第一限制条件指示每个设备的当天计划生产量之和*节拍时间＜设备可用时间*设备稼动率；第二限制条件指示内部工厂的优先级为第一优先级，外部代工厂的优先级为第二优先级，且第一优先级低于第二优先级；第三限制条件指示工厂转运时间在预设范围内；第四限制条件指示用于制造显示模组的半成品和物料的量在预设范围内；第五限制条件指示每个设备每天生产的显示模组的型号的数量小于预设值。
根据本公开的实施例，其中，目标函数包括以下至少之一：用于使针对产品的需求满足度最大化的第一目标函数、用于使延迟交期的产品数量最小化的第二目标函数、用于使生产产品的设备产能利用率最大化的第三目标函数、用于最小化工厂运转时间的第四目标函数以及用于使产品在同一条生产线上连续生产的时间最大化的第五目标函数。
根据本公开的实施例，第一目标函数为Max(需求满足度)，其中需求满足度＝多个订单中累计的需要达交的显示模组的数量/显示模组的需求总量；第二目标函数为Min(延迟交期数量)，其中延迟交期数量＝多个订单中累计的需要交期外的显示模组的数 量/显示模组的需求总量；第三目标函数为Max(设备产能利用率)，其中设备产能利用率＝在一天内累计的设备使用时间/在一天内累计的(设备可用时间*设备稼动率)；第四目标函数为Min(工厂间转运时间)，其中工厂运转时间为多个订单中累计的工厂间运输时间；第五目标函数为Max(每个型号的显示模组在同一个生产线上连续生产的时间)；其中Max()表示最大化计算，Min()表示最小化计算。
根据本公开的实施例，所述方法还包括：在得到订单排序结果之后，从订单排序结果中各订单中去除库存量。
根据本公开的实施例，其中，生产日期以天或星期为单位，时间范围以月或季度为单位。
根据本公开的实施例，其中，产品为显示模组，半成品包括阵列基板以及包括阵列基板的显示单元，成品为包括显示单元的显示模组。
根据本公开的实施例，其中，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序包括：
基于用于生产产品的工厂的生产需求，从产品的后段核心工艺涉及的多个工序中选择至少一个工序作为产品的瓶颈工序。
根据本公开的另一方面，提供了一种电子设备，包括：
存储器和处理器，所述存储器中存储有所述处理器可执行的指令，所述指令在由所述处理器执行时使所述处理器执行实现如上所述的方法。
根据本公开的另一方面，提供了一种存储有计算机指令的非瞬时计算机可读存储介质，其中，所述计算机指令用于使所述计算机执行实现如上所述的方法。
根据本公开的另一方面，提供了一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现如上所述的方法。
附图说明
附图用于更好地理解本方案，不构成对本公开的限定。其中：
图1是根据本公开实施例的产品的生产排程方法的流程图；
图2是根据本公开实施例的获取基础数据的方法的示意图；
图3是根据本公开实施例的确定瓶颈工序的方法的示意图；
图4是根据本公开另一实施例的产品的生产排程方法的流程图；
图5是根据本公开实施例的基于生产数据模型和订单排序结果得到第二排程结果的 方法的流程图；
图6是根据本公开实施例的基于生产数据模型和订单排序结果得到第二排程结果的示意图；
图7是用来实现本公开实施例的产品的生产排程方法的电子设备的框图。
具体实施方式
以下结合附图对本公开的示范性实施例做出说明，其中包括本公开实施例的各种细节以助于理解，应当将它们认为仅仅是示范性的。因此，本领域普通技术人员应当认识到，可以对这里描述的实施例做出各种改变和修改，而不会背离本公开的范围和精神。同样，为了清楚和简明，以下的描述中省略了对公知功能和结构的描述。
图1是根据本公开实施例的产品的生产排程方法的流程图。
如图1所示，产品的生产排程方法100包括操作S110～S150。本公开实施例的方法可以是计算机实现的方法。例如可以使用计算机来基于高级计划与排程系统(Advanced Planning and Scheduling，APS)实现本公开实施例的生产排程方法。APS系统是一种综合考虑了物料、设备、人员、产能、客户需求、运输等资源约束条件，利用最优化算法自动生成工厂生产计划与生产排程的系统。
在操作S110，获取用于对产品进行生产排程的基础数据。
这里所谓的基础数据可以包括销售需求数据和生产信息数据。销售需求数据例如可以包括与产品相关的销售需求信息(例如订单交期、销售及库存计划等)。生产信息数据例如可以包括物料需求及供应情况、生产工艺信息、生产周期、半成品库存信息、制程良品率等。在本公开实施例中，可以根据获取的基础数据对产品进行生产排程。
如图2所示，例如可以利用APS系统201分别从订单管理系统(Order Management System，OMS)202中获取销售需求信息(例如订单交期、销售及库存计划等)，从企业资源计划(Enterprise Resource Planning，ERP)系统203中获取与产品或半成品库存信息相关数据，从物质需求计划(Material Requirement Planning，MRP)系统204中获取与物料需求及供应计划相关的数据，从PLM(Product Lifecycle Management，产品生命周期管理)/MDS(Master Data Management，主数据管理)集成系统205中获取与生产工艺相关的数据，从制造执行管理系统(Manufacturing Execution System，MES)206中获取与生产实绩或在工情况(例如制程良品率)相关的数据，或者可以从业务规划与合并(Business Planning and Consolidation，BPC)系统207中获取与营收、利润或成本计划等 相关的数据。上述各个系统之间可以实现数据交互，例如，对于MRP系统204来说，除了可以向APS系统201提供与物料需求及供应计划相关的数据之外，MPS系统204例如可以从APS系统201中获取相应的生产计划，从PLM/MDM集成系统205中获取物料清单(Bill of Material，BOM)信息，从ERP系统203中获取与材料库存相关的数据，并向ERP系统203提供物料采购信息(例如采购申请单PR)。基于上述各个系统之间可以实现数据交互的功能，APS系统201可以定期(或者不定期)获取用于对产品进行生产排程的基础数据。需要说明的是，图2所示仅为获取基础数据的示例，并非意在限定本公开的范围。
以上所描述的产品例如可以是任意一种或多种经由多个生产工序加工而得到的成品，具体可以根据实际确定。
在本公开实施例中，所述的产品例如可以为显示面板或者显示面板中的显示模组，下文主要以显示模组为例来进行说明。显示模组主要经由阵列基板(Array)工序、显示单元(Cell)工序以及显示模组(Module)工序加工而得到。可以理解，本公开的方案可以适用于对任意以上所述的产品进行生产排程，说明书及附图中以显示模组为例进行说明仅是示例性的，以帮助本领域技术人员理解本公开的方案，本公开不仅限于此。
在操作S110中，例如可以依据以上所述的方法获取用于对显示模组进行生产排程的基础数据，后续可以根据获取的基础数据对显示模组进行生产排程，这里不再赘述。
返回参考图1，在操作S120，根据基础数据，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序。
由于产品的整个制造过程包括多个生产工序，且各个生产工序之间先后关联顺序较强，在实际排产过程中，顺序在后的生产工序的生产计划通常需要考虑前一生产工序的供给；其中各个生产工序可能还会涉及多种产品型号、物料、制造工艺等等，一旦其中某个环节或者工序出现问题，最终都可能会影响产品的生产计划的达成。基于上述考虑，可以在制定产品的生产计划之前，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序。瓶颈工序(Bottleneck Process)通常是指制约整条生产线产出量的一个或多个生产工序或者工艺过程。瓶颈工序主要是针对生产流程来定义的，瓶颈工序的资源决定了产出量和库存水平。通常将一个流程中生产节拍(Take Time，TT)最慢的环节称为“瓶颈”。由于产品的多个生产工序的生产节拍可能不同，从而会影响各个生产工序的生产节奏。基于实践考虑，可以根据基础数据，从产品的多个工序中确定至少一个工序作为瓶颈工序，这样提前预测瓶颈工序，后续可以通过改善瓶颈工序，以避免物料和 在制品堆积，从而提高工序的产能利用率。
在本公开实施例中，瓶颈工序例如可以根据各个工序的产能来确定，例如选择产能最小的工序作为瓶颈工序，而各个工序的产能可以根据操作S110中获取的基础数据得到。
以产品是显示模组为例，前面已经介绍，显示模组的生产过程主要包括制造阵列基板(Array)的工艺段、制造显示单元(Cell)的工艺段以及制造显示模组(Module)的工艺段。在实际排产过程中，针对显示模组(Module)的工艺段的生产计划通常需要考虑显示单元工艺段的供给，而针对显示单元的工艺段的生产计划通常需要考虑阵列基板工艺段的供给。
在根据以上步骤获取与显示模组相关的基础数据之后，可以从该基础数据中提取与显示模组相关的销售需求信息(例如各种型号产品的销售需求)、各工序制程良品率、各工序生产节拍、生产线或者设备资源情况等等。可以根据产品销售量以及各工序的制程良品率确定用于生产阵列基板的工厂的生产需求、用于生产显示单元的工厂的生产需求以及用于生产显示模组的工厂的生产需求。然后，基于用于生产阵列基板的工厂的生产需求，根据生产阵列基板的各个工序的产能来确定阵列基板的瓶颈工序；基于用于生产显示单元的工厂的生产需求，根据生产显示单元的各个工序的产能来确定显示单元的瓶颈工序；基于用于生产显示模组的工厂的生产需求，根据生产显示模组的各个工序的产能来确定显示模组的瓶颈工序。
图3是根据本公开实施例的确定瓶颈工序的方法的示意图，以下将参考图3对确定瓶颈工序的方法的示例性实现方式进行说明。需要说明的是，确定各个生产工序的瓶颈工序的过程相同或类似，这里将以确定显示模组的瓶颈工序为例进行介绍。另外，由于每个工序可能还会涉及多种产品型号、物料、制造工艺等等，为了节省篇幅，除特别说明外，以下与工序相关的数据(例如产能、生产需求)例如都可以是指包括有各种产品型号的情况，下面将不再赘述。
如图3所示，在获取基础数据之后，可以从基础数据中提取关于各种型号的显示模组的月销售需求、各工序生产节拍、生产线或者设备资源情况、库存数据以及各个工序的制程良品率等。在考虑库存的情况下，根据各种型号的显示模组的月销售需求以及各工序的制程良品率计算得到相应工序的月生产需求(含各种型号)，其中各工序的月生产需求＝∑(各种型号产品的月销售需求/对应工序制程良品率)。例如，可以根据各种型号的显示模组的月销售需求300以及显示模组工序的制程良品率计算得到每种型号的 显示模组月生产需求330。
例如，制造阵列基板(Array)的工艺段例如包括工序A 1、工序A 2、…、工序A N，制造显示单元(Cell)的工艺段例如包括工序B 1、工序B 2、…、工序BN，制造显示模组(module)的工艺段例如包括工序C 1、工序C 2、…、工序C N，每个工序对应有各自的生产节拍。
以制造显示模组(module)涉及的工序C 1、工序C 2、…、工序C N为例，可以针对每个工序，根据各工序对应的生产节拍(TT)、可使用生产线资源情况(例如可使用的设备数量以及设备可用时长、设备综合效率等)以及每种型号的显示模组月生产需求330，计算得到各工序的产能331(含各种型号)。在一些实施例中，在计算工序的产能331时还可以考虑工序对应的成品或半成品的加工层数，例如对于产品为显示模组的情况，阵列基板可以包括多个层(layer)，可以在计算阵列基板的工序的产能时考虑阵列基板的层数。对于显示单元和显示模组的工序来说，可以认为加工层数为1。各工序的产能331＝设备可用时间×设备台数×设备综合效率(OEE)/(∑(各种型号产品的月生产需求×TT)/月生产需求总量)。例如，对于工序C 1，假设工序C 1对应的生产节拍为TT 1，可使用的设备数量为n1，每台设备可用时长t1，每台设备综合效率为∝1，依据上述数据可以计算得到工序C 1的产能。同样地，采用上述方式可以获得其他工序的产能，例如工序C 2至工序C N的产能。在获取各工序产能331之后，通过比较各工序的产能大小来确定瓶颈工序332，例如可以选择产能最小的工序作为瓶颈工序。例如，对于工序C 1至工序C N，各工序对应的产能分别为m 1＜m 2＜…＜m N，则确定工序C 1为显示模组的瓶颈工序332。以类似的方式，还可以计算出阵列基板的瓶颈工序和显示单元的瓶颈工序，这里不再赘述。
在一些实施例中，在确定各工序的瓶颈工序时，还可以考虑加工次数(例如层数(layer))，例如，对于工序C 1，假设工序C 1需要加工的层数为d1，则工序C 1的产能＝(设备可用时间×设备台数×设备综合效率(OEE))/加权的加工次数/(∑(各种型号产品的月生产需求×TT)/月生产需求总量)。采用上述方式可以确定其他工序的产能，从而确定显示模组工序的瓶颈工序。
基于以上描述的方法，可以根据基础数据，从显示模组的多个生产工序中确定至少一个生产工序作为瓶颈工序，这些瓶颈工序将作为生产排程的对象，用于对显示模组进行生产排程。例如，分别从显示模组的阵列基板工序、显示单元工序和显示模组工序中确定至少一个阵列基板的瓶颈工序312、至少一个显示单元的瓶颈工序322和至少一个显示模组的瓶颈工序332。
在一些实施例中，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序包括：基于用于生产产品的工厂的生产需求，根据产品的后段核心工艺的产能来确定产品的瓶颈工序。
针对从产品的多个生产工序中确定的至少一个瓶颈工序，由于各瓶颈工序对于产品的产出量和库存水平的影响程度是不同的，因而可以从上述确定的多个瓶颈工序中确定核心工艺的瓶颈工序，以用于后续对产品的生产进行排程。通过考虑核心工艺的瓶颈工序来进行产品的生产排程，可以获得更有针对性的生产计划，从而提高整个工序的产能利用率。如上面提到的，产品可以依次通过多段工艺(也称作工艺段)制造而成，每个工艺段包括多个工序。对于生产过程涉及多个阶段跨度较长的产品，控制后段核心工艺能够保证最终订单的达成。
例如，如上文提到的，显示面板的制造过程需要经历Array、Cell、Module多个工艺段，生产过程跨度较长。由于面板的这种生产特性，显示面板的生产过程主要受限于后段核心工艺。可以从显示模组工艺段涉及的多个工序中确定瓶颈工序，进而根据显示模组工艺段的瓶颈工序来安排生产排程。例如可以按照上述方式从显示模组工艺段涉及的多个工序中确定瓶颈工序，具体不再赘述。
返回参考图1，在操作S130，针对瓶颈工序的限制条件对目标函数进行线性规划求解，得到产品的第一排程结果，第一排程结果包括多个第一条目，每个第一条目包括产品的生产日期、生产数量和对应的生产资源。
确定瓶颈工序之后，可以根据瓶颈工序的限制条件对目标函数进行线性规划求解，从而得到产品的第一排程结果。
在本公开实施例中，针对瓶颈工序的限制条件对目标函数进行线性规划求解，例如可以采用优化器(optimizer)实现。在将基础数据输入优化器之前，可以对这些数据进行预处理，以将这些数据转换成预设格式(例如TXT格式)，以便于后续进行线性规划求解，表1示意性给出了一些将基础数据整理成预设格式的示例。
Figure PCTCN2022077231-appb-000001
表1
依据表1的处理方式对各瓶颈工序对应的基础数据进行预处理，以将其转换成预设格式，优化器读取转换后的数据之后，结合各瓶颈工序的限制条件对目标函数进行线性规划求解，从而得到产品的第一排程结果。其中，第一排程结果包括多个第一条目，每个第一条目包括产品的生产日期、生产数量和对应的生产资源。
在本公开实施例中，瓶颈工序涉及相关的设备、生产线、工厂、物料等等，瓶颈工序的限制条件可以包括以下至少之一：针对设备产能的第一限制条件、针对生产线优先级的第二限制条件、针对工厂运转时间的第三限制条件、针对物料的第四限制条件以及针对换线次数的第五限制条件。
其中，第一限制条件指示每个设备的当天计划生产量之和*节拍时间＜设备可用时间*设备稼动率；第二限制条件指示内部工厂的优先级为第一优先级，外部代工厂的优先级为第二优先级，且第一优先级低于第二优先级；第三限制条件指示工厂转运时间在预设范围内；第四限制条件指示用于制造显示模组的半成品和物料的量在预设范围内；第五限制条件指示每个设备每天生产的显示模组的型号的数量小于预设值。
以上所描述的目标函数例如可以包括以下至少之一：用于使针对显示模组的需求满足度最大化的第一目标函数、用于使延迟交期的显示模组数量最小化的第二目标函数、用于使生产显示模组的设备产能利用率最大化的第三目标函数、用于最小化工厂运转时间的第四目标函数以及用于使显示模组在同一条生产线上连续生产的时间最大化的第五目标函数。
其中，第一目标函数为：Max(需求满足度)，其中需求满足度＝多个订单中累计的需要达交的显示模组的数量/显示模组的需求总量；第二目标函数为：Min(延迟交期数量)，其中延迟交期数量＝多个订单中累计的需要交期外的显示模组的数量/显示模组的需求总量；第三目标函数为：Max(设备产能利用率)，其中设备产能利用率＝在一天内累计的设备使用时间/在一天内累计的(设备可用时间*设备稼动率)；第四目标函数为：Min(工厂间转运时间)，其中工厂运转时间为多个订单中累计的工厂间运输时间；第五目标函数为：Max(每个型号的显示模组在同一个生产线上连续生产的时间)，其中Max()表示最大化计算；其中Max()表示最大化计算，Min()表示最小化计算。在一些实施例中，可以根据需要为各个目标函数分别设定权重。例如，可以设置第一目标函数至第五目标函数的权重分别为1、0.1、0.001、0.001、0.001。
在本公开实施例中，依据各瓶颈工序对应的基础数据、各瓶颈工序的限制条件对目标函数进行线性规划求解，得到显示模组的第一排程结果，这个过程可以理解为，依据 产品的瓶颈工序对应的基础数据，利用优化器(例如Xpress-Optimizer)找到一种满足产品生产排程限制参数和目标函数的最优解，也即产品的第一排程结果。Xpress-Optimizer是Xpress-MP工具包中的一个求解引擎。Xpress-MP是一种数学建模和优化工具包，用于求解线性、整数、二次以及随机规划问题。Xpress-MP工具包可以用于计算机平台，并且具有不同性能的版本以解决各种不同规模的问题。Xpress-MP工具包中的Xpress-Optimizer包含的算法使得能够求解线性规划问题、混合整数规划问题、二次规划问题以及混合整数二次规划问题。
本公开通过提前预测瓶颈工序，并以产品工序的瓶颈工序的基础数据作为线性规划结果的输入，从而得到满足产品生产排程限制条件和目标函数的第一排程结果。基于上述方法，能够缓解甚至避免前后工序生产节奏不匹配以及工序瓶颈漂移，从而提高生产线产能的产能利用率。
表2示意性给出了部分针对产品的第一排程结果。如表2所示，第一排程结果包括多个第一条目，每个第一条目包括产品的生产日期、生产数量和对应的生产资源，例如在序号为1的条目中，型号为A1的产品的生产日期为2021-9-14，生产数量为100个，生产资源为生产线4。在表2中生产日期是以天来计的，然而本公开的实施例不限于此，生产日期可以采用其他的计算单位，例如以星期为单位。基于操作S130得到的产品的第一排程结果是综合考虑了设备产能、生产线优先级、工厂运转时间、物料以及生产线换线次数等因素所给出的理想状态下的生产排程。但是，基于线性规划求解方式得到的产品的生产排程结果具有一定的局限性，其难以表达生产连续性以及生产次序。例如，表2中产品A1在9月18日和9月21日的间隔投入，这样的结果与生产实际需求存在偏差。
序号 生产工厂 生产设备 产品型号 生产时间 数量
1 工厂1 生产线4 A1 2021-9-14 100
2 工厂1 生产线4 A1 2021-9-15 100
3 工厂1 生产线5 A1 2021-9-18 100
4 工厂1 生产线5 A1 2021-9-21 50
5 工厂2 生产线1 A2 2021-9-14 80
6 工厂2 生产线2 A2 2021-9-14 50
表2
在本公开实施例中，将考虑通过线性规划求解方法得到的产品的第一排程结果与实际排程过程之间存在的差异，例如无法表达生产连续性以及生产次序，对产品的第一排程结果进行产品排序和订单排序，得到订单排序结果，以下将参考操作S140和操作S150进行详细说明。
在操作S140，将生产日期落入相同时间范围的相同产品对应的多个第一条目合并为第二条目，得到多个第二条目，每个第二条目包括同一产品在一个时间范围的生产数量。
在获得产品的第一排程结果之后，可以对各种型号的产品的生产顺序进行调整，以使得生产日期落入相同时间范围的相同产品对应的多个第一条目合并为第二条目，得到多个第二条目，每个第二条目包括同一产品在一个时间范围的生产数量。上述过程可以理解为将不同订单中生产日期落入相同时间范围的相同产品进行整合，以得到以产品(或产品型号)为维度的排序结果。基于上述方式可以使得生产日期落入相同时间范围内的相同产品排序在一起，从而避免了生产不连续的情况。
根据本公开的实施例，上述生产日期例如可以以天或星期为单位，时间范围例如可以以月或季度为单位，具体可以根据实际排产情况设定，这里不做限定。
在一些实施例中，可以基于产品排序维度对整合后的结果进行排序，得到各种产品(或者产品型号)的排序结果。上述产品排序维度例如可以考虑如下因素中的至少之一：产品在时间范围内的需求量、产品对应的可用生产线的数量以及产品的生产周期。基于上述排序的方式，可以在产能有限的情况下，根据调整后的产品生产顺序来决定产能资源的分配，从而实现产能的分级利用，进而更进一步地提高产能的利用率
表3示意性示出了上述调整后的产品的排程结果。例如，以月为单位的时间范围内，在得到表2的第一排程结果进行调整之后，将在生产日期落在2021/8的所有产品型号为A1的第一条目整合为一个第二条目，该第二条目中表示了型号A1的产品在2021/8的计划生产数量为44523。类似地，将在2021/8计划生产的所有产品型号为A4的第一条目整合为另一个第二条目。以此类推，得到如表3所示的多个第二条目。
对上述第一条目进行整合之后，还可以对整合后的结果(即得到的多个第二条目)进行排序，得到对应的产品生产排序情况(见表3中产品排序一栏)。如表3所示，对于涉及同一个时间范围(例如2021年8月)的多个第二条目，可以按照产品在时间范围内的需求量来对第二条目进行排序，从而确定产品的排序为：A1＞A4＞A2＞A5＞A6。由此可以得到更贴合实际的排序结果。
基于上述调整方式可以实现不同订单的组合，可以在产能有限的情况下，根据调整 后的产品生产顺序来决定产能资源的分配，从而实现产能的分级利用，进而更进一步地提高产能的利用率。此外，基于上述方式，还可以避免生产不连续的情况。
Figure PCTCN2022077231-appb-000002
表3
在操作S150，针对每个第二条目中的产品，获取产品对应的订单并按照订单交期和订单优先级中的至少之一对该产品的订单进行排序，得到订单排序结果。
在本公开实施例中，在上述排序结果的基础上，还可以按照订单交期或订单优先级对第二条目进一步排序，上述排序结果进行更精细化调整，从而得到更准确的产品排程结果。
订单排序维度例如可以包括待排序订单的订单交期或者订单优先级。其中，订单交期越靠前的优先级越高；订单的优先级可以参考订单交期和成本来定义，订单优先级顺序例如可以设定为五个等级(仅为示例)，例如红线＞A＞B＞C＞D，其中红线表示综合订单交期和成本考虑订单排序必须靠前的情况(例如紧急情况下的生产订单)，其优先级最高。
在对产品进行产品维度排序之后，针对每种型号的产品，获取每种型号产品对应的订单，并针对每种型号产品按照订单交期和订单优先级中的至少一者对该产品型号的订单进行排序，从而得到该产品型号下的订单排序结果。采用上述方式，可以在产品排序的基础上，从订单排序维度对产品排序维度排序的结果进行进一步细化处理，从而使得 产品排序结果更准确。
表4示意性示出了表3中产品型号为A1的订单排序结果。请一并参阅表3和表4，在表3中，以月为单位的时间范围内，将在2021/8计划生产的所有产品型号为A1的第一条目整合为如表3所示的第二条目，其中与产品型号A1对应的订单包括三个(仅为示例)，例如订单A1-001至A1-003。
可以理解，若仅从产品排序维度进行排序，可能无法对每个产品对应的多个订单进行准确排序，这就可能存在应当排序在前的订单实际上可能排序靠后的情况。为了获取更准确的产品的排程结果，可以根据操作S150中的方法对与产品型号A1对应的订单A1-001至A1-003进行排序，得到订单排序结果(如表4所示)。
Figure PCTCN2022077231-appb-000003
表4
图4是根据本公开另一实施例的产品的生产排程方法的流程图。
如图4所示，在本公开实施例中，产品的生产排程方法400包括操作S410～S480。其中，操作S410至操作S450分别与操作S110至操作S150以相同的方式实现，重复的部分不再详细赘述。
在操作S410，获取用于对产品进行生产排程的基础数据。
在操作S420，根据基础数据，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序。
在操作S430，利用线性规划求解模型对产品进行生产排程，得到产品的第一排程结果，线性规划求解模型包括针对瓶颈工序的限制条件以及目标函数，第一排程结果包括多个第一条目，每个第一条目包括产品的生产日期、生产数量和对应的生产资源。
在操作S440，将生产日期落入相同时间范围的相同产品对应的多个第一条目合并为第二条目，得到多个第二条目，每个第二条目包括同一产品在一个时间范围的生产数量。
在操作S450，针对每个第二条目中的产品，获取产品对应的订单并按照订单交期和订单优先级中的至少之一对该产品的订单进行排序，得到订单排序结果。在一些实施例 中，还可以在得到的订单排序结果中从各个订单中去除产品的库存量，以使各订单包括生产净需求。
在操作S460，构建产品的生产数据模型。
在本公开实施例中，可以依据以上获取的基础数据，得到针对每种产品型号的产品的生产路径、原材料、半成品/成品、产能以及库存信息等，基于上述数据或信息可以构建得到针对每种产品型号的产品的生产数据模型。例如可以利用SupplyNet引擎来构建生产数据模型。SupplyNet引擎是一种用于生产排程的后台程序，可以用于例如但不限于基础数据检验生产计划模拟、计划报表分析、工厂产能利用率分析等等。
这里所谓的生产数据模型例如可以包括半成品与用于生产半成品的生产资源、物料和工序之间的关联、成品与用于生产成品的生产资源、物料和工序之间的关联以及成品与半成品之间的关联。
以构建显示模组的生产数据模型为例，根据已经获取的基础数据，根据本操作S460的方法构建得到针对每种产品型号的显示模组的生产数据模型，该生产数据模型包括半成品阵列基板以及包括阵列基板的显示单元与生产上述半成品的生产资源、物料和工序之间的关联、成品(包括显示单元的显示模组)与用于生产成品的生产资源、物料和工序之间的关联以及成品(包括显示单元的显示模组)与半成品(阵列基板以及包括阵列基板的显示单元)之间的关联。
在操作S470，从每个订单提取各个工序的生产需求，每个工序的生产需求包括通过该工序计划生产的成品或半成品的数量。
从每个订单提取成品的生产需求，然后根据成品与半成品之间的关联、物料与工序之间的关联等，将成品的生产需求转化为各个工序的生产需求，每个工序的生产需求包括通过该工序计划生产的成品或半成品的数量。其中，将成品的生产需求转化为各个工序的生产需求与以上描述的确认各工序的生产需求的过程相同或类似，在此不做赘述。
在操作S480，基于生产数据模型和订单排序结果，将各个订单的生产需求分配给对应的生产资源和生产时段，得到第二排程结果。
基于上述构建得到的产品的生产数据模型和订单排序结果，可以获取每个工序的生产资源情况以及订单的排序情况、订单的交付日期等，由此可以依据订单排序结果、订单交付日期等将各个订单的生产需求分配给对应的生产资源和生产时段，从而得到第二排程结果。
在本公开实施例中，例如可以通过前向排程(Forward Scheduling)方法基于生产数据模型和订单排序结果将各个订单的生产需求分配给对应的生产资源和生产时段，得到第二排程结果。
前向排程方法通常是指按照订单排序结果中的优选顺序，以排序在前的订单为起点，向排序在后的订单推导排程，一直到所有订单排完为止。在此过程中，通常可以根据生产资源产能利用情况适时安排订单以消耗剩余的产能，以提高生产资源的产能利用率。
需要说明的是，本公开实施例中，将各个订单的生产需求分配给对应的生产资源和生产时段并非仅限于前向排程方法，在其他实施例中，可以根据实际选择其他合适的方式，例如，可以采用后向排程(Backward Scheduling)方法，本公开对此不做限定。
本公开实施例中，基于生产数据模型和订单排序结果来提供相对更合理的第二排程结果，提高了产能利用率以及生产效率。
图5是根据本公开实施例的基于生产数据模型和订单排序结果得到第二排程结果的方法的流程图。以下将参考图5对上述操作S480的示例实现方式进行说明。
如图5所示，基于生产数据模型和订单排序结果得到第二排程结果的方法包括操作S581～S582。
在操作S581，基于生产数据模型来确定产品的各个工序的顺序，以及每个工序涉及的生产资源。
前面已经介绍，生产数据模型可以包括半成品与用于生产半成品的生产资源、物料和工序之间的关联、成品与用于生产成品的生产资源、物料和工序之间的关联以及成品与半成品之间的关联。因而，可以根据构建的生产数据模型可以确定产品的各个工序的先后顺序以及每个工序涉及的生产资源。
在操作S582，按照订单排序结果中的订单顺序对各个订单执行生产需求的分配，生产需求的分配包括将从订单提取出的各个工序的生产需求分配给对应的生产资源和生产时段，其中排序在前的工序的生产需求所对应的生产时段位于排序在后的工序的生产需求所对应的生产时段之前。
在对各个订单执行生产需求的分配的过程中，如果分配给各工序的生产资源较为充足，可以根据订单排序结果以及订单的交期等灵活地将从各个订单提取出的各个工序的生产需求分配给对应的生产资源和生产时段。由此，可以获得更合理的排程结果，提高生产效率和产能利用率。
图6是根据本公开实施例的基于生产数据模型和订单排序结果得到第二排程结果的示意图。以下将参考图6并以确定显示模组的第二排程结果为例对本公开的方案进行介绍。图6中示意性示出了构建得到的显示模组的生产数据模型601、订单排序结果中的多个订单(例如602和603)以及基于生产数据模型601将订单排序结果中的多个订单(例如602和603)分配给各个用于生产显示模组的生产资源所得到的第二排程结果604。需要说明的是，图6所示仅为示例，旨在帮助本领域技术人员理解本公开的方案，并非用以限定本公开的保护范围。
如图6所示，根据生产数据模型601能够确定半成品与用于生成半成品的设备、物料和工序之间的关联、成品与用于生成成品的设备、物料和工序之间的关联以及成品与半成品之间的关联。例如，根据生产数据模型601能够确定原材料R通过工序1得到半成品Z1，半成品Z1经过工序2得到半成品Z2，半成品Z2经过工序3得到成品P。根据生产数据模型601还能够确定可供工序1使用的生产资源包括生产线SB1，可供工序2使用的生产资源包括生产线SB2和SB3，可供工序3使用的生产资源包括生产线SB4和SB5。根据生产数据模型601确定的上述信息后续可以用来进行第二次排程，以最大化利用生产线产能，从而更进一步地提高生产线的产能利用率。
如图6中的602和603所示，第一排程结果中涉及两个不同订单PO1和PO2。两个订单的顺序可以是通过上述订单排序结果来确定的，例如第一订单PO1排在第二订单PO2之前。可以从每个订单中提取各个工序的生产需求。例如，第一订单PO1包含的生产信息例如包括产品型号(例如产品型号A1)、计划生产数量(例如60个)和产品的计划交付时间(例如D2)。根据第一订单PO1中针对产品型号A1的计划生产数量可以确定各个工序的生产需求，例如，确定产品型号A1的工序1、工序2和工序3的生产需求分别为ACT1、ACT2和ACT3，其中生产需求ACT1指示需要生产60个半成品Z1，生产需求ACT2指示需要生产60个半成品Z2，生产需求ACT3指示需要生产60个成品P。基于同样地方式，针对第二订单PO2(例如产品型号A2，计划生产数量80，计划交付时间D4)，可以确定产品型号A2的工序1、工序2和工序3的生产需求分别为ACT4、ACT5和ACT6，生产需求ACT4指示需要生产80个半成品Z1，生产需求ACT5指示需要生产80个Z2，生产需求ACT6指示需要生产80个成品P。
请继续参阅图6，基于以上构建得到的显示模组的生产数据模型601中可使用的生产线情况，例如可以通过前向排程方法将显示模组的多个订单(例如第一订单PO1和第二订单PO2)中提取出来的针对各个工序的生产需求分配给对应的生产资源(例如生 产线SB1、SB2、SB3、SB4、SB5)和生产时段(例如日期D1至D4中的各个时段)，得到第二排程结果604。第二排程结果604中的D1至D4表示连续的日期，例如分别表示2021年的01月01日、01月02日、01月03日和01月04日。当然本公开的实施例不限于此，日期可以根据需要来采用各种其他表达方式，例如D1至D4也可以表示2021年1月的第一周、第二周、第三周和第四周，等等。
例如，由于第一订单PO1排在第二订单PO2之前，因此先对第一订单PO1中提取出来的各个工序的生产需求进行资源分配。
对于第一订单PO1，根据生产数据模型601确定了工序1可使用的生产资源为生产线SB1，因而将第一订单PO1中针对产品型号A1的工序1的生产需求ACT1(需要生产60个半成品Z1)分配给生产线SB1和对应的生产时段。在本实施例中，生产需求ACT1的实现所需要的时间不足一整天，因此可以占用日期D1的一部分时段，例如2021年1月1日的00：00至18：00)。工序2可使用的生产资源包括生产线SB2和SB3，可以将第一订单PO1中针对产品型号A1的工序2的生产需求ACT2(需要生产60个半成品Z2)分配给生产线SB2和SB3的至少之一以及对应的生产时段。例如在图6的示例中将生产需求ACT2分配了给生产线SB2和对应的生产时段。由于工序2在生产顺序上排在工序1之后，因此生产需求ACT2对应的生产时段在生产需求ACT1对应的生产时段之后。在本实施例中，如图6所示，生产需求ACT2被分配到的生产时段包括日期D1的后一部分时段和日期D2的前一部分时段。工序3可使用的生产资源包括生产线SB4和SB5，可以将第一订单PO1中针对产品型号A1的工序3的生产需求ACT3(需要生产60个成品P)分配给生产线SB4和SB5的至少之一以及对应的生产时段。例如在图6的示例中将生产需求ACT3分配给了生产线SB4和对应的生产时段。由于工序3在生产顺序上排在工序2之后，因此生产需求ACT3对应的生产时段在生产需求ACT2对应的生产时段之后。在本实施例中，如图6所示，生产需求ACT3被分配到的生产时段包括日期D2的后一部分时段和日期D3的前一部分时段。
由于每个工序的生产节拍不同，各个工序中可使用的生产资源的产能也可能不同，因而每个工序的生产情况可以根据实际情况调整。例如，对于第一订单PO1，如果将针对产品型号A1的工序3的生产需求ACT3在生产线SB4完成，实际成品交付时间为D3，这会导致订单的交期延误(其计划交付时间为D2)；如果将工序3的生产需求ACT3在生产线SB5完成，其中生产线SB5的产能高于生产线SB4的产能，此时成品可能在计划交付时间之前完成。
在第一订单PO1中提取出的各个工序的生产需求被分配给各个用于生产显示模组的生产线之后，可以根据剩余产能以及第二订单PO2的计划交付时间将第二订单PO2中提取出来的各个工序的生产需求分配给剩余生产资源。
请参阅图6，对于第二订单PO2，由于工序1可使用的生产资源为生产线SB1，因而将第二订单PO2中针对产品型号A2的工序1的生产需求ACT4分配给生产线SB1，并且该生产需求ACT4的生产时段在第一订单PO1的工序1的生产需求ACT1的生产时段之后。工序2可使用的生产资源包括生产线SB2和SB3，若生产线SB2还有剩余产能，可以将第二订单PO2中针对产品型号A2的工序2的生产需求ACT5分配给生产线SB2，或者将其分配给生产线SB3，具体可以根据实际选择，具体不做限定。类似地，生产需求ACT5分配至对应的生产时段，在生产需求ACT4对应的生产时段之后。在本实施例中生产需求ACT5对应的生产时段包括日期D2的后一部分时段和日期D3的前一部分时段。以类似的方式，将生产需求ACT6分配给生产线SB4或者SB5以及对应的生产时段(日期D3的后一部分时段和日期D4的前一部分时段)，这里不再赘述。
通过按照上述方式分配从第一订单PO1和第二订单PO2中提取的各个生产需求，使得在满足订单交付期限要求的前提下，能够实现产能的最大化利用，从而更进一步提高生产资源的产能利用率。
虽然上文中以两个订单为例对第二次排程进行了说明，然而本公开的实施例不限于此，可以按照上述方式对任意数量的订单进行第二次排程。
根据本公开的实施例，本公开还提供了一种电子设备、一种可读存储介质和一种计算机程序产品。
图7示意性示出了根据本公开实施例的适于实现信息识别方法的电子设备的方框图。
如图7所示，根据本公开实施例的电子设备700包括处理器701，其可以根据存储在只读存储器(ROM)702中的程序或者从存储部分708加载到随机访问存储器(RAM)703中的程序而执行各种适当的动作和处理。处理器701例如可以包括通用微处理器(例如CPU)、指令集处理器和/或相关芯片组和/或专用微处理器(例如，专用集成电路(ASIC))等等。处理器701还可以包括用于缓存用途的板载存储器。处理器701可以包括用于执行根据本公开实施例的方法流程的不同动作的单一处理单元或者是多个处理单元。
在RAM 703中，存储有电子设备700操作所需的各种程序和数据。处理器701、ROM 702以及RAM 703通过总线704彼此相连。处理器701通过执行ROM 702和/或RAM 703 中的程序来执行根据本公开实施例的方法流程的各种操作。需要注意，所述程序也可以存储在除ROM 702和RAM 703以外的一个或多个存储器中。处理器701也可以通过执行存储在所述一个或多个存储器中的程序来执行根据本公开实施例的方法流程的各种操作。
根据本公开的实施例，电子设备700还可以包括输入/输出(I/O)接口705，输入/输出(I/O)接口705也连接至总线704。电子设备700还可以包括连接至I/O接口705的以下部件中的一项或多项：包括键盘、鼠标等的输入部分706；包括诸如阴极射线管(CRT)、液晶显示器(LCD)等以及扬声器等的输出部分707；包括硬盘等的存储部分708；以及包括诸如LAN卡、调制解调器等的网络接口卡的通信部分709。通信部分709经由诸如因特网的网络执行通信处理。驱动器710也根据需要连接至I/O接口705。可拆卸介质711，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器710上，以便于从其上读出的计算机程序根据需要被安装入存储部分708。
本公开还提供了一种计算机可读存储介质，该计算机可读存储介质可以是上述实施例中描述的设备/装置/系统中所包含的；也可以是单独存在，而未装配入该设备/装置/系统中。上述计算机可读存储介质承载有一个或者多个程序，当上述一个或者多个程序被执行时，实现根据本公开实施例的方法。
根据本公开的实施例，计算机可读存储介质可以是非易失性的计算机可读存储介质，例如可以包括但不限于：便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。例如，根据本公开的实施例，计算机可读存储介质可以包括上文描述的ROM 702和/或RAM 703和/或ROM 702和RAM 703以外的一个或多个存储器。
本公开的实施例还包括一种计算机程序产品，其包括计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。当计算机程序产品在计算机系统中运行时，该程序代码用于使计算机系统实现本公开实施例所提供的产品的生产排程方法。
在该计算机程序被处理器701执行时执行本公开实施例的系统/装置中限定的上述功能。根据本公开的实施例，上文描述的系统、装置、模块、单元等可以通过计算机程序模块来实现。
在一种实施例中，该计算机程序可以依托于光存储器件、磁存储器件等有形存储介质。在另一种实施例中，该计算机程序也可以在网络介质上以信号的形式进行传输、分发，并通过通信部分709被下载和安装，和/或从可拆卸介质711被安装。该计算机程序包含的程序代码可以用任何适当的网络介质传输，包括但不限于：无线、有线等等，或者上述的任意合适的组合。
在这样的实施例中，该计算机程序可以通过通信部分709从网络上被下载和安装，和/或从可拆卸介质711被安装。在该计算机程序被处理器701执行时，执行本公开实施例的系统中限定的上述功能。根据本公开的实施例，上文描述的系统、设备、装置、模块、单元等可以通过计算机程序模块来实现。
根据本公开的实施例，可以以一种或多种程序设计语言的任意组合来编写用于执行本公开实施例提供的计算机程序的程序代码，具体地，可以利用高级过程和/或面向对象的编程语言、和/或汇编/机器语言来实施这些计算程序。程序设计语言包括但不限于诸如Java，C++，python，“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中，远程计算设备可以通过任意种类的网络，包括局域网(LAN)或广域网(WAN)，连接到用户计算设备，或者，可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，上述模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图或流程图中的每个方框、以及框图或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
本领域技术人员可以理解，本公开的各个实施例和/或权利要求中记载的特征可以进行多种组合或/或结合，即使这样的组合或结合没有明确记载于本公开中。特别地，在不脱离本公开精神和教导的情况下，本公开的各个实施例和/或权利要求中记载的特征可以进行多种组合和/或结合。所有这些组合和/或结合均落入本公开的范围。
以上对本公开的实施例进行了描述。但是，这些实施例仅仅是为了说明的目的，而并非为了限制本公开的范围。尽管在以上分别描述了各实施例，但是这并不意味着各个实施例中的措施不能有利地结合使用。本公开的范围由所附权利要求及其等同物限定。不脱离本公开的范围，本领域技术人员可以做出多种替代和修改，这些替代和修改都应落在本公开的范围之内。

Claims (15)

  1. 一种产品的生产排程方法，包括：
    获取用于对产品进行生产排程的基础数据；
    根据所述基础数据，从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序；
    利用线性规划求解模型对产品进行生产排程，得到产品的第一排程结果，所述线性规划求解模型包括针对瓶颈工序的限制条件以及目标函数，所述第一排程结果包括多个第一条目，每个第一条目包括产品的生产日期、生产数量和对应的生产资源；
    将生产日期落入相同时间范围的相同产品对应的多个第一条目合并为第二条目，得到多个第二条目，每个第二条目包括同一产品在一个时间范围的生产数量；
    针对每个第二条目中的产品，获取产品对应的订单并按照订单交期和订单优先级中的至少之一对该产品的订单进行排序，得到订单排序结果。
  2. 根据权利要求1所述的方法，还包括：
    构建产品的生产数据模型，所述生产数据模型包括半成品与用于生产半成品的生产资源、物料和工序之间的关联、成品与用于生产成品的生产资源、物料和工序之间的关联以及成品与半成品之间的关联；
    从每个订单提取各个工序的生产需求，每个工序的生产需求包括通过该工序计划生产的成品或半成品的数量；
    基于所述生产数据模型和所述订单排序结果，将各个订单的生产需求分配给对应的生产资源和生产时段，得到第二排程结果，所述第二排程结果包括每个生产资源在各个生产时段对应的生产需求。
  3. 根据权利要求2所述的方法，其中，所述基于所述生产数据模型和所述订单排序结果，将各个订单的生产需求分配给对应的生产资源和生产时段包括：
    基于所述生产数据模型来确定产品的各个工序的顺序，以及每个工序涉及的生产资源；
    按照订单排序结果中的订单顺序对各个订单执行生产需求的分配，所述生产需求的分配包括将从所述订单提取出的各个工序的生产需求分配给对应的生产资源和生产时段，其中排序在前的工序的生产需求所对应的生产时段位于排序在后的工序的生产需求所对应的生产时段之前。
  4. 根据权利要求3所述的方法，其中，所述将从所述订单提取出的各个工序的生 产需求分配给对应的生产资源和生产时段包括：
    通过前向排程法或后向排程法，将从所述订单提取出的各个工序的生产需求分配给对应的生产资源和生产时段。
  5. 根据权利要求1至4中任一项所述的方法，其中，所述限制条件包括以下至少之一：针对设备产能的第一限制条件、针对生产线优先级的第二限制条件、针对工厂运转时间的第三限制条件、针对物料的第四限制条件以及针对换线次数的第五限制条件。
  6. 根据权利要求5所述的方法，其中，
    所述第一限制条件指示每个设备的当天计划生产量之和*节拍时间＜设备可用时间*设备稼动率；
    第二限制条件指示内部工厂的优先级为第一优先级，外部代工厂的优先级为第二优先级，且第一优先级低于第二优先级；
    第三限制条件指示工厂转运时间在预设范围内；
    第四限制条件指示用于制造显示模组的半成品和物料的量在预设范围内；
    第五限制条件指示每个设备每天生产的显示模组的型号的数量小于预设值。
  7. 根据权利要求1至6中任一项所述的方法，其中，所述目标函数包括以下至少之一：用于使针对产品的需求满足度最大化的第一目标函数、用于使延迟交期的产品数量最小化的第二目标函数、用于使生产产品的设备产能利用率最大化的第三目标函数、用于最小化工厂运转时间的第四目标函数以及用于使产品在同一条生产线上连续生产的时间最大化的第五目标函数。
  8. 根据权利要求7所述的方法，其中，
    第一目标函数为Max(需求满足度)，其中需求满足度＝多个订单中累计的需要达交的显示模组的数量/显示模组的需求总量；
    第二目标函数为Min(延迟交期数量)，其中延迟交期数量＝多个订单中累计的需要交期外的显示模组的数量/显示模组的需求总量；
    第三目标函数为Max(设备产能利用率)，其中设备产能利用率＝在一天内累计的设备使用时间/在一天内累计的(设备可用时间*设备稼动率)；
    第四目标函数为Min(工厂间转运时间)，其中工厂运转时间为多个订单中累计的工厂间运输时间；
    第五目标函数为Max(每个型号的显示模组在同一个生产线上连续生产的时间)；
    其中Max()表示最大化计算，Min()表示最小化计算。
  9. 根据权利要求1至8中任一项所述的方法，还包括：在得到订单排序结果之后，从订单排序结果中各订单中去除库存量。
  10. 根据权利要求1至9中任一项所述的方法，其中，所述生产日期以天或星期为单位，所述时间范围以月或季度为单位。
  11. 根据权利要求1至10中任一项所述的方法，其中，所述产品为显示模组，所述半成品包括阵列基板以及包括阵列基板的显示单元，所述成品为包括显示单元的显示模组。
  12. 根据权利要求1至11中任一项所述的方法，其中，所述从产品的多个生产工序中确定至少一个生产工序作为瓶颈工序包括：
    基于用于生产产品的工厂的生产需求，从产品的后段核心工艺涉及的多个工序中选择至少一个工序作为产品的瓶颈工序。
  13. 一种电子设备，包括存储器和处理器，所述存储器中存储有所述处理器可执行的指令，所述指令在由所述处理器执行时使所述处理器执行如权利要求1至12中任一项所述的方法。
  14. 一种存储有计算机指令的非瞬时计算机可读存储介质，其中，所述计算机指令用于使所述计算机执行根据权利要求1至12中任一项所述的方法。
  15. 一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现根据权利要求1至12中任一项所述的方法。
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KR20120138549A (ko) * 2011-06-15 2012-12-26 전북대학교산학협력단 주문생산형 공장의 생산일정계획 수립을 위한 알고리즘 및 이를 이용한 생산일정계획 수립시스템
CN104077633A (zh) * 2014-06-27 2014-10-01 歌尔声学股份有限公司 基于瓶颈工序的排产方法
CN113506081A (zh) * 2021-06-15 2021-10-15 刘俊艳 一种生产计划管理系统及其排产方法

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CN117745721A (zh) * 2024-02-19 2024-03-22 江苏中天互联科技有限公司 一种基于标识解析的排产计划优化方法及相关设备
CN117745721B (zh) * 2024-02-19 2024-05-07 江苏中天互联科技有限公司 一种基于标识解析的排产计划优化方法及相关设备

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