Workshop rack data processing method and device, medium and electronic equipment

Abstract

Embodiments of the present application provide a kind of workshop rack data processing method, device, computer readable medium and electronic equipment.The method comprises: obtaining the production constraint data and production rule data of various parts in rack measurement time, and the rack is used to store the parts;According to the production constraint data and the production rule data, the production of various parts in the rack measurement time is planned by the mixed integer programming model constructed in advance, and the production scheduling data of various parts is obtained;According to the production scheduling data, the input quantity and optimization quantity of various racks in the rack measurement time are counted.The technical scheme of the embodiment of the present application can improve the accuracy of workshop rack data processing.

Description

Technical Field

[0001] This application relates to the field of computer and data processing technology, and more specifically, to a method, apparatus, medium, and electronic equipment for processing data on workshop material racks. Background Technology

[0002] Shelves, also known as racks, are used to store various parts in production workshops. Currently, the deployment plan for shelves affects the overall production efficiency of the workshop. Insufficient shelf deployment leads to production stoppages, wasting resources and preventing the completion of production tasks. Excessive shelf deployment results in wasted costs, and shelves not used for extended periods occupy warehouse space, requiring wasted manpower for disposal if damaged. In existing solutions, shelf deployment plans in warehouses are often based on experience; however, this method often fails to accurately determine shelf deployment schedules. Therefore, improving the accuracy of shelf data processing in the workshop is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] Embodiments of this application provide a workshop rack data processing method, apparatus, computer program product or computer program, computer-readable medium and electronic device, which can at least to some extent improve the accuracy of workshop rack data processing.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to one aspect of the embodiments of this application, a workshop rack data processing method is provided. The method includes: acquiring production constraint data and production rule data of various parts within a rack measurement time, wherein the rack is used to store the parts; performing production planning for various parts within the rack measurement time using a pre-constructed mixed integer programming model based on the production constraint data and the production rule data to obtain production scheduling data for various parts; and calculating the input quantity and optimization quantity of various racks within the rack measurement time based on the production scheduling data.

[0006] In some embodiments of this application, based on the foregoing scheme, the objective function of the mixed-integer programming model includes the following formula:

[0007]

[0008] Where pp represents the penalty factor for unmet part production demand, and lp it Let be an integer variable, representing the number of parts i that did not meet production requirements at time t; pe represents the penalty factor for parts not reaching the economic batch size; wtx itFor continuous variables, represents the wasted time for spare parts i at time t due to not reaching the economic batch production quantity; pi represents the inventory penalty factor, inv it Let be an integer variable, representing the inventory quantity of part i at the end of time t; pm represents the penalty factor for not meeting the safety stock requirement, ln it This is an integer variable representing the quantity of part i whose inventory at the end of time t does not meet the production demand for the next day.

[0009] In some embodiments of this application, based on the foregoing scheme, the step of calculating the input and optimized quantities of various racks within the rack calculation period according to the production scheduling data includes: simulating the inventory quantity of various parts and the rack capacity of various racks at the end of each day within the rack calculation period according to the production scheduling data; calculating the occupied quantity of various racks at the end of each day within the rack calculation period according to the inventory quantity of various parts and the rack capacity of various racks; and calculating the input and optimized quantities of various racks within the rack calculation period according to the occupied quantity.

[0010] In some embodiments of this application, based on the foregoing scheme, the number of various racks occupied at the end of each day during the rack measurement period is calculated using the following formula:

[0011]

[0012] Among them, rack rt An integer variable representing the number of shelves r occupied at the end of time t, RI r Inv represents the set of parts types that the rack r can store. it `cap` is an integer variable representing the inventory quantity of part `i` at the end of time `t`. r This indicates the capacity of the rack r, which is the maximum number of parts of the corresponding type that can be placed on a single rack.

[0013] In some embodiments of this application, based on the aforementioned scheme, the step of calculating the input and optimized quantities of various racks within the rack measurement time according to the occupied quantity includes: calculating the peak occupied quantity of various racks within the rack measurement time; obtaining the actual rack quantity of various racks; and determining the input and optimized quantities of various racks within the rack measurement time based on the peak occupied quantity and the actual rack quantity.

[0014] In some embodiments of this application, based on the foregoing scheme, the quantity of various racks input during the rack measurement time is determined by the following formula:

[0015] investment r =max(highest) r -actualr ,0)

[0016] Among them, investment r This indicates the number of items loaded onto the rack (r) within the calculated time period of the rack, highest. r This indicates the peak number of shelves r occupied during the calculated time period of the shelf; actual r This indicates the actual number of racks in the current rack r.

[0017] In some embodiments of this application, based on the foregoing scheme, the optimized quantity of various racks within the rack measurement time is determined by the following formula:

[0018] optimization r =max(actual r -highest r ,0)

[0019] Among them, optimization r This represents the optimized number of racks r within the calculated time period of the rack, highest. r This indicates the peak number of shelves r occupied during the calculated time period of the shelf; actual r This indicates the actual number of racks in the current rack r.

[0020] According to one aspect of the embodiments of this application, a workshop rack data processing device is provided. The device includes: an acquisition unit, configured to acquire production constraint data and production rule data of various parts within the rack measurement time, wherein the rack is used to store the parts; a planning unit, configured to perform production planning for various parts within the rack measurement time based on the production constraint data and the production rule data using a pre-constructed mixed integer programming model, thereby obtaining production scheduling data for various parts; and a statistics unit, configured to calculate the input quantity and optimization quantity of various racks within the rack measurement time based on the production scheduling data.

[0021] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0022] According to one aspect of the embodiments of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the above embodiments.

[0023] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method described in the above embodiments.

[0024] In some embodiments of this application, the technical solutions provide that, based on the production constraint data and production rule data of various parts within the calculation time, a pre-constructed mixed integer programming model is used to plan the production of various parts within the calculation time of the rack, thereby obtaining production scheduling data for various parts. Then, based on the production scheduling data, the input quantity and optimized quantity of various racks within the calculation time of the rack can be statistically analyzed. Since this technical solution considers the scheduling data of various parts in the future production process through a mixed integer programming model, the rack usage within the calculation period can be statistically analyzed using the scheduling data. This can guide the actual input quantity or optimized quantity of racks, solving the problem of a large gap between the rack input quantity determined by current rack input experience and the actual production rack demand. This improves the accuracy of rack data processing and avoids production stoppages or waste of rack resources due to inaccurate rack data.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0027] Figure 1 A flowchart of a workshop rack data processing method according to an embodiment of this application is shown;

[0028] Figure 2 A detailed flowchart illustrating the input and optimization quantities of various racks during the rack measurement time, according to one embodiment of this application, is shown.

[0029] Figure 3 A detailed flowchart illustrating the input and optimization quantities of various racks during the rack measurement time, according to one embodiment of this application, is shown.

[0030] Figure 4A block diagram of a workshop rack data processing apparatus according to an embodiment of this application is shown;

[0031] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0033] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0035] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0036] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0037] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0038] Before elaborating on this solution in detail, let's first explain the specific application scenarios of this solution:

[0039] The racks involved in this application, also known as shelves, are the main storage equipment in a material warehouse, facilitating the classification, storage, and receipt of materials. Based on function, they can be divided into dedicated racks and common racks. Dedicated racks can only store one type of material (such as parts), while common racks can store multiple types of materials.

[0040] For example, in the automotive manufacturing sector, automotive OEMs are mainly divided into three major workshops: body shop (BS), painting shop (PS / PA), and general assembly shop (GA / CA).

[0041] The stamping workshop serves as a precursor to the body shop, providing essential parts for different parts of the vehicle body, such as left and right doors and front and rear panels. The body shop provides overall vehicle demand information, which, based on the Bill of Materials (BOM), can be broken down into stamping part requirements. Planners schedule production in the stamping workshop according to the required quantity and timing of stamping parts, as well as production constraints, guiding actual production. During production, stamped parts, after completing the stamping process, must be stored on racks; if there are no empty racks, stamping cannot proceed.

[0042] The logistics department is responsible for the input and daily management of material racks. During stamping operations, the stamping workshop transfers the necessary material racks from the logistics department to the stamping workshop, storing the stamped parts and transporting them to the warehouse for use by the body shop. After the body shop has used the parts on the racks, the empty racks are returned to the logistics department for unified dispatch. Therefore, the logistics department's plan for material rack input affects the overall production efficiency of the workshop. Insufficient material rack input leads to production stoppages, wasting production resources and preventing the completion of production tasks; excessive material rack input leads to cost waste, and unused racks occupy warehouse space and require wasted manpower for disposal after damage.

[0043] The deployment of material racks takes time, approximately three months from application for procurement to final production. This period is known as the material rack deployment lead time. Therefore, planners need to develop material rack deployment plans at least three months in advance to provide material rack support for production three months later.

[0044] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0045] Figure 2 A flowchart illustrating a workshop rack data processing method according to an embodiment of this application is shown. This method can be executed by a device with computational processing capabilities. (Refer to...) Figure 1 As shown, the data processing method for the workshop material rack includes at least steps 110 to 150, which are detailed below:

[0046] In step 110, production constraint data and production rule data of various parts are obtained within the material rack calculation time. The material rack is used to store the parts.

[0047] In this application, the material rack calculation time can refer to n months after the material rack is put into operation. The required production constraint data and production rule data are entered into the system. These data can be adjusted arbitrarily according to the actual situation before the scheduling plan is formulated.

[0048] In this application, production constraint data may specifically include part information, rack information, production line information, calendar information (within the calculation time range), part demand information (within the calculation time range), and existing inventory information.

[0049] The information includes: part number, corresponding rack number, production line number, and production rate; rack information, rack number, current quantity, and capacity; production line information, line number, preparation time, and changeover time; date, line number, and available duration; part demand information, date, part number, and required quantity; and existing inventory information, part number and inventory quantity.

[0050] In this application, the production rule data may specifically include the following aspects:

[0051] 1. Economic batch production of parts, that is, the production quantity of each part in each batch should meet the economic production batch as much as possible. The formula for calculating the economic production batch is as follows (1):

[0052]

[0053] Among them, epq i Indicates the economic production quantity of part i; spm iThis represents the number of parts i produced per minute; mpt l This indicates the preparation time for each production line; ml i This indicates the production line corresponding to part i.

[0054] It should be noted that the economic production batch refers to the optimal quantity to produce at one time when replenishing inventory due to the setup and adjustment time of the production system.

[0055] For example, in a stamping workshop, different stamped parts require different dies for stamping. These dies are large and heavy, necessitating the use of cranes to place them onto the production line. Therefore, changing dies takes a long time, including the preparation time for the crane to move the die to the production line and the manual die-changing time on the production line. Preparation time is typically around one hour, and die-changing time is generally around ten minutes. If the production time of the previous part does not reach the preparation time before the next die is ready for production, the production line will stop until the next die is available and production resumes. Therefore, the economic production batch size for stamped parts is: preparation time × production rate.

[0056] 2. Full rack production, meaning that the production quantity of each part in each batch must be an integer multiple of the rack capacity corresponding to that part.

[0057] 3. Meet production needs, meaning that the inventory of parts at the end of each day should ideally cover the production needs for that day. The formula for calculating the inventory of parts is as follows:

[0058] inv i,t-1 +px it -dp it =inv it (2)

[0059] px it dp is an integer variable representing the production quantity of part i at time t. it `inv` is an integer variable representing the production demand for part `i` at time `t`. it Let be an integer variable representing the inventory quantity of part i at the end of time t.

[0060] 4. Meet safety requirements, meaning that the inventory of parts at the end of each day should cover the production needs of the next day as much as possible.

[0061] 5. Meet capacity limits, meaning that the total production time of all parts on the production line each day cannot exceed the available time of the production line on that day.

[0062] 6. Reduce inventory costs, that is, maintain a low level of inventory as much as possible while meeting production needs and safety stock, so as to avoid occupying shelves and increasing warehousing costs.

[0063] Continue to refer to Figure 1 In step 130, based on the production constraint data and the production rule data, a pre-built mixed integer programming model is used to plan the production of various parts within the time frame of the material rack, thereby obtaining the production scheduling data of various parts.

[0064] Mixed Integer Programming (MIP) is a model where the objective function and all constraints are linear. By setting the constraints, it can accurately output the planning results. This application uses MIP to accurately plan the production scheduling data of various parts.

[0065] In one embodiment of this application, the objective function of the mixed integer programming model includes the following formula:

[0066]

[0067] Where pp represents the penalty factor for unmet part production demand, and lp it Let be an integer variable, representing the number of parts i that did not meet production requirements at time t; pe represents the penalty factor for parts not reaching the economic batch size; wtx it For continuous variables, represents the wasted time for spare parts i at time t due to not reaching the economic batch production quantity; pi represents the inventory penalty factor, inv it Let be an integer variable, representing the inventory quantity of part i at the end of time t; pm represents the penalty factor for not meeting the safety stock requirement, ln it This is an integer variable representing the quantity of part i whose inventory at the end of time t does not meet the production demand for the next day.

[0068] In this application, the production scheduling data obtained by formula (3) refers to the predicted data of various parts during the production process within the material rack calculation time. For example, it may include the production data of various parts at the end of each day, and may also include the consumption data of various parts at the end of each day.

[0069] Continue to refer to Figure 1 In step 150, based on the production scheduling data, the number of various material racks put into operation and the number of optimized racks are calculated within the material rack calculation time.

[0070] In one embodiment of this application, based on the production scheduling data, the input and optimized quantities of various material racks within the calculated time period of the material rack are statistically analyzed, which can be done according to... Figure 2 Perform the steps shown:

[0071] See Figure 2This document illustrates a detailed flowchart of the statistical calculation of the input and optimization quantities of various racks within the rack measurement time, according to an embodiment of this application. Specifically, it includes steps 151 to 153:

[0072] Step 151: Based on the production scheduling data, simulate the inventory quantity of various parts and the shelf capacity of various shelves at the end of each day within the shelf calculation time.

[0073] Step 152: Based on the inventory quantity of various parts and the shelf capacity of various shelves, calculate the number of shelves occupied at the end of each day during the shelf calculation period.

[0074] Step 153: Based on the occupied quantity, calculate the number of various material racks put into operation and the number of optimized racks within the material rack calculation time.

[0075] In this embodiment, the number of various racks occupied at the end of each day during the rack measurement period can be calculated using the following formula:

[0076]

[0077] Among them, rack rt An integer variable representing the number of shelves r occupied at the end of time t, RI r Inv represents the set of parts types that the rack r can store. it `cap` is an integer variable representing the inventory quantity of part `i` at the end of time `t`. r This indicates the capacity of the rack r, which is the maximum number of parts of the corresponding type that can be placed on a single rack.

[0078] In this application, it is necessary to distinguish between common racks and special racks. Common racks can store multiple types of parts, while special racks can only store one type of part. This factor needs to be taken into account when calculating the number of parts occupied by each type of rack at the end of each day.

[0079] In this application, by taking into account the difference between common racks and dedicated racks, the rationality of subsequent processing of rack data can be improved.

[0080] In this embodiment, based on the occupied quantity, the input and optimized quantities of various racks are calculated within the rack measurement time, and further, it can be done according to... Figure 3 Perform the steps shown:

[0081] See Figure 3 This document illustrates a detailed flowchart of the statistical calculation of the input and optimization quantities of various racks within the rack measurement time, according to an embodiment of this application. Specifically, it includes steps 1531 to 1533:

[0082] Step 1531: Calculate the peak number of each type of rack occupied during the rack measurement time.

[0083] Step 1532: Obtain the actual number of various material racks.

[0084] Step 1533: Based on the peak number of occupied shelves and the actual number of shelves, determine the number of shelves to be put into operation and the number to be optimized within the shelf calculation period.

[0085] It should be noted that the peak occupancy count refers to the highest number of each type of shelf occupied during the shelf measurement period. For example, for shelf r, the number of shelves occupied from day 1 to day 7 could be 9, 10, 7, 8, 9, 11, and 15 respectively. Therefore, 15 shelves represent the peak occupancy count of shelf r over 7 days. The actual shelf quantity refers to the current actual number of each type of shelf in existence.

[0086] In this embodiment, the quantity of each type of material rack to be put into operation within the time limit for measuring the material rack can be determined by the following formula:

[0087] investment r =max(highest) r -actual r ,0) (5)

[0088] Among them, investment r This indicates the number of items loaded onto the rack (r) within the calculated time period of the rack, highest. r This indicates the peak number of shelves r occupied during the calculated time period of the shelf; actual r This indicates the actual number of racks in the current rack r.

[0089] For example, for rack r, if the peak number of racks occupied during the rack calculation time is 18, the actual number of racks is 15. The number of racks r put into operation during the rack calculation time can be determined by formula (5) as 3.

[0090] In this embodiment, the optimized quantity of various racks within the rack measurement time can be determined by the following formula:

[0091] optimization r =max(actual r -highest r ,0) (6)

[0092] Among them, optimization r This represents the optimized number of racks r within the calculated time period of the rack, highest. rThis indicates the peak number of shelves r occupied during the calculated time period of the shelf; actual r This indicates the actual number of racks in the current rack r.

[0093] For example, for rack r, if the peak number of racks occupied during the rack calculation time is 18, the actual number of racks is 20. The optimized number of racks r during the rack calculation time can be determined by formula (6) as 2.

[0094] In this embodiment, the quantity of racks added can reflect which racks are currently insufficient to meet production needs within the calculated timeframe, and how many additional racks are needed to ensure normal workshop operation in the future. Optimizing the number of racks can also identify which racks have redundant quantities in the future, meaning that such a large number of racks is unnecessary. If necessary, these racks can be modified to meet the shortage requirements after evaluation.

[0095] This application considers actual production needs and rules, and implements an automated workshop scheduling method. Based on the production demand within the rack measurement range, and taking into account factors such as dynamic safety stock, full rack production, economic batch production, and capacity constraints, a mixed integer programming model is established to solve for the optimal scheduling scheme that meets production rules and resource constraints within the rack measurement range. Based on the scheduling results within the rack measurement time range, the inventory quantity of each type of part at the end of each day is calculated. The usage quantity of racks is derived from the inventory quantity, and the peak usage of each type of rack within the measurement time range is statistically analyzed and compared with the actual rack quantity to guide the future rack input quantity and the number of optimizable racks.

[0096] Compared to existing methods that determine rack input based on experience, the rack data processing scheme proposed in this application is more in line with actual production conditions. In addition, by taking into account information on dedicated racks and common racks, the occupancy of racks is calculated based on the inventory information of parts that can be stored on the racks. The racks that can store more types of parts will have a more accurate calculated input quantity than the input quantity determined by existing experience.

[0097] The following describes an embodiment of the apparatus described in this application, which can be used to execute the workshop rack data processing method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the workshop rack data processing method described above in this application.

[0098] Figure 4 A block diagram of a workshop rack data processing apparatus according to an embodiment of this application is shown.

[0099] Reference Figure 4As shown, a workshop rack data processing device 400 according to an embodiment of this application includes: an acquisition unit 401, a planning unit 402, and a statistics unit 403.

[0100] The acquisition unit 401 is used to acquire production constraint data and production rule data of various parts within the material rack calculation time, wherein the material rack is used to store the parts; the planning unit 402 is used to perform production planning for various parts within the material rack calculation time based on the production constraint data and the production rule data, using a pre-constructed mixed integer programming model, to obtain production scheduling data for various parts; the statistics unit 403 is used to calculate the input quantity and optimization quantity of various material racks within the material rack calculation time based on the production scheduling data.

[0101] In some embodiments of this application, based on the foregoing scheme, the objective function of the mixed-integer programming model includes the following formula:

[0102]

[0103] Where pp represents the penalty factor for unmet part production demand, and lp it Let be an integer variable, representing the number of parts i that did not meet production requirements at time t; pe represents the penalty factor for parts not reaching the economic batch size; wtx it For continuous variables, represents the wasted time for spare parts i at time t due to not reaching the economic batch production quantity; pi represents the inventory penalty factor, inv it Let be an integer variable, representing the inventory quantity of part i at the end of time t; pm represents the penalty factor for not meeting the safety stock requirement, ln it This is an integer variable representing the quantity of part i whose inventory at the end of time t does not meet the production demand for the next day.

[0104] In some embodiments of this application, based on the foregoing scheme, the statistics unit 403 is configured to: simulate the inventory quantity of various parts and the shelf capacity of various shelves at the end of each day within the shelf calculation time according to the production scheduling data; calculate the occupied quantity of various shelves at the end of each day within the shelf calculation time according to the inventory quantity of various parts and the shelf capacity of various shelves; and statistically analyze the input quantity and optimization quantity of various shelves within the shelf calculation time according to the occupied quantity.

[0105] In some embodiments of this application, based on the foregoing scheme, the number of various racks occupied at the end of each day during the rack measurement period is calculated using the following formula:

[0106]

[0107] Among them, rackrt An integer variable representing the number of shelves r occupied at the end of time t, RI r Inv represents the set of parts types that the rack r can store. it `cap` is an integer variable representing the inventory quantity of part `i` at the end of time `t`. r This indicates the capacity of the rack r, which is the maximum number of parts of the corresponding type that can be placed on a single rack.

[0108] In some embodiments of this application, based on the foregoing scheme, the statistics unit 403 is further configured to: count the peak number of occupancy of various racks during the rack measurement time; obtain the actual number of racks for various racks; and determine the input and optimization quantities of various racks during the rack measurement time based on the peak number of occupancy and the actual number of racks.

[0109] In some embodiments of this application, based on the foregoing scheme, the quantity of various racks input during the rack measurement time is determined by the following formula:

[0110] investment r =max(highest) r -actual r ,0)

[0111] Among them, investment r This indicates the number of items loaded onto the rack (r) within the calculated time period of the rack, highest. r This indicates the peak number of shelves r occupied during the calculated time period of the shelf; actual r This indicates the actual number of racks in the current rack r.

[0112] In some embodiments of this application, based on the foregoing scheme, the optimized quantity of various racks within the rack measurement time is determined by the following formula:

[0113] optimization r =max(actual r -highest r ,0)

[0114] Among them, optimization r This represents the optimized number of racks r within the calculated time period of the rack, highest. r This indicates the peak number of shelves r occupied during the calculated time period of the shelf; actual r This indicates the actual number of racks in the current rack r.

[0115] Figure 5A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0116] It should be noted that, Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0117] like Figure 5 As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage portion 508 into Random Access Memory (RAM) 503, such as performing the methods described in the above embodiments. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0118] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 505 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0119] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.

[0120] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0122] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0123] In another aspect, this application also provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0124] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0125] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0126] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0127] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0128] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for processing workshop material rack data, characterized in that, The method includes: The production constraint data and production rule data of various parts are obtained within the material rack calculation time, wherein the material rack is used to store the parts; Based on the production constraint data and the production rule data, a pre-built mixed integer programming model is used to plan the production of various parts within the time frame of the material rack, thereby obtaining the production scheduling data of various parts. Based on the production scheduling data, simulate the inventory quantity of various parts and the rack capacity of various racks at the end of each day during the rack measurement time. Based on the inventory quantity of various parts and the shelf capacity of various racks, calculate the number of racks occupied at the end of each day within the shelf calculation period. The peak occupancy rate of various racks was recorded during the time period specified in the data collection report. Obtain the actual quantity of various material racks; Based on the peak occupancy rate and the actual number of racks, determine the number of racks to be put into operation and the number to be optimized for each type of rack within the time frame of the rack measurement. The objective function of the mixed-integer programming model includes the following formula: in, This represents a penalty factor for unmet parts production demands. An integer variable representing a part In time The quantity that did not meet production demand; This indicates a penalty factor for parts not meeting the economic batch production standard. As a continuous variable, representing the parts In time Wasted time on mold preparation due to failure to reach the economic batch production quantity; The penalty factor representing inventory. An integer variable representing a part In time Inventory quantity at the end; The penalty factor indicates that the safety stock requirement has not been met. An integer variable representing a part In time The inventory at the end of the day did not meet the production demand for the next day; The number of various racks occupied at the end of each day during the rack measurement period is calculated using the following formula: in, Integer variable, representing the material rack In time The amount occupied at the end, Indicates material rack A collection that can store different types of parts. An integer variable representing a part In time Inventory quantity at the end of the period Indicates material rack The capacity, that is, the maximum number of corresponding types of parts that can be placed in a single shelf; The quantity of each type of shelf to be put into operation within the time limit for measuring the shelf is determined by the following formula: in, This indicates the number of items placed on the rack r within the calculated time period. This indicates the peak number of shelves r occupied within the time frame measured by the shelf. This indicates the actual number of racks in the current rack r; The optimal quantity of various racks within the calculated time period is determined using the following formula: in, This represents the optimized number of racks r within the calculated time period of the rack. This indicates the peak number of shelves r occupied within the time frame measured by the shelf. This indicates the actual number of racks in the current rack r.

2. A workshop material rack data processing device, characterized in that, The device includes: The acquisition unit is used to acquire production constraint data and production rule data of various parts within the material rack calculation time, wherein the material rack is used to store the parts. The planning unit is used to perform production planning for various parts within the time frame of the material rack based on the production constraint data and the production rule data, using a pre-built mixed integer programming model, to obtain production scheduling data for various parts. The statistics unit is used to simulate, based on the production scheduling data, the inventory quantity of various parts and the shelf capacity of various shelves at the end of each day within the shelf calculation period; calculate the occupied quantity of various shelves at the end of each day within the shelf calculation period based on the inventory quantity of various parts and the shelf capacity of various shelves; count the peak occupied quantity of various shelves within the shelf calculation period; obtain the actual shelf quantity of various shelves; and determine the input quantity and optimization quantity of various shelves within the shelf calculation period based on the peak occupied quantity and the actual shelf quantity. The objective function of the mixed-integer programming model includes the following formula: in, This represents a penalty factor for unmet parts production demands. An integer variable representing a part In time The quantity that did not meet production demand; This indicates a penalty factor for parts not meeting the economic batch production standard. As a continuous variable, representing the parts In time Wasted time on mold preparation due to failure to reach the economic batch production quantity; The penalty factor representing inventory. An integer variable representing a part In time Inventory quantity at the end; The penalty factor indicates that the safety stock requirement has not been met. An integer variable representing a part In time The inventory at the end of the day did not meet the production demand for the next day; The number of various racks occupied at the end of each day during the rack measurement period is calculated using the following formula: in, Integer variable, representing the material rack In time The amount occupied at the end, Indicates material rack A collection that can store different types of parts. An integer variable representing a part In time Inventory quantity at the end of the period Indicates material rack The capacity, that is, the maximum number of corresponding types of parts that can be placed in a single shelf; The quantity of each type of shelf to be put into operation within the time limit for measuring the shelf is determined by the following formula: in, This indicates the number of items placed on the rack r within the calculated time period. This indicates the peak number of shelves r occupied within the time frame measured by the shelf. This indicates the actual number of racks in the current rack r; The optimal quantity of various racks within the calculated time period is determined using the following formula: in, This represents the optimized number of racks r within the calculated time period of the rack. This indicates the peak number of shelves r occupied within the time frame measured by the shelf. This indicates the actual number of racks in the current rack r.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations described in claim 1.

4. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operation performed by the method as described in claim 1.

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