Metal processing production scheduling method and device, computer device and medium

Abstract

The application relates to a metal processing production scheduling method and device, computer equipment and a medium, wherein the method comprises the following steps: obtaining slitting and finishing coil data to be cut and processing order data to be processed; based on the processing order data to be processed, a mapping relationship between product blocks and finishing coils of metal processing is obtained by using a slitting algorithm; production grouping is performed according to the mapping relationship and product process requirements carried in the processing order data to be processed; and a furnace loading and annealing algorithm processing is performed based on the production grouping result to obtain a production scheduling result. The whole scheme can significantly improve the delivery efficiency of metal processing products.

Description

Technical Field

[0001] This application relates to the field of metal processing technology, and in particular to a metal processing production scheduling method, apparatus, computer equipment and medium. Background Technology

[0002] Existing technologies for optimizing metal processing mainly focus on improving product utilization. For example, conventional metal processing optimization techniques improve raw material utilization by using methods such as order batching and sorting, and cutting optimization.

[0003] While these traditional methods can improve raw material utilization to some extent, it's not a primary concern for certain metal products. Take aluminum foil production as an example: the specifications demanded by customers are highly diverse (due to its widespread use in food, beverages, cigarettes, pharmaceuticals, photographic plates, and household goods). The characteristics of multiple varieties and small batches create an enormous range of possible combinations for order-based cutting methods. Therefore, improving the delivery efficiency of metal processing has become a pressing technical challenge. Summary of the Invention

[0004] Therefore, it is necessary to provide a metal processing production scheduling method, apparatus, computer equipment, storage medium, and computer program product that can effectively improve delivery efficiency in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for scheduling metal processing production. The method includes:

[0006] Obtain data on the precision rolled coils to be slit and the orders to be processed;

[0007] Based on the order data to be processed, and using a slitting algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained;

[0008] Production is grouped according to the mapping relationship and the product process requirements carried in the order data to be processed;

[0009] Based on the production grouping results, the furnace loading annealing algorithm is used to obtain the production scheduling results.

[0010] In one embodiment, obtaining the data of the precision rolled coil to be slit and the data of the order to be processed includes:

[0011] Get the preset time length;

[0012] Data on precision rolled coils to be slit and data on orders to be processed are obtained from the production order pool based on the preset time length.

[0013] In one embodiment, obtaining the mapping relationship between metal processing product blocks and precision rolled coils based on the order data to be processed and using a slitting algorithm includes:

[0014] Based on the order data to be processed, obtain the process information corresponding to each product to be processed;

[0015] The identifier of each product to be processed is used as the primary key, and the orders to be processed are split and grouped according to the process information;

[0016] Based on the splitting and grouping results, and using a slitting algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained.

[0017] In one embodiment, obtaining the mapping relationship between metal processing product blocks and precision rolled coils based on the splitting and grouping results and using a slitting algorithm includes:

[0018] Based on the splitting and grouping results, and using the tabu search algorithm and the BL algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained.

[0019] In one embodiment, obtaining the mapping relationship between metal processing product blocks and finished coils based on the splitting and grouping results, and using the tabu search algorithm and the Black-Blood algorithm, includes:

[0020] Based on the splitting and grouping results, obtain the size parameters of the product blocks in different groups;

[0021] Determine the initial corner coordinates of the product block when it is placed;

[0022] Based on the initial corner coordinates and corresponding size parameters, the BL algorithm is used to update the corner coordinates of each product block.

[0023] With the goal of maximizing the effective utilization area of ​​the finished coil, a tabu search algorithm is used to construct the mapping relationship between product blocks and finished coils.

[0024] In one embodiment, the step of constructing the mapping relationship between product blocks and finished coils using a tabu search algorithm with the goal of maximizing the effective utilization area of ​​the finished coil includes:

[0025] With the goal of maximizing the effective utilization area of ​​the finished coil, and constrained by the first and second constraints, a tabu search algorithm is used to construct the mapping relationship between product blocks and finished coils.

[0026] The first constraint is that the product blocks are not allowed to exceed the boundary of the finished coil after placement; the second constraint is that the product blocks are not allowed to overlap.

[0027] In one embodiment, the process of performing furnace loading annealing algorithm based on production grouping results to obtain production scheduling results includes:

[0028] Obtain parameter information for the annealing furnace;

[0029] Based on the production grouping results, obtain the association information of product blocks with the same process requirements. The association information includes product block size information, product block corresponding precision rolled coil information, and product block delivery time information.

[0030] Based on the associated information and the parameter information of the annealing furnace, the production scheduling result is obtained by using an exact solution algorithm.

[0031] Secondly, this application also provides a metal processing production scheduling device. The device includes:

[0032] The data acquisition module is used to acquire data on the precision rolled coils to be slit and data on orders to be processed.

[0033] The mapping construction module is used to obtain the mapping relationship between metal processing product blocks and precision rolled coils based on the order data to be processed and using a slitting algorithm;

[0034] The production grouping module is used to group production based on the mapping relationship and the product process requirements carried in the order data to be processed.

[0035] The production scheduling module is used to process the furnace loading annealing algorithm based on the production grouping results to obtain the production scheduling results.

[0036] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0037] Obtain data on the precision rolled coils to be slit and the orders to be processed;

[0038] Based on the order data to be processed, and using a slitting algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained;

[0039] Production is grouped according to the mapping relationship and the product process requirements carried in the order data to be processed;

[0040] Based on the production grouping results, the furnace loading annealing algorithm is used to obtain the production scheduling results.

[0041] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0042] Obtain data on the precision rolled coils to be slit and the orders to be processed;

[0043] Based on the order data to be processed, and using a slitting algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained;

[0044] Production is grouped according to the mapping relationship and the product process requirements carried in the order data to be processed;

[0045] Based on the production grouping results, the furnace loading annealing algorithm is used to obtain the production scheduling results.

[0046] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0047] Obtain data on the precision rolled coils to be slit and the orders to be processed;

[0048] Based on the order data to be processed, and using a slitting algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained;

[0049] Production is grouped according to the mapping relationship and the product process requirements carried in the order data to be processed;

[0050] Based on the production grouping results, the furnace loading annealing algorithm is used to obtain the production scheduling results.

[0051] The aforementioned metal processing production scheduling method, apparatus, computer equipment, storage medium, and computer program products acquire data on the finished rolled coils to be slit and data on orders to be processed. Based on the order data and using a slitting algorithm, the mapping relationship between metal processing product blocks and finished rolled coils is obtained. Production is grouped according to the mapping relationship and the product process requirements carried in the order data. Based on the production grouping results, a furnace annealing algorithm is applied to obtain the production scheduling result. The entire scheme finds the mapping relationship between product blocks and finished rolled coils through a slitting algorithm. Based on this mapping relationship, the order information corresponding to each part of each finished rolled coil can be clearly obtained. Subsequently, the order information is used to schedule the slitting of this finished rolled coil at an appropriate time. Finally, through production grouping and furnace annealing algorithm processing, a reasonable production scheduling result is obtained, which can significantly improve the delivery efficiency of metal processing products. Attached Figure Description

[0052] Figure 1 This is a diagram illustrating the application environment of a metal processing production scheduling method in one embodiment.

[0053] Figure 2 This is a flowchart illustrating a metal processing production scheduling method in one embodiment;

[0054] Figure 3 This is a flowchart illustrating a metal processing production scheduling method in another embodiment;

[0055] Figure 4 This is a schematic diagram of a sub-process of S300 in one embodiment;

[0056] Figure 5 This is a structural block diagram of a metal processing production scheduling device in one embodiment;

[0057] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] To explain in detail the overall technical concept of the metal processing production scheduling method of this application, the following will first introduce the relevant technologies and the technical concept of this application in detail using aluminum foil production and processing scheduling as an application scenario.

[0060] In actual production and daily life, the specifications requested by aluminum foil customers are more diverse (due to its wide application in food, beverages, cigarettes, pharmaceuticals, photographic plates, household goods, etc.). The characteristics of multiple varieties and small batches create an extremely large combination space for slitting methods to meet demand orders. Traditional technologies have improved raw material utilization by using order batching and categorization, and optimized cutting. However, improving raw material utilization is not a very tangible benefit for light metal processing companies (as these materials are often recyclable). Some companies are more concerned with how to improve delivery efficiency and increase the sales-to-inventory ratio when resources are limited. Therefore, previous technologies lacked research on the upstream and downstream linkages of the slitting and annealing processes in light metal production scheduling. Specifically, after the metal is slitting to determine its length, width, and other properties, it still needs to undergo a "furnace annealing" step to improve its process performance. The furnace loading process imposes specific requirements on the slit products. Only rolls of the same specification and width (commonly referring to a width range, typically 300mm to 1800mm; intervals of 100mm are generally considered a single interval, such as 300mm to 400mm) can be loaded into the same annealing furnace for annealing. Previous technologies focused solely on slitting to improve raw material utilization. However, they failed to address how to load the furnace after slitting to achieve the required furnace fullness, and when to execute the slitting plan to meet order delivery deadlines (and order completion rates), as well as coordination with upstream and downstream production lines. This leads to problems such as an unreasonable inventory structure and delayed deliveries. Slit semi-finished products, unable to be successfully furnace-loaded with other semi-finished products, become inventory buildup during production. Sometimes, to meet the delivery deadline of a particular order, emergency orders and production scheduling occur, severely impacting the production plans and delivery times of other orders.

[0061] This metal processing production scheduling application aims to address the aforementioned shortcomings in the metal processing production process. Through a heuristic algorithm, it finds the "optimal mapping relationship" between orders in the order pool to be scheduled and the finished coils to be slit. This mapping relationship clearly reveals the order information corresponding to each part of each finished coil, allowing for the scheduling of slits at appropriate times using this order information. The mapping relationship changes with changes in the orders in the order pool and the finished coils to be slit. By constructing a mathematical model and using a solver, it is possible to determine: the day each finished coil is slit, the day each slit product is placed in which annealing furnace, and the type of product loaded into each annealing furnace and the start date of annealing. Specifically, an integer programming solver can be used; commercially available solvers include Gurobi and CPLEX; open-source solvers include SCIP and Highs.

[0062] Specifically, the metal processing production scheduling method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed in the cloud or on another network server. When a user operates on terminal 102, terminal 102 responds by generating a metal processing production scheduling request and sending it to server 104. Server 104 responds to the request by obtaining data on the coils to be slit and the order data to be processed. Based on the order data, bill of materials, and process data, the data is split into required product blocks, and then grouped according to the specifications of the required product blocks. Based on the splitting and grouping results, a slitting algorithm is used to obtain the mapping relationship between the required product blocks and the coils to be slit. Based on the obtained mapping relationship between the required product blocks and the coils to be slit, the order delivery time, product process, and other factors, a furnace annealing algorithm is used to obtain the production scheduling result. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0063] In one embodiment, such as Figure 2 As shown, a metal processing production scheduling method is provided, which is applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0064] S100: Obtain data on the precision rolled coils to be slit and the order data to be processed.

[0065] Specifically, data on the pre-slit rolled coils, pending orders, bills of materials, and process data can be obtained from the MES (Manufacturing Execution System) of metal processing. MES is a software system that comprehensively manages the production process of a manufacturing enterprise. It optimizes the entire production process from raw materials to the final product, including management of production planning, production execution, quality control, and equipment maintenance. Through seamless integration with the upstream planning layer and the downstream equipment control layer, MES provides enterprises with a collaborative, integrated, and real-time manufacturing execution platform. Here, the data on the pre-slit rolled coils includes the metal type, size, specifications, and quantity of the rolled coils. Furthermore, the pre-slit rolled coils here encompass multiple parts, including not only the rolled coils that are already prepared and ready for immediate processing, but also the rolled coils that are estimated to be available during the scheduling period—meaning that the preliminary actions for obtaining the estimated rolled coils can all be completed. Pending orders refer to orders that require production scheduling. Specifically, all pending orders are located in the "Pending Production Order Pool." The specific rule is that the business determines a certain time period, and orders within that time period enter the "Pending Production Order Pool." In addition, orders that need to be scheduled for production in the near future based on their latest delivery date, as well as "urgent orders," are also added to the "Pending Production Order Pool."

[0066] S200: Based on the order data to be processed and using a cutting algorithm, obtain the mapping relationship between the metal processing product blocks and the precision rolled coils.

[0067] Customer-provided orders for processing may include quantity requirements for various product specifications. After obtaining the "orders for processing," further segmentation can be performed based on this data. Specifically, by encoding fields such as order, process requirements, and quantity, each "product" required by the customer (in the order for processing) can be obtained. Each product, as a unique primary key, can include its process information and which order it belongs to. After organizing the order data for processing based on the above dimensions, it can be segmented and grouped according to the process rules and process similarity of the "products."

[0068] After splitting and grouping, a slitting algorithm is used to perform the slitting operation based on the results, establishing a mapping relationship between product blocks and finished coils. Specifically, a product block refers to a semi-finished product obtained during metal processing, while the corresponding product is the finished product. The length, width, thickness, and other parameters of the product and product block are basically the same; the product block only needs to undergo furnace annealing to become a finished product. The mapping relationship between product blocks and finished coils clearly defines the correspondence between each product block and the finished coil; in other words, this mapping relationship clearly shows the order information (product block) corresponding to each part of each finished coil. Here, the slitting algorithm obtains the optimal mapping relationship between the finished coil to be slitted and the product blocks. In subsequent production operations, slitting according to this correspondence logic can achieve higher resource utilization, maximizing the use of existing finished coils to produce as many products as possible and improving delivery efficiency.

[0069] S300: Group production based on mapping relationships and product process requirements carried in the order data to be processed.

[0070] Based on the product process requirements carried in the order data to be processed, product blocks that meet the same process requirements are grouped together for calculation, and the mapping relationship obtained in S200 is used to perform overall production grouping. Production planning time can also be considered when performing production grouping, using weeks or days as units, scheduling the production of product blocks with the same process requirements within fixed time units. S400: Based on the production grouping results, the furnace loading annealing algorithm is processed to obtain the production scheduling results.

[0071] Based on the production grouping results, data belonging to the same group are input into the subsequent furnace loading annealing algorithm for processing, yielding the final production scheduling result. Furthermore, the furnace loading annealing algorithm can be implemented using an exact solution algorithm from operations research optimization algorithms. Specifically, a mathematical model can be constructed based on business requirements, and the Gurobi solver can be used for solving.

[0072] The aforementioned metal processing production scheduling method acquires data on finished coils to be slit and order data to be processed. Based on the order data and using a slitting algorithm, it obtains the mapping relationship between metal processing product blocks and finished coils. Production is grouped according to the mapping relationship and the product process requirements carried in the order data. Based on the production grouping results, a furnace annealing algorithm is applied to obtain the production scheduling result. The entire scheme finds the mapping relationship between product blocks and finished coils through a slitting algorithm. Based on this mapping relationship, the order information corresponding to each part of each finished coil can be clearly obtained. Subsequently, the order information is used to schedule the slitting of this finished coil at an appropriate time. Finally, through production grouping and furnace annealing algorithms, a reasonable production scheduling result is obtained, which can significantly improve the delivery efficiency of metal processing products.

[0073] In one embodiment, obtaining the data of the precision rolled coil to be slit and the data of the order to be processed includes:

[0074] Obtain the preset time length; based on the preset time length, obtain the data of the precision rolled coils to be slit and the data of the orders to be processed from the order pool to be scheduled.

[0075] The preset time length can be set according to actual needs, such as 3 days, 7 days, etc. Data on the coils to be slit and the orders to be processed are retrieved from the pending production order pool according to a fixed time length, so as to conduct overall, macro-level planning of subsequent production scheduling.

[0076] like Figure 3 As shown, in one embodiment, S200 includes:

[0077] S220: Based on the order data to be processed, obtain the process information corresponding to each product to be processed.

[0078] S240: Use the identifier of each product to be processed as the primary key, and split and group the orders to be processed according to the process information.

[0079] Specifically, the splitting criteria include parameters such as the required product thickness, length, and product category (aseptic package, soft package, etc.). After the orders are split, they are grouped and aggregated, and orders in the same group are produced together.

[0080] S260: Based on the splitting and grouping results, and using a slitting algorithm, obtain the mapping relationship between metal processing product blocks and precision rolled coils.

[0081] The technological information for each different product to be processed varies. Here, based on the order data, we obtain the technological information for each product. Generally, we need to encode fields such as "process requirements" and "quantity" to obtain each "product" required by the customer. Each product, as a unique primary key, can include its technological information and which order it belongs to. After this segmentation, we group the "products" according to their technological rules and similarity. After segmentation and grouping, we use a slicing algorithm to obtain the mapping relationship between the metal processing product blocks and the finished coils.

[0082] In one embodiment, based on the splitting and grouping results and using a slitting algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained, including:

[0083] Based on the splitting and grouping results, and using the tabu search algorithm and the BL algorithm, the mapping relationship between metal processing product blocks and precision rolled coils is obtained.

[0084] Tabu search is a heuristic search algorithm that avoids redundant searches by memorizing already searched nodes and paths, thus finding the global optimum more efficiently. The core idea of ​​tabu search is to consciously avoid (but not completely isolate) some locally optimal solutions found, thereby expanding the search range. Bottom Left (BL) algorithm is a common two-dimensional bin packing algorithm used to place a group of rectangular objects into a rectangular container as efficiently as possible. The basic idea of ​​BL is to start from the bottom left corner and place the rectangular objects into the container one by one. In this embodiment, tabu search and BL algorithms are used to identify the finished coil corresponding to each product block and construct the mapping relationship between product blocks and finished coils.

[0085] like Figure 4 As shown, in one embodiment, S260 includes:

[0086] S261: Based on the splitting and grouping results, obtain the size parameters of the product blocks in different groups.

[0087] A series of pending orders, after being broken down into product blocks, correspond to a series of finished rolled coils to be slit. When considering the order of using the finished rolled coils, a priority rule is set: "incomplete" finished rolled coils are used first; these are typically coils that were not fully used in previous slits. In practical applications, based on the already split and grouped results, the dimensional parameters of the product blocks in different groups are identified to further calculate the appropriate mapping relationship between the product block and the finished rolled coil. These dimensional parameters mainly include the length and width dimensions of the product block itself.

[0088] S262: Determine the initial corner coordinates of the product block when it is placed.

[0089] For each finished coil, consider placing the most suitable "product block". After placing a product block each time, two initial "corner points" will be generated, which are generally the upper left and lower right corner points of the rectangular product block. Select one of these two initial corner points as the initial corner point, and initialize the corner point coordinates as (0, 0).

[0090] S263: Based on the initial corner coordinates and corresponding size parameters, the BL algorithm is used to update the corner coordinates of each product block.

[0091] The Blue-Browser (BL) algorithm prioritizes placing product blocks on the leftmost and bottommost corners. The corner update logic is as follows: after placing a product block on a corner, the used corner is removed from the corner set. Newly acquired corners are added to the corner set, and after each round of operation, the corners are sorted according to the BL algorithm logic. Specifically, in the second quadrant of the two-dimensional plane coordinate axis, there is only one corner (0, 0). When the algorithm starts, it plans to place a product block. At this point, there will be two new corners in the coordinate system, and the already placed corners are deleted from the corner list. This process is repeated to continuously poll and determine the corners corresponding to each product block, ultimately determining the placement position of each product block in the coordinate system.

[0092] S264: With the goal of maximizing the effective utilization area of ​​the finished coil, a tabu search algorithm is used to construct the mapping relationship between product blocks and finished coils.

[0093] After obtaining the corner points corresponding to each product block in S263, a tabu search algorithm is used to construct the mapping relationship between the product blocks and the finishing coil with the goal of maximizing the effective utilization area of ​​the finishing coil, so as to maximize the utilization rate of the finishing coil in actual production.

[0094] Furthermore, when performing S264 processing, constraints can be added to consider actual production applications. Specifically, a first constraint and a second constraint can be added. The first constraint is that the product block must not exceed the boundary of the finished coil after placement; the second constraint is that product blocks must not overlap. Specifically, the evaluation of whether placing a product block at a corner point is appropriate firstly determines whether it will exceed the length and width range of the finished coil; secondly, it determines whether placing it at the corner point will cause product blocks to overlap; finally, it assesses the similarity between the usable area of ​​the corner point from the boundary and the area of ​​the product block itself, prioritizing the placement of product blocks with higher similarity.

[0095] For the slitting algorithm, the overall goal is to maximize the effective utilization area of ​​the finished coil. W and L represent the width and length of the finished coil, while w and l represent the width and length of each product block. The decision variable is the placement corner point of the product block. In the tabu search implementation, the product blocks already placed for each finished coil are recorded. If the same placement logic appears in subsequent domain searches, it is tabulied based on its existence in the tabu list to avoid duplicate searches. Simultaneously, the tabu list is updated according to the search progress; after setting the maintenance length of the tabu list, tabuli objects that entered the tabu list earlier are removed. Furthermore, the tabu-breaking criterion ensures that newly discovered optimal solutions are not missed during the domain search. The tabu search algorithm continuously determines the correspondence between different product blocks and different finished coils, ultimately constructing a mapping relationship between product blocks and finished coils.

[0096] like Figure 3 As shown, in one embodiment, S400 includes:

[0097] S420: Obtain parameter information for the annealing furnace;

[0098] S440: Based on the production grouping results, obtain the association information of product blocks with the same process requirements. The association information includes product block size information, product block corresponding precision rolled coil information, and product block delivery time information.

[0099] S460: Based on the correlation information and the parameter information of the annealing furnace, the production scheduling result is obtained by using an exact solution algorithm.

[0100] In this embodiment, the furnace loading annealing algorithm is implemented using an exact solution algorithm from operations research optimization algorithms. During the solution calculation, multiple dimensions of data are comprehensively considered, including product block size information, the corresponding finished coil information, and product block delivery time information. Furthermore, a mathematical model can be constructed based on business requirements and solved using a Gurobi solver. Specifically, the exact solution algorithm here can be an exact solution algorithm based on a mixed integer programming model.

[0101] The following section will detail the formulas and related content involved in the specific modeling of the furnace charging annealing algorithm.

[0102] I. Decision Variables

[0103] X r，b ∈{0,1}: Whether the b-th finely rolled coil is slit on day r;

[0104] Y r，k，q ∈{0,1}: Whether the k-th product is placed in the annealing furnace on the r-th day after it is cut (and it is assumed that the furnace is started on the same day);

[0105] Z r，q，l ∈{0,1}: Whether the q-th annealing furnace used to hold product blocks of length l is ignited on day r;

[0106] The earliest / latest production time for the product corresponding to the 0th order;

[0107] II. Objective Function

[0108] Annealing and baking dates:

[0109]

[0110] Production cycle for the same order:

[0111]

[0112] Introduction satisfy:

[0113]

[0114] Where M is a sufficiently large number, then minimize T o It can be represented as:

[0115]

[0116] Complete set rate: Complete set rate for the same order:

[0117]

[0118] Combined objective function:

[0119]

[0120] Where w1, w2 > 0 are weighting coefficients, i.e.:

[0121]

[0122] III. Constraints

[0123] Annealing furnace constraint: Products can only be placed inside the furnace on day r when it becomes available (assuming all products are placed inside on the day the furnace is turned on).

[0124]

[0125] Furnace loading rate on the day of slitting and for the next 3 days (to avoid slitting out large quantities of products that cannot be put into the annealing furnace in time):

[0126]

[0127] Annealing furnace loading specifications: The furnace can only use one width specification; and if a product of length l is loaded and fired on a certain day, then in the future t l No more goods can be loaded into it:

[0128]

[0129] Slitting constraints: Maximum of 21 rolls per day; each roll can only be slit once.

[0130]

[0131]

[0132] The product can only be placed in one annealing furnace and can only be placed once:

[0133]

[0134] Annealing furnace loading quantity constraint: The loading quantity on day r cannot exceed the maximum loading limit.

[0135]

[0136] Full furnace ratio constraint (ignition must be started when the annealing furnace is more than 95% full):

[0137]

[0138] Reduce annealing furnace downtime: Limit maximum downtime to 2 days.

[0139]

[0140]

[0141] For a given list of orders mapped to a fine-rolled coil, in the... The cutting must be completed by the next day:

[0142]

[0143] Alternatively, for a block of products contained in an order within a list, in the... The pieces must be cut (as guaranteed by constraint 8) and placed in the annealing furnace before the specified time.

[0144]

[0145] Products that can be placed into the annealing furnace must be cut from the finishing coils in advance:

[0146]

[0147] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0148] Based on the same inventive concept, this application also provides a metal processing production scheduling apparatus for implementing the metal processing production scheduling method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the metal processing production scheduling apparatus provided below can be found in the limitations of the metal processing production scheduling method described above, and will not be repeated here.

[0149] In one embodiment, such as Figure 5 As shown, a metal processing production scheduling device is provided, comprising:

[0150] Data acquisition module 100 is used to acquire data on the precision rolled coils to be slit and data on orders to be processed;

[0151] The mapping construction module 200 is used to obtain the mapping relationship between metal processing product blocks and precision rolled coils based on the order data to be processed and using a cutting algorithm;

[0152] The production grouping module 300 is used to group production based on mapping relationships and product process requirements carried in the order data to be processed.

[0153] The production scheduling module 400 is used to perform furnace loading annealing algorithm processing based on the production grouping results to obtain the production scheduling results.

[0154] In one embodiment, the data acquisition module 100 is further configured to acquire a preset time length; and acquire data on the precision rolled coils to be slit and data on the orders to be processed from the production order pool based on the preset time length.

[0155] In one embodiment, the splitting module 200 is further configured to obtain the process information corresponding to each product to be processed based on the order data to be processed; use the identifier of each product to be processed as the primary key and split and group the orders to be processed according to the process information; and obtain the mapping relationship between the metal processing product blocks and the precision rolled coils based on the splitting and grouping results and using a slitting algorithm.

[0156] In one embodiment, the mapping construction module 300 is further configured to obtain the mapping relationship between metal processing product blocks and precision rolled coils based on the splitting and grouping results and on the tabu search algorithm and the BL algorithm.

[0157] In one embodiment, the mapping construction module 300 is further configured to obtain the size parameters of product blocks in different groups based on the splitting and grouping results; determine the initial corner coordinates of the product blocks when they are placed; update the corner coordinates of each product block using the BL algorithm based on the initial corner coordinates and the corresponding size parameters; and construct the mapping relationship between the product blocks and the finished coils using the tabu search algorithm with the goal of maximizing the effective utilization area of ​​the finished coils.

[0158] In one embodiment, the mapping construction module 300 is further configured to construct the mapping relationship between product blocks and the finished roll with the goal of maximizing the effective utilization area of ​​the finished roll and with the first constraint and the second constraint as constraints, using a tabu search algorithm; the first constraint is that the product blocks are not allowed to exceed the boundary of the finished roll after placement; the second constraint is that the product blocks are not allowed to overlap.

[0159] In one embodiment, the production scheduling module 500 is also used to obtain parameter information of the annealing furnace; according to the production grouping results, obtain the association information of product blocks with the same process requirements; based on the association information and the parameter information of the annealing furnace, use an exact solution algorithm to solve for the production scheduling results; the association information includes product block size information, product block corresponding fine rolled coil information, and product block delivery time information.

[0160] Each module in the aforementioned metal processing production scheduling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0161] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores preset data or historical data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a metal processing production scheduling method.

[0162] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0163] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the metal processing production scheduling method described above.

[0164] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the metal processing production scheduling method described above.

[0165] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the metal processing production scheduling method described above.

[0166] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0167] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0168] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0169] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A metal processing production scheduling method, characterized in that, The method is used in the production of various types of light metals in small batches, and includes: Acquire data on the precision rolled coils to be slit and the orders to be processed; Based on the order data to be processed, and using a slicing algorithm, the mapping relationship between metal processing product blocks and finished coils is obtained; this includes: obtaining the process information corresponding to each product to be processed based on the order data; using the identifier of each product to be processed as the primary key, and splitting and grouping the order to be processed according to the process information; obtaining the size parameters of the product blocks in different groups based on the splitting and grouping results; determining the initial corner coordinates of the product blocks when they are placed; updating the corner coordinates of each product block using the BL algorithm based on the initial corner coordinates and the corresponding size parameters; and constructing the mapping relationship between product blocks and finished coils using a tabu search algorithm with the goal of maximizing the effective utilization area of ​​the finished coils. Production is grouped according to the mapping relationship and the product process requirements carried in the order data to be processed; Based on the production grouping results, a furnace loading annealing algorithm is applied to obtain the production scheduling results, including: Obtain parameter information for the annealing furnace; Based on the production grouping results, obtain the association information of product blocks with the same process requirements. The association information includes product block size information, product block corresponding precision rolled coil information, and product block delivery time information. Based on the associated information and the parameter information of the annealing furnace, the production scheduling result is obtained by using an exact solution algorithm.

2. The method according to claim 1, characterized in that, The acquisition of data on the precision rolled coils to be slit and data on orders to be processed includes: Get the preset time length; Data on precision rolled coils to be slit and data on orders to be processed are obtained from the production order pool based on the preset time length.

3. The method according to claim 2, characterized in that, The method of constructing the mapping relationship between product blocks and finished coils using a tabu search algorithm, with the goal of maximizing the effective utilization area of ​​the finished coil, includes: With the goal of maximizing the effective utilization area of ​​the finished coil, and constrained by the first and second constraints, a tabu search algorithm is used to construct the mapping relationship between product blocks and finished coils. The first constraint is that the product blocks are not allowed to exceed the boundary of the finished coil after placement; the second constraint is that the product blocks are not allowed to overlap.

4. A metal processing production scheduling device, characterized in that, The apparatus for performing the steps of the method according to any one of claims 1 to 3, the apparatus comprising: The data acquisition module is used to acquire data on the precision rolled coils to be slit and data on orders to be processed. The mapping construction module is used to obtain the mapping relationship between metal processing product blocks and precision rolled coils based on the order data to be processed and using a slitting algorithm; The production grouping module is used to group production based on the mapping relationship and the product process requirements carried in the order data to be processed. The production scheduling module is used to process the furnace loading annealing algorithm based on the production grouping results to obtain the production scheduling results.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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