Model construction method and device for solving batch planning problem, cluster, storage medium and program product

Abstract

The invention discloses a model construction method and device for solving a batch planning problem, a cluster, a storage medium and a program product, relates to the technical field of computers, and improves the modeling efficiency of the batch planning problem. The method comprises the following steps: acquiring production characteristics of a production environment; the production characteristics comprise resource characteristics used for producing the target product in the production environment, production process characteristics and production demand characteristics of the target product; according to the production characteristics, the problem type of a batch planning problem corresponding to the production environment is determined, and the solution of the batch planning problem is used for determining a production plan for batch production of the target product in the production environment; obtaining a target function and a constraint condition corresponding to the problem type; and according to the objective function and the constraint condition, constructing a mathematical model corresponding to the batch planning problem.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a model building method, apparatus, cluster, storage medium, and program product for solving batch planning problems. Background Technology

[0002] Almost every manufacturing company typically faces issues such as managing production scheduling and inventory, including the capacitated lot sizing problem (CLSP). CLSP refers to the problem of how to reduce costs while meeting product demand under the constraint of limited production resources through the development of a reasonable production plan.

[0003] Because solving CLSP involves many factors, such as the usage rules of production equipment, the conversion relationships between different materials, production process routes, human resource requirements, etc., and while meeting the production needs of the product, it is also necessary to consider how to minimize production costs, start-up costs, inventory holding costs, and other objectives, it is often necessary to work with industry experts, operations research experts, etc. to build a customized mathematical model for a specific production environment to express CLSP in that production environment. However, this method of building mathematical models that is highly dependent on manual labor is inefficient. Summary of the Invention

[0004] This application provides a model building method, apparatus, cluster, storage medium, and program product for solving batch planning problems. It realizes the automatic construction of batch planning problem models on computing devices, thereby significantly improving modeling efficiency and significantly reducing modeling costs. Moreover, the computing device first determines the problem type of batch planning problem based on the production characteristics of the production environment it obtains, and then obtains the corresponding objective function and constraints to build the model based on the problem type. Therefore, it can be applied to different production environments, has high versatility, and can quickly adapt and remodel after changes in production characteristics.

[0005] Firstly, this application provides a model construction method for solving batch planning problems. The method includes: a computing device acquiring production characteristics of a production environment; these production characteristics include resource characteristics, production process characteristics, and production demand characteristics of the target product used in the production environment; the computing device determining the problem type of the batch planning problem corresponding to the production environment based on the production characteristics, and the solution to the batch planning problem being used to determine a production plan for batch production of the target product in the production environment; the computing device acquiring the objective function and constraints corresponding to the problem type; and the computing device constructing a mathematical model corresponding to the batch planning problem based on the objective function and constraints.

[0006] Understandably, computing devices can automatically determine the problem type of batch planning based on the production characteristics of the production environment, and then automatically construct the corresponding mathematical model based on the objective function and constraints of the problem type, replacing the traditional manual modeling method. This significantly improves the efficiency of model construction for solving batch planning problems, and can flexibly and efficiently adapt to different production characteristics, thus having high versatility.

[0007] In one possible implementation, the computing device determines the problem type of the batch planning problem corresponding to the production environment based on the production characteristics, including: the computing device obtaining the matching degree between the production characteristics and the production characteristics corresponding to different types of batch production planning problems; and the computing device determining the problem type of the batch planning problem corresponding to the production environment based on the matching degree.

[0008] Understandably, by matching the production characteristics corresponding to different types of batch production planning problems obtained from the acquisition of production characteristics, and then determining the problem type based on the matching degree, the computing device can improve the accuracy of the problem type determined by the computing device.

[0009] In one possible implementation, after the computing device constructs a mathematical model corresponding to the batch planning problem based on the objective function and constraints, the method further includes: the computing device obtaining a first set of constraints, which includes conflicting constraints; the computing device determining key production features based on the conflicting constraints; and the computing device obtaining adjusted production features, where there are no conflicting constraints in the constraints corresponding to the adjusted production features.

[0010] Understandably, computing devices can identify key production features based on conflicting constraints, enabling them to detect model feasibility, saving time spent solving infeasible models, and improving efficiency.

[0011] In one possible implementation, before obtaining the adjusted production characteristics, the method further includes: the computing device displaying adjustment information to instruct the user to adjust key production characteristics.

[0012] Understandably, computing devices can guide users to make adjustments by displaying adjustment information, helping business personnel to adjust key production characteristics in a timely manner to eliminate conflicting constraints and help generate feasible production plans.

[0013] In one possible implementation, after the computing device constructs a mathematical model corresponding to the batch planning problem based on the objective function and constraints, the method further includes: the computing device obtaining a feasible solution to the mathematical model; and the computing device determining a production plan for batch production of the target product in a production environment based on the feasible solution.

[0014] It is understandable that a feasible solution is a solution that satisfies all the constraints in the model. Therefore, the computing device can generate a truly feasible production plan for mass production of the target product based on the feasible solution, achieving an optimization effect based on a global perspective.

[0015] In one possible implementation, the computing device obtains a feasible solution to the mathematical model, including: the computing device obtains a first solution to the mathematical model; the first solution is a solution obtained by solving the mathematical model using an initial solution algorithm; if the goodness of the first solution meets the target goodness threshold, the computing device determines the first solution as a feasible solution.

[0016] Understandably, computing devices can quickly obtain the first solution of a mathematical model through an initial solution algorithm, shortening the solution time and improving the solution efficiency. Furthermore, the computing device determines the first solution that meets the target goodness threshold as a feasible solution, which helps to generate a production plan that meets the user's requirements in the future.

[0017] In one possible implementation, if the goodness of the first solution does not meet the target goodness threshold, the computing device obtains a feasible solution of the mathematical model, including: the computing device obtains a second solution of the mathematical model; the second solution is a solution obtained by solving the mathematical model using a mathematical programming algorithm; if the goodness of the second solution meets the target goodness threshold, the computing device determines the second solution as a feasible solution.

[0018] It is understandable that when the goodness of the first solution does not meet the target goodness threshold, the computing device obtains the second solution of the mathematical model through a mathematical programming algorithm. Moreover, compared with the initial solution algorithm, the mathematical programming algorithm can often obtain a second solution with a higher goodness, thus helping the computing device to obtain a feasible solution that meets the target goodness threshold.

[0019] In one possible implementation, if the goodness of the second solution does not meet the target goodness threshold, the computing device obtains a feasible solution of the mathematical model, including: the computing device obtains a third solution of the mathematical model; the third solution is a solution obtained by solving the mathematical model using an iterative algorithm based on the second solution; if the goodness of the third solution meets the target goodness threshold, the computing device determines the third solution as a feasible solution.

[0020] Understandably, if the goodness of the second solution does not meet the target goodness threshold, the computing device uses an iterative algorithm to continue calculating a feasible solution with a higher goodness based on the second solution, so that the computing device can obtain a third solution that meets the target goodness threshold and determine it as a feasible solution.

[0021] Secondly, this application provides a model building apparatus, which includes a module that performs the method described in the first aspect or any implementation thereof.

[0022] In one possible implementation, embodiments of this application can divide the model building apparatus into functional modules according to the method provided in the first aspect above. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. For instance, embodiments of this application can divide the model building apparatus into a first acquisition module, a determination module, a second acquisition module, and a construction module, etc., according to function. The descriptions of the possible technical solutions and beneficial effects of the various functional modules described above can be found in the technical solutions provided in the first aspect above or its corresponding possible implementations, and will not be repeated here.

[0023] Thirdly, embodiments of this application provide a computing device comprising a processor and a memory, the processor being coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computing device to implement the model building method for solving batch planning problems as described above.

[0024] Fourthly, embodiments of this application provide a computing device cluster, which includes at least one computing device, each computing device including a processor and a memory, the processor being coupled to the memory; the processor of the at least one computing device is used to execute computer instructions stored in the memory of the at least one computing device, so that the computing device cluster executes the model building method for solving batch planning problems provided in the various optional implementations of the first aspect above.

[0025] Fifthly, embodiments of this application provide a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to implement the model building method for solving batch planning problems as described above.

[0026] Sixthly, embodiments of this application provide a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computing device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computing device to perform the model building method for solving batch planning problems provided in the various optional implementations of the first aspect described above.

[0027] For a detailed description of aspects two through six and their various implementations in this application, please refer to the detailed description in aspect one and its various implementations; and for a detailed description of the beneficial effects of aspects two through six and their various implementations, please refer to the beneficial effect analysis in aspect one and its various implementations, which will not be repeated here.

[0028] These or other aspects of this application will become more readily apparent in the following description. Attached Figure Description

[0029] Figure 1 A schematic diagram illustrating an application scenario provided in an embodiment of this application;

[0030] Figure 2 A flowchart illustrating a related technology provided in this application embodiment;

[0031] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application;

[0032] Figure 4 A flowchart illustrating a model construction method for solving batch planning problems, provided in an embodiment of this application;

[0033] Figure 5 for Figure 4 The illustrated embodiment is a schematic diagram showing the correspondence between a problem type and a production characteristic;

[0034] Figure 6 for Figure 4 The illustrated embodiment is a schematic diagram showing the correspondence between a problem type and a target / constraint.

[0035] Figure 7 for Figure 4 The illustrated embodiment includes a flowchart of a feasibility analysis.

[0036] Figure 8 for Figure 4 The illustrated embodiment involves a flowchart of a mathematical model solution process;

[0037] Figure 9 A schematic diagram of a sheet metal processing production line provided in this application embodiment;

[0038] Figure 10 A flowchart of model construction in a sheet metal forming process scenario is provided as an embodiment of this application;

[0039] Figure 11 This is a schematic diagram of the structure of the model building apparatus provided in the embodiments of this application;

[0040] Figure 12 A schematic diagram of a computing device provided in an embodiment of this application;

[0041] Figure 13 A schematic diagram of a computing device cluster provided in an embodiment of this application;

[0042] Figure 14 This is a schematic diagram illustrating a connection method between computing device clusters provided in an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0044] In this article, "multiple" 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.

[0045] Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0046] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" are not necessarily different. Meanwhile, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

[0047] First, the application scenarios of the embodiments of this application will be introduced by way of example.

[0048] like Figure 1 As shown, Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. Figure 1The Advanced Planning and Scheduling (APS) system shown is based on operations research theory and aims to use advanced algorithms and computer systems to plan and schedule projects based on data from... Figure 1 The customer business systems shown, such as Manufacturing Execution System (MES), Customer Relationship Management (CRM) system, and Enterprise Resource Planning (ERP) system, acquire production characteristics (such as orders, production processes, etc.) to optimize and solve a series of problems in the production and manufacturing process, including planning, scheduling, capacity, personnel, and materials, to achieve the goal of intelligent and lean manufacturing in the factory. It mainly consists of sales and operations planning (S&OP), material requirement planning (MRP), capacity lot sizing problem (CLSP), and production scheduling.

[0049] CLSP is a common optimization problem in production and inventory management. It typically takes data such as the enterprise's resource plan, capacity requirement plan, material requirement plan, and product characteristics as inputs. Under the conditions of meeting the actual production environment's equipment usage rules, process constraints, resource allocation, and other actual production rules, and with the goal of improving production efficiency, reducing production costs, reducing inventory backlog, and improving customer satisfaction, it comprehensively decides on the production time, production volume, and selection of production machines to generate a reasonable batch production plan.

[0050] Traditional solutions to batch planning problems often rely on simple rule-based logic. These empirical rule-based algorithms are usually short-sighted and cannot achieve globally optimal decision-making, resulting in low production planning quality, which in turn affects production efficiency, material requirements, and other aspects.

[0051] like Figure 2 As shown, Figure 2A flowchart of a related technology provided in this application embodiment includes: S110, computing resource requirements; S120, identifying potential bottleneck resources; S130, splitting the production sequence; S140, allocating bottleneck resources according to node occupancy rate; and S150, generating a production plan. In this scheme, determining the production plan mainly involves splitting the production sequence into multiple nodes based on a given production situation, generating corresponding production nodes and their time series, allocating bottleneck resources in the production sequence according to node occupancy rate, and using a Poisson algorithm to generate their distribution, thereby verifying the production situation, updating the production plan, and / or executing it.

[0052] However, this technical solution generates production plans in a relatively simple way. Over-focusing on bottleneck resources may cause other resources to become new bottlenecks in certain situations, resulting in inaccurate and suboptimal production plans. Moreover, when multiple bottleneck resources exist, the allocation method based on node occupancy may lead to shortages or delays of various products. Furthermore, resource imbalance can easily lead to idle non-bottleneck resources, resulting in an unbalanced resource allocation.

[0053] To obtain better production plans, the industry has proposed a method using solvers to solve Common Processes of Programming (CLSP). A solver is a tool used to find feasible and optimal solutions in complex business scenarios with massive amounts of data and numerous constraints. It typically incorporates various mathematical programming algorithms. The corresponding solution usually requires first expressing the CLSP as a mathematical model, such as a mixed integer programming (MIP) model, based on the customer's business orders, production processes, production resources, etc., and then solving it using a solver to obtain the corresponding production plan.

[0054] However, due to the diverse production environments and varying production characteristics in the real world, different types of CLSP (Catch Planning and Scheduling) problems arise. For example, by classifying problem characteristics, batch planning problems can be qualitatively defined and categorized based on different dimensions: 1) Based on product dimension: Consider whether the product quantity is a single product or multiple products, whether the product structure is single-level or multi-level, and whether product demand allows for delays, has time window constraints, or has a substitution relationship. 2) Based on resource dimension: Whether the production stage is single-stage or multi-stage, whether the number of machines is single-machine or multi-machine, whether exceeding capacity constraints is allowed, whether inventory constraints need to be balanced or have a flexible range, whether the batch size is an integer or continuous, and whether equipment and products are compatible. 3) Based on production process: Whether mold change allows across periods or consumes multiple time periods, whether mold change costs are sequentially related or sequentially independent or satisfy the triangle inequality relationship, and whether mold change time is a fixed time unit or a periodic range. 4) Based on planning cycle: Consider whether it is an hourly period where at most one mold change and product production is carried out, or a long period where any number of mold changes can be carried out.

[0055] These complex factors mean that solutions using solvers often require the collaboration of professional operations research experts and industry experts to build corresponding mathematical models based on customer production information, order information, and so on. This case-by-case modeling method is highly dependent on manual labor, which is inefficient. Moreover, the accuracy of manually built models is highly correlated with the experience of experts. In other words, due to the negligence of experts, the final production plan may not be accurate or optimal.

[0056] In view of this, this application proposes a model building method for solving batch planning problems that can be quickly and flexibly adapted to different production environments. The method uses computing equipment to determine the problem type of CLSP based on the production characteristics of the production environment, and then obtains the corresponding objective function and constraints based on the problem type of CLSP, thereby achieving efficient and accurate automated modeling, significantly reducing the cost of modeling. Moreover, through feasibility analysis and a variety of algorithms with their own advantages, the feasible solution of the model is finally obtained, ensuring the efficiency and excellence of determining the production plan.

[0057] In some feasible embodiments, the method includes: a computing device acquiring production characteristics of the production environment; production characteristics including resource characteristics, production process characteristics, and production demand characteristics of the target product used in the production environment; the computing device determining the problem type of the batch planning problem corresponding to the production environment based on the production characteristics, and the solution to the batch planning problem being used to determine the production plan for batch production of the target product in the production environment; the computing device acquiring the objective function and constraints corresponding to the problem type; and the computing device constructing a mathematical model corresponding to the batch planning problem based on the objective function and constraints. Since the computing device first determines the type of the batch planning problem based on the production characteristics of the production environment, and then acquires the corresponding objective function and constraints based on the type of the batch planning problem, automated modeling of the batch planning problem is achieved, significantly improving modeling efficiency. Furthermore, the model construction method for solving the batch planning problem provided in this application can complete automated modeling for the production characteristics of various production environments. In other words, this application embodiment can quickly and flexibly adapt to different production environments and is a highly versatile model construction method.

[0058] Secondly, the system architecture of the embodiments of this application will be described by way of example.

[0059] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Figure 3The computing device 1000 shown includes an Advanced Planning and Scheduling (APS) module 1100 and an interface module 1200, wherein the Advanced Planning and Scheduling module 1100 includes a configuration management module 1110 and a modeling module 1120.

[0060] Among them, the advanced planning and scheduling module 1100, through the collaboration of multiple functional modules, can realize the function of building a model for solving batch planning problems based on the production characteristics of the production environment. Specifically, the configuration management module 1110 can be used to obtain the forward period characteristics and granularity characteristics of the (user-configured) production plan, whether it is allowed to exceed the shift capacity, production process characteristics, and other production characteristics; the modeling module 1120 can be used to determine the problem type of batch planning problem and build the corresponding mathematical model.

[0061] Optionally, the advanced planning and scheduling module 1100 also includes a feasibility analysis module 1130 and a solution module 1140.

[0062] The feasibility analysis module 1130 can be used to determine the feasibility of the mathematical model, and if the mathematical model is determined to be infeasible, it can identify the key factors that make it infeasible and provide relevant adjustment suggestions; the solution module 1140 can be used to solve the mathematical model and obtain the corresponding production plan.

[0063] Figure 3 The advanced planning and scheduling module 1100 shown can transmit and interact with other modules and external devices through the interface module 1200. For example, in some feasible embodiments, the computing device 1000 also includes a customer business system 2000. The advanced planning and scheduling module 1100 can obtain necessary data (such as orders, products, resources, production processes, etc.) from the customer business system 2000 through the interface module 1200 to implement the model building method for solving batch planning problems provided in the embodiments of this application.

[0064] Optionally, Figure 3 The computing device 1000 shown can be a terminal device, such as a computer, mobile terminal, tablet computer, laptop computer, etc., or it can be an edge device (e.g., a box carrying a chip with processing capabilities). Alternatively, Figure 3 The computing device 1000 shown can also be a server device or a cloud server.

[0065] Optionally, the customer business system 2000 may include an ERP system, a MES system, a bill of materials (BOM) system, a logistics execution system (LES), a supplier relationship management (SRM) system, a warehouse management system (WMS), and so on.

[0066] In some feasible embodiments, the customer business system 2000 may also run on other computing devices. The computing device 1000 can communicate with the customer business system 2000 running on other computing devices through the interface module 1200, so that the advanced planning and scheduling module 1100 can obtain the necessary information to implement the model building method for solving batch planning problems provided in the embodiments of this application.

[0067] It needs to be explained that, Figure 3 This is merely a schematic diagram of the structure of an exemplary computing device provided in an embodiment of this application. Figure 3 The positional relationships between the devices, modules, etc., shown are not limiting. Depending on the actual modeling and solving requirements, the computing device 1000 may include more or fewer components, and this application does not limit this. Furthermore, the system architecture and application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. Those skilled in the art will understand that with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0068] For ease of understanding, the model building method for solving batch planning problems provided in this application is described below with reference to the accompanying drawings. This model building method for solving batch planning problems is applicable to... Figure 3 The computing device shown is 1000.

[0069] like Figure 4 As shown, Figure 4 A flowchart illustrating a model building method for solving batch planning problems, provided in this application embodiment. The model building method for solving batch planning problems specifically includes the following steps:

[0070] S210, the computing device acquires the production characteristics of the production environment.

[0071] In this step, specifically it can be Figure 3The computing device 1000 shown connects to the user's production-related systems via an interface module 1200 to acquire relevant data. The production characteristics acquired by the computing device include resource characteristics, production process characteristics, and production demand characteristics of the target product within the production environment.

[0072] The computing device can acquire production characteristics of the production environment through various possible implementation methods. For example, the computing device can interface with the user's business system, thereby acquiring production characteristics from the business system. Alternatively, the computing device can provide a user interface (UI) for the user to configure it themselves. For example, the computing device can display a list of production characteristics through the UI, which the user can select and edit according to the actual situation, thereby acquiring the production characteristics selected by the user. The two implementation methods mentioned above can also be used in combination, and this application does not limit this.

[0073] For example, the computing device obtains information from the connected MES and ERP systems, such as the type of the target product being product A, the production requirement for the target product being 100 units, resource characteristics including 5 punch presses, a shift capacity of 20 units, a production plan outlook period of 30 days and a granularity of 1 day, as well as production characteristics including setup time, sequence related to setup time, and not exceeding shift capacity.

[0074] S220, the computing device determines the problem type of the batch planning problem corresponding to the production environment based on the production characteristics.

[0075] In this embodiment, a template library can be pre-set. This template library includes the correspondence between production characteristics, batch planning problem types, and objectives / constraints. Furthermore, this template library is configurable; that is, the objectives / constraints can be flexibly added, deleted, or modified. For example, this template library can be set... Figure 3 In the modeling module 1120 shown, or it can also be set Figure 3 The storage location of the template library is not limited to the location of the computing device 1000 shown, but the computing device 1000 can access and obtain the data therein.

[0076] Different CLSP types correspond to different production characteristics, and the objectives and constraints to be considered in subsequent modeling are also different. Therefore, the computing device first determines the problem type of the batch planning problem corresponding to the production environment based on the obtained production characteristics through step S220. The solution of the batch planning problem CLSP is used to determine the production plan for batch production of the target product in the production environment.

[0077] In one possible implementation, the computing device obtains the matching degree between production characteristics and the production characteristics corresponding to different types of batch production planning problems. Then, based on the matching degree, the computing device determines the problem type of the batch planning problem corresponding to the production environment.

[0078] For example, see Figure 5 , Figure 5 for Figure 4 The illustrated embodiment is a schematic diagram showing the correspondence between problem types and production characteristics. Figure 5 It can be seen that when a computing device acquires production characteristics such as initial inventory, allowance for additional capacity, and allowance for delays, these production characteristics are related to... Figure 5 The production feature matching degree corresponding to the batch planning problem with initial inventory (CLSP-II) shown is 100%, so the computing device can determine that the problem type is CLSP-II. When the computing device obtains production features including changeover time and production features related to the changeover time sequence, these production features... Figure 5 The batch planning problem considering changeover time (CLSP-ST) shown has a 100% matching degree with the corresponding production characteristics, so the computing device can determine that the problem type of the batch planning problem is CLSP-ST.

[0079] It needs to be explained that, Figure 5 This is just one example; there are many other types of batch planning problems, such as the capacitated lot-sizing problem with parallel machines (CLSP-PM), the multi-level capacitated lot-sizing problem (MLCLSP), and so on. Each type of CLSP can be configured with its corresponding production characteristics (e.g., Figure 5 The document also shows that it allows for cross-period processing, including molds, considering heterogeneous parallel machines, considering homogeneous parallel machines, allowing substitution, allowing additional capacity, etc., and the correspondence between each type of CLSP and many production characteristics can be determined based on industry experts, operations research, and the accumulation of experience in actual project operations.

[0080] S230, The computing device obtains the objective function and constraints corresponding to the problem type.

[0081] In step S230, the computing device can obtain the corresponding objective function and constraints from the template library according to the problem type determined in step S220.

[0082] For example, see Figure 6 , Figure 6 for Figure 4 The illustrated embodiment is a schematic diagram of the correspondence between problem types and objectives / constraints. Figure 6 It can be seen that the objective function of CLSP-ST includes minimizing substitution costs, and the constraints of CLSP-ST include demand satisfaction constraints, capacity constraints, and inventory constraints; while the objective function of CLSP-II includes minimizing inventory costs and minimizing stockout costs, and the constraints of CLSP-ST include procurement constraints and capacity constraints. Figure 6 The objective functions shown, such as minimizing procurement costs and minimizing production costs, as well as other constraints such as substitution constraints, can be compared with other types of CLSPs.

[0083] Taking the aforementioned capacity constraint as an example, the capacity constraint specifically refers to the upper limit of the quantity of target products to be produced within a certain time (such as within 24 hours) in the current production environment, calculated by the computing equipment based on the acquired production characteristics. When the corresponding mathematical model is subsequently established and solved, the capacity constraint should be met; otherwise, the final production plan may become infeasible.

[0084] S240, the computing device constructs a mathematical model corresponding to the batch planning problem based on the objective function and constraints.

[0085] In some feasible embodiments, the modeling module in the computing device (e.g. Figure 3 The modeling module 1120 shown can construct the objective function, constraints, and other necessary variables at the code / software level by calling the solver's interface, thereby obtaining a module for batch planning problems, such as the MIP model or the linear programming (LP) model.

[0086] S250, the computing device performs a feasibility analysis on the mathematical model.

[0087] In this step, the computing device can input the actual data from the acquired production characteristics into the constructed mathematical model and analyze the feasibility of the model.

[0088] Specifically, when a computing device performs a feasibility analysis on a mathematical model, it primarily determines whether there are conflicts among the multiple constraints involved in the mathematical model. For example, constraint 1 is: a + b < 20, while constraint 2 is: a + b > 40. These two constraints are clearly mathematically impossible to satisfy simultaneously; in other words, constraint 1 and constraint 2 are conflicting constraints. If there are conflicting constraints (the first set of constraints), the computing device can determine that the corresponding mathematical model is infeasible. Conversely, if there are no conflicting constraints (the first set of constraints), the computing device can determine that the corresponding mathematical model is feasible.

[0089] In some feasible embodiments, the feasibility analysis module in the computing device (e.g. Figure 3 The feasibility analysis module 1130 shown can directly obtain the set of conflicting constraints (the first set of constraints) output by the solver by calling the feasibility analysis function of the solver.

[0090] If the computing device determines that the mathematical model is feasible, then proceed to step S270; if the computing device determines that the mathematical model is not feasible, then proceed to step S260.

[0091] S260, the computing device acquires the adjusted production characteristics.

[0092] In one possible implementation, step S260 can be further divided into the following three steps, such as... Figure 7 As shown, Figure 7 for Figure 4 The illustrated embodiment includes a flowchart of a feasibility analysis:

[0093] S261, The computing device determines the critical production characteristics among the production characteristics based on conflicting constraints.

[0094] For example, the current production capacity constraint is a+b<20, which can be understood as the current capacity being able to produce a maximum of 20 target products per day; while the demand satisfaction constraint is a+b>40, which can be understood as the corresponding capacity needing to produce 40 target products per day to meet the production demand of the target products. Thus, the key production characteristic of the equipment can be determined as: capacity, which can be further specified as the process characteristics of producing the target products, the number of equipment, etc.

[0095] S262, Display adjustment information for computing devices.

[0096] In some feasible embodiments, the computing device may display adjustment information to instruct the user to adjust key production features, for example, the adjustment information being: current capacity is insufficient, and two more machines A for producing the target product should be added.

[0097] S263, The computing device acquires the adjusted production characteristics.

[0098] After obtaining the adjusted production characteristics, the computing device can return to execute the aforementioned steps S220-S250, thereby re-determining the problem type, constructing a mathematical model, and conducting feasibility analysis (which can verify that there are no conflicting constraints among the constraints corresponding to the adjusted production characteristics), etc.

[0099] Through the steps S261-S263 described above, the computing device can quickly determine the feasibility of the mathematical model and inform the user in advance. It helps identify and evaluate production characteristics that may make the plan unfeasible. By displaying adjustment information, it can also guide the user to make adjustments and help business personnel adjust production characteristics in a timely manner.

[0100] S270, The computing device obtains a feasible solution to the mathematical model.

[0101] Through the aforementioned step S260, the computing device has determined that the constructed mathematical model is feasible. Then, the computing device can use one or more algorithms (such as initial solution algorithm, mathematical programming algorithm, iterative algorithm, etc.) to solve the mathematical model, thereby obtaining a feasible solution to the mathematical model.

[0102] In one possible implementation, the computing device can adopt a targeted algorithm based on the CLSP type corresponding to the mathematical model when solving the mathematical model, thereby improving the efficiency of solving and the quality of feasible solutions.

[0103] In some feasible embodiments, the specific solution process can be found in [reference needed]. Figure 8 , Figure 8 for Figure 4 The illustrated embodiment involves a flowchart of a mathematical model solution process:

[0104] S271, The computing device obtains the first solution.

[0105] In this step, the computing device can use an initial solution algorithm to solve the mathematical model. An initial solution algorithm is an algorithm that achieves higher solution efficiency by fixing some variable values ​​or relaxing some constraints, such as the greedy algorithm, genetic algorithm, simulated annealing algorithm, etc.

[0106] S272, The computing device determines whether the goodness of the first solution meets the target goodness threshold.

[0107] The target goodness threshold can be set by the user. For example, the target goodness threshold is that the production efficiency of the target production line is greater than or equal to 90%. If the first solution indicates that the production efficiency of the target production line is 95% (i.e., the goodness of the first solution meets the target goodness threshold), the computing device can determine that the first solution is a feasible solution and then execute step S277. If the first solution indicates that the production efficiency of the target production line is 80% (i.e., the goodness of the first solution does not meet the target goodness threshold), the computing device executes step S273.

[0108] S273, The computing device obtains the second solution.

[0109] In this step, the computing device can use mathematical programming algorithms to solve the mathematical model, such as the branch and bound method and the cutting plane method. These algorithms are generally less efficient than the initial solution algorithm, but they offer higher goodness. Therefore, if the solution obtained by the initial solution algorithm does not meet the target goodness threshold, the computing device can use mathematical programming algorithms to attempt to obtain a feasible solution with higher goodness to satisfy the target goodness threshold.

[0110] S274, The computing device determines whether the goodness of the second solution meets the target goodness threshold.

[0111] If the goodness of the second solution meets the target goodness threshold, the computing device can determine that the second solution is a feasible solution and then execute step S277; otherwise, the computing device executes step S275.

[0112] S275, The computing device obtains the third solution.

[0113] In this step, the computing device can use an iterative algorithm and, based on the feasible solution obtained in step S274, try to obtain a more favorable feasible solution by adjusting some objective functions and constraints in the mathematical model, so as to meet the target goodness threshold.

[0114] S276: The computing device determines whether the goodness of the third solution meets the target goodness threshold.

[0115] If the goodness of the third solution meets the target goodness threshold, the computing device can determine that the third solution is a feasible solution and then execute step S277; otherwise, the computing device can repeat step S275 for multiple iterations until the set number of iterations is reached. If the goodness still cannot be met, an alarm message is returned to prompt the user to lower the target goodness threshold or adjust the production characteristics.

[0116] During the execution of steps S271-S275 by the computing device, the solution time continuously increases. In some production environments where the solution time is sensitive, it is necessary to determine a feasible solution in a timely manner. The embodiments of this application provide a possible implementation method, including: during the process of the computing device obtaining a feasible solution of the mathematical model, the solution time is statistically analyzed, and when the solution time reaches the target time threshold, the most recently obtained solution is determined as a feasible solution. In this way, the computing device can provide a feasible solution in a timely manner, which meets the cycle time requirements in some production environments.

[0117] S277, the computing device generates a production plan.

[0118] In this step, the computing device can generate a corresponding production plan based on the feasible solutions obtained in the previous steps. For example, a feasible solution is to produce 20 units of product A using equipment A within 8 hours, requiring 3 workers to operate equipment A; to produce 40 units of product B using equipment B within 8 hours, requiring 3 workers to operate equipment B; and to process product A and product B into 20 units of product C using equipment C within 8 hours, requiring 5 workers to operate equipment C. Assuming that the efficiency of the aforementioned production process is uniform, the production plan generated by the computing device can be as follows: In the first shift of date A (8:00-17:00), group 1 and group 2 (each including 3 workers) are assigned to operate equipment A and equipment B respectively to produce 20 units of product A and 40 units of product B. In the second shift of date A (13:00-21:00), group 3 (including 5 workers) is assigned to operate equipment C to process the products A and B produced that day into 20 units of product C.

[0119] Through steps S210-S270, the computing device can automatically determine the problem type of batch planning based on the production characteristics of the production environment. Then, it automatically constructs a corresponding mathematical model based on the objective function and constraints of the problem type, replacing the traditional manual modeling method and significantly improving the efficiency of model construction for solving batch planning problems. Furthermore, by performing feasibility analysis on the model, the computing device can prompt the user to adjust production characteristics in a timely manner if the model is infeasible. If the model is feasible, the computing device can further combine various algorithms with different advantages to obtain feasible solutions, achieving a balance between solution time and the quality of feasible solutions, ultimately resulting in a feasible production plan.

[0120] The following section uses sheet metal forming processing as an exemplary business scenario to explain and illustrate the model construction method for solving batch planning problems provided in this application.

[0121] In a production environment related to sheet metal forming and processing, the purpose of solving batch planning problems is usually to determine monthly shift times and staffing, as well as to optimize the sheet metal processing task arrangement for each shift (or simply to determine the monthly production plan).

[0122] Sheet metal processing, depending on the type of product being processed, requires production to be completed on multiple production lines, with each line consisting of one or more punch presses connected in series, as shown below. Figure 9 As shown, Figure 9 This is a schematic diagram of a sheet metal processing production line provided in an embodiment of this application, showing production line 1 and production line 2. Production line 1 includes punch presses 1-3, and production line 2 includes punch presses 4 and 5. Raw materials can be processed into shaped sheet metal through the processing of each production line.

[0123] Although sheet metal processing involves multiple punch presses, these different presses on the same production line are inseparable and must be considered as a whole. Furthermore, each type of sheet metal processing requires a corresponding sheet metal die to be installed on the punch press. Figure 9 (as shown in the image).

[0124] Moreover, in the embodiments of this application, it is assumed that there is only one set of molds for each type of sheet metal, that is, each type of sheet metal is only allowed to be produced on one production line in the same shift.

[0125] The algorithm needs to provide the shift mode selection and employee allocation plan for each production line based on sheet metal requirements and other parameter information, as well as the quantity of various sheet metals produced by each production line in each shift.

[0126] Based on the foregoing content regarding sheet metal forming and processing, the model construction method for solving batch planning problems provided in this application embodiment is detailed below. Figure 10 , Figure 10 A flowchart for model construction in a sheet metal forming process scenario provided in this application embodiment includes:

[0127] S310: The computing device acquires production characteristics of the production environment.

[0128] For example, the computing device obtains production characteristics such as orders, production resource characteristics, production process characteristics, consideration of homogeneous parallel machines, consideration of heterogeneous parallel machines, allowance of time spans, raw material characteristics, inventory capacity characteristics, and a production plan outlook period of 30 days with a granularity set to 1 day from the customer's MES system.

[0129] S320: The calculation device determines the problem type of the monthly batch plan for sheet metal.

[0130] For example, the computing device determines the problem type of the sheet metal monthly batch planning problem as CLSP-PM based on production characteristics that include consideration of homogeneous parallel machines, consideration of heterogeneous parallel machines, and allowance of inter-period production.

[0131] S330: The computing device obtains the objective function and constraints corresponding to the problem type and constructs the corresponding mathematical model.

[0132] For example, the computing device obtains the objective function and constraints corresponding to CLSP-PM from a preset template library. The objective function specifically includes minimizing labor costs, minimizing time costs, minimizing inventory costs, minimizing unmet demand costs, and minimizing production costs. The constraints include capacity constraints, labor constraints, raw material constraints, production rule constraints, and sheet metal inventory capacity constraints. Then, the computing device constructs the corresponding mathematical model based on the aforementioned objective function and constraints.

[0133] S340: The computing device performs a feasibility analysis on the mathematical model.

[0134] For example, the computing device calls the feasibility analysis function of the solver to perform a feasibility analysis on the mathematical model it has constructed. If the constructed model is not feasible, it obtains the conflicting constraints returned by the solver and determines the key generation features in the production features based on the conditions. For example, if the current production capacity is insufficient to meet the order demand, the sheet metal inventory capacity is too small, resulting in the inability to store the formed sheet metal. The computing device can then display adjustment information: increase the sheet metal inventory capacity, obtain the adjusted production features, and return to the execution step S320.

[0135] If the computing device determines that the mathematical model is feasible, it continues to execute step S350.

[0136] S350: The computing device obtains a feasible solution to the mathematical model.

[0137] For example, the computing device directly calls the mathematical programming algorithm in the solver to obtain a solution that meets the target goodness threshold set by the user as the final feasible solution, and further generates a corresponding production plan based on this. In addition, the computing device can also generate a corresponding resource plan based on the production plan.

[0138] Through the above steps S310-S350, the computing device realizes that in a sheet metal forming and processing related production environment, the problem type of batch planning problem (CLSP-PM) is determined according to its production characteristics, and the objective function and constraints corresponding to the problem type are obtained. Then, the corresponding mathematical model is automatically constructed, which significantly improves the modeling efficiency, and a feasible production plan is obtained based on the feasible solution of the mathematical model.

[0139] The foregoing mainly describes the solutions of the embodiments of this application from a methodological perspective. It is understood that, in order to achieve the above functions, the model building apparatus includes at least one of the hardware structures and software modules corresponding to each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0140] This application embodiment can divide the model building device into functional units according to the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0141] For example, Figure 11 This application provides a schematic diagram of a model building apparatus 800. The model building apparatus 800 is applied in a computing device, or the model building apparatus 800 can be a computing device. The model building apparatus 800 includes:

[0142] The first acquisition module 810 is used to acquire the production characteristics of the production environment; the production characteristics include the resource characteristics, production process characteristics, and production demand characteristics of the target product used in the production environment.

[0143] The determination module 820 is used to determine the problem type of the batch planning problem corresponding to the production environment based on the production characteristics, and the solution of the batch planning problem is used to determine the production plan for batch production of the target product in the production environment.

[0144] The second acquisition module 830 is used to acquire the objective function and constraints corresponding to the problem type;

[0145] The construction module 840 is used to construct a mathematical model corresponding to the batch planning problem based on the objective function and the constraints.

[0146] For example, combining Figure 4 The first acquisition module 810 can be used to perform, for example... Figure 4As shown in S210, the determination module 820 can be used to perform, for example... Figure 4 As shown in S220, the second acquisition module 830 can be used to perform, for example... Figure 4 As shown in S230, the building module 840 can be used to perform, for example... Figure 4 S240 is shown.

[0147] In one possible implementation, the determining module 820 is further configured to: obtain the matching degree between the production characteristics and the production characteristics corresponding to different types of batch production planning problems; and determine the problem type of the batch planning problem corresponding to the production environment based on the matching degree.

[0148] In one possible implementation, the apparatus further includes a feasibility analysis module, which is configured to: obtain a first set of constraints, the first set of constraints including conflicting constraints; determine key production features among the production features based on the conflicting constraints; and obtain adjusted production features, wherein the constraints corresponding to the adjusted production features do not contain conflicting constraints.

[0149] In one possible implementation, the device further includes a display module for displaying adjustment information that instructs a user to adjust the key production feature.

[0150] In one possible implementation, the apparatus further includes a solution module, which is configured to: obtain a feasible solution to the mathematical model; and determine a production plan for mass production of the target product in the production environment based on the feasible solution.

[0151] In one possible implementation, the solution module is further configured to: obtain a first solution of the mathematical model; the first solution is a solution obtained by solving the mathematical model using an initial solution algorithm; if the goodness of the first solution meets the target goodness threshold, determine the first solution as the feasible solution.

[0152] In one possible implementation, the solution module is further configured to: obtain a second solution to the mathematical model; the second solution is a solution obtained by solving the mathematical model using a mathematical programming algorithm; if the goodness of the second solution meets the target goodness threshold, determine the second solution as the feasible solution.

[0153] In one possible implementation, the solution module is further configured to: obtain a third solution of the mathematical model; the third solution is a solution obtained by solving the mathematical model using an iterative algorithm based on the second solution; if the goodness of the third solution meets the target goodness threshold, the third solution is determined to be the feasible solution.

[0154] As a feasible example, the model building device 800 provided in this application is implemented through a software module. For example, the software module can be provided to users through a cloud service subscription model, and users can choose different subscription levels according to their needs. Alternatively, the software module can also provide enterprise-level customized services with professional domain customization, interface personalization and extended functions according to the needs of users or enterprises.

[0155] Furthermore, the model building device 800 provided in this application can also be provided to users as a value-added service, and this application does not limit this. When the model building device 800 is implemented through a software module, it can also be embedded into other batch planning problem solving software or operations research optimization systems.

[0156] This application also provides a computing device 100. For example... Figure 12 As shown, the computing device 100 includes a bus 102, a processor 104, a memory 106, and a communication interface 108. The processor 104, the memory 106, and the communication interface 108 communicate with each other via the bus 102. The computing device 100 may be a server or a terminal device. It should be understood that the embodiments of this application do not limit the number of processors and memories in the computing device 100.

[0157] Bus 102 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 12 The bus 104 may be represented by a single line, but this does not mean that there is only one bus or one type of bus. The bus 104 may include a path for transmitting information between various components of the computing device 100 (e.g., memory 106, processor 104, communication interface 108).

[0158] The processor 104 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0159] Memory 106 may include volatile memory, such as random access memory (RAM). Processor 104 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0160] The memory 106 stores executable program code, and the processor 104 executes the executable program code to implement the functions of the aforementioned first acquisition module, determination module, second acquisition module, and construction module, thereby realizing the model construction method for solving batch planning problems. That is, the memory 106 stores instructions for executing the model construction method for solving batch planning problems.

[0161] This application also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0162] like Figure 13 As shown, the computing device cluster includes at least one computing device 100. The memory 106 of one or more computing devices 100 in the computing device cluster may store the same instructions for executing a model building method for solving batch planning problems.

[0163] In some possible implementations, the memory 106 of one or more computing devices 100 in the computing device cluster may also store partial instructions for executing a model-building method for solving batch planning problems. In other words, a combination of one or more computing devices 100 can jointly execute instructions for executing a model-building method for solving batch planning problems.

[0164] It should be noted that the memory 106 in different computing devices 100 within the computing device cluster can store different instructions, each used to execute a portion of the functions of the model building device. That is, the instructions stored in the memory 106 of different computing devices 100 can implement the functions of one or more modules among the first acquisition module, the determination module, the second acquisition module, and the construction module.

[0165] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN) or a local area network (LAN), etc. Figure 14 One possible implementation is shown. For example... Figure 14 As shown, two computing devices 100A and 100B are connected via a network. Specifically, they are connected to the network through communication interfaces in each computing device. In this possible implementation, the memory 106 in computing device 100A stores instructions for executing the functions of the first acquisition module. Simultaneously, the memory 106 in computing device 100B stores instructions for executing the functions of the determination module, the second acquisition module, and the construction module.

[0166] It should be understood that Figure 11 The functions of the computing device 100A shown can also be performed by multiple computing devices 100. Similarly, the functions of the computing device 100B can also be performed by multiple computing devices 100.

[0167] This application also provides a computer program product containing instructions. The computer program product may be a software or program product containing instructions, capable of running on a computing device or stored on any available medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to execute a model building method for solving batch planning problems.

[0168] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute a model-building method for solving batch planning problems, or instruct the computing device to execute a model-building method for solving batch planning problems.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model construction method for solving batch planning problems, characterized in that, The method includes: Acquire the production characteristics of the production environment; the production characteristics include the resource characteristics, production process characteristics, and production demand characteristics of the target product used in the production environment. Based on the production characteristics, the problem type of the batch planning problem corresponding to the production environment is determined, and the solution of the batch planning problem is used to determine the production plan for batch production of the target product in the production environment. Obtain the objective function and constraints corresponding to the problem type; Based on the objective function and the constraints, construct the mathematical model corresponding to the batch planning problem.

2. The method according to claim 1, characterized in that, The step of determining the problem type of the batch planning problem corresponding to the production environment based on the production characteristics includes: Obtain the matching degree between the production characteristics and the production characteristics corresponding to different types of batch production planning problems; Based on the matching degree, the problem type of the batch planning problem corresponding to the production environment is determined.

3. The method according to claim 1 or 2, characterized in that, After constructing the mathematical model corresponding to the batch planning problem based on the objective function and the constraints, the method further includes: Obtain a first set of constraints, which includes conflicting constraints among the constraints. Based on the conflicting constraints, the key production characteristics among the production characteristics are determined; Obtain the adjusted production characteristics, wherein there are no conflicting constraints in the constraints corresponding to the adjusted production characteristics.

4. The method according to claim 3, characterized in that, Before obtaining the adjusted production characteristics, the method further includes: Display adjustment information, which instructs the user to adjust the key production features.

5. The method according to any one of claims 1-4, characterized in that, After constructing the mathematical model corresponding to the batch planning problem based on the objective function and the constraints, the method further includes: Obtain a feasible solution to the mathematical model; Based on the feasible solution, a production plan for mass production of the target product in the production environment is determined.

6. The method according to claim 5, characterized in that, Obtaining a feasible solution to the mathematical model includes: Obtain the first solution of the mathematical model; the first solution is the solution obtained by solving the mathematical model using the initial solution algorithm; If the goodness of the first solution meets the target goodness threshold, the first solution is determined to be a feasible solution.

7. The method according to claim 6, characterized in that, If the goodness of the first solution does not meet the target goodness threshold, obtaining a feasible solution to the mathematical model includes: Obtain a second solution to the mathematical model; the second solution is the solution obtained by solving the mathematical model using a mathematical programming algorithm; If the goodness of the second solution meets the target goodness threshold, the second solution is determined to be the feasible solution.

8. The method according to claim 7, characterized in that, If the goodness of the second solution does not meet the target goodness threshold, obtaining a feasible solution to the mathematical model includes: Obtain a third solution to the mathematical model; the third solution is a solution obtained by solving the mathematical model using an iterative algorithm based on the second solution. If the goodness of the third solution meets the target goodness threshold, the third solution is determined to be a feasible solution.

9. A model building apparatus, characterized in that, The device includes: The first acquisition module is used to acquire the production characteristics of the production environment; the production characteristics include the resource characteristics, production process characteristics, and production demand characteristics of the target product used in the production environment. The determination module is used to determine the problem type of the batch planning problem corresponding to the production environment based on the production characteristics, and the solution of the batch planning problem is used to determine the production plan for batch production of the target product in the production environment. The second acquisition module is used to acquire the objective function and constraints corresponding to the problem type; The construction module is used to construct a mathematical model corresponding to the batch planning problem based on the objective function and the constraints.

10. The apparatus according to claim 9, characterized in that, The determining module is used for: Obtain the matching degree between the production characteristics and the production characteristics corresponding to different types of batch production planning problems; Based on the matching degree, the problem type of the batch planning problem corresponding to the production environment is determined.

11. The apparatus according to claim 9 or 10, characterized in that, The device further includes a feasibility analysis module, which is used for: Obtain a first set of constraints, which includes conflicting constraints among the constraints. Based on the conflicting constraints, the key production characteristics among the production characteristics are determined; Obtain the adjusted production characteristics, wherein there are no conflicting constraints in the constraints corresponding to the adjusted production characteristics.

12. The apparatus according to claim 11, characterized in that, The device further includes a display module, which is used for: Display adjustment information, which instructs the user to adjust the key production features.

13. The apparatus according to any one of claims 9-12, characterized in that, The device further includes a solution module, which is used for: Obtain a feasible solution to the mathematical model; Based on the feasible solution, a production plan for mass production of the target product in the production environment is determined.

14. The apparatus according to claim 13, characterized in that, The solution module is used for: Obtain the first solution of the mathematical model; the first solution is the solution obtained by solving the mathematical model using the initial solution algorithm; If the goodness of the first solution meets the target goodness threshold, the first solution is determined to be a feasible solution.

15. The apparatus according to claim 14, characterized in that, The solution module is used for: Obtain a second solution to the mathematical model; the second solution is the solution obtained by solving the mathematical model using a mathematical programming algorithm; If the goodness of the second solution meets the target goodness threshold, the second solution is determined to be the feasible solution.

16. The apparatus according to claim 15, characterized in that, The solution module is used for: Obtain a third solution to the mathematical model; the third solution is a solution obtained by solving the mathematical model using an iterative algorithm based on the second solution. If the goodness of the third solution meets the target goodness threshold, the third solution is determined to be a feasible solution.

17. A computing device cluster, characterized in that, It includes at least one computing device, each computing device including a processor and memory; The processor of the at least one computing device is used to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the model building method for solving batch planning problems as described in any one of claims 1-8.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer instructions; when the computer instructions are executed in a computing device, the computing device performs the model building method for solving batch planning problems according to any one of claims 1-8.

19. A computer program product, characterized in that, When the computer program product is run in a computing device, the computing device executes the model building method for solving batch planning problems as described in any one of claims 1-8.

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