Multi-factory-oriented production plan optimization method and device
By building a target model for multi-factory production planning optimization, using dynamic break-even strategy and multi-dimensional break-even constraints, the problem of break-even and multi-constraint handling in multi-factory production planning optimization in the existing technology is solved, and feasible production planning decisions and operating profits are maximized in the oversupply scenario.
Patent Information
- Application Number
- CN202510195517.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-21
- Publication Date
- 2025-05-23
AI Technical Summary
In the optimization of multi-factory production planning, it is difficult to effectively deal with complex break-even and multi-constraint conditions, especially in the case of oversupply, resulting in unfeasible production planning decisions.
By obtaining relevant data on the target products produced by multiple factories, a target model of multi-bubble conditions and objective functions based on decision variables is constructed, and a dynamic break-even strategy and multi-dimensional break-even constraints are used to optimize production plans.
In the case of oversupply, we have achieved the goal of avoiding excessive pursuit of profits, ensuring the feasibility of production planning decisions, and maximizing operating profits under the premise of basic factory operation.
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Figure CN120031206A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing technology, and in particular to a production plan optimization method and device for multiple factories. Background Art
[0002] In the production management process of an enterprise, the requirements for production lines and overall output of different products produced by the factory are different, and the adjustment of production lines will directly affect the efficiency of the factory. Frequent adjustments will lead to reduced production efficiency and product quality problems. Therefore, enterprises usually conduct medium- and long-term supply and demand balance calculations for each factory through sales orders and other demand conditions to decide on the delivery date of orders and the allocation of factory production capacity, especially the setting and adjustment of production lines under different product structures. The optimization of production plans for multiple factories is a complex problem. In business applications, it is required to meet various constraints and achieve the improvement and improvement of business goals. Currently, the commonly used modeling method converts the business model into a mathematical model, such as Figure 1 As shown, the optimization of production plan is completed by finding a better solution or the optimal solution.
[0003] Existing technologies based on the idea of operations research and optimization solve the problem of resource allocation for enterprises to balance production and sales with profit orientation through multi-constraint and multi-objective decision-making, which is a good technical direction. However, when setting up the business model, profit maximization and cost minimization are usually used as optimization goals. The results of this are usually not executable, because there are many special influencing factors in the business operation process, which cannot be intuitively portrayed in the model. For example, in the scenario of oversupply, simply maximizing profits and minimizing costs will cause the demand allocation of some factories to be 0, but in actual applications, even if the production volume allocated to a factory is 0, the equipment must not stop running to ensure the healthy operation of the equipment. The shutdown of a factory is not simply based on profit and cost as indicators, but also needs to consider many invisible conditions, such as policy taxes, severance costs, equipment maintenance, and future market expectations. It can be seen that the existing model is not sufficient to support the above demands. Summary of the invention
[0004] It would be advantageous for the present disclosure to provide a mechanism to mitigate, alleviate or eliminate at least one of the problems discussed above.
[0005] In a first aspect, a method for optimizing production plans for multiple factories is provided. The method comprises:
[0006] Acquire input data, wherein the input data is relevant data of target products produced by multiple factories, and the input data includes cost data of the target products, wherein the cost data refers to cost ladder data based on the quantity of products;
[0007] Building a target model based on the input data, the target model includes multiple constraints based on decision variables and an objective function, wherein the decision variables include a product quantity variable for each factory producing the target product and a profit and loss slack variable for each factory, the multiple constraints are used to constrain the profit and loss balance of the multiple factories, and the objective function is used to indicate an optimization target of a production plan for the target product;
[0008] The target model is solved to obtain an optimization result of the multi-factory production plan.
[0009] Optionally, the input data further includes profit data of the target product and a factory penalty coefficient, wherein the factory penalty coefficient is used to control the profit and loss relaxation cost of the factory. The target model is constructed based on the input data, including:
[0010] The objective function is constructed according to the profit data, the factory penalty coefficient and the decision variables.
[0011] Optionally, constructing the objective function according to the profit data, the factory penalty coefficient and the decision variable includes:
[0012] Acquire total profit data of the plurality of factories according to the profit data of the target product and the product quantity variable;
[0013] Acquire total cost data of the plurality of factories according to the cost data of the target product and the product quantity variable;
[0014] Obtaining a total profit and loss penalty item for the plurality of factories according to the profit and loss slack variable of each factory and the factory penalty coefficient;
[0015] An objective function is constructed by using the total profit data of the multiple factories, the total cost data of the multiple factories and the total profit and loss penalty items of the multiple factories.
[0016] Optionally, the input data further includes total loss data allowed by the multiple factories, the multiple constraints include a break-even constraint of each factory and a total loss constraint of the multiple factories, and the method further includes:
[0017] Constructing the break-even constraint of each factory through the product quantity variable of the target product produced by each factory, the cost data of the target product, the profit data of the target product and the break-even slack variable of each factory;
[0018] The total profit and loss slack variables of the multiple factories are obtained through the profit and loss slack variables of each factory, and the total loss constraints of the multiple factories are constructed according to the total profit and loss slack variables of the multiple factories and the total loss data allowed by the multiple factories.
[0019] Optionally, the multiple constraints further include a supply constraint, and the supply constraint includes at least one of the following:
[0020] Demand satisfaction constraint, used to constrain the production volume of the target product to meet the demand plan;
[0021] Capacity constraints, used to constrain the production of each factory to not exceed the upper capacity of the factory;
[0022] The material supply constraint is used to constrain that the total material demand of the multiple factories should not exceed the total supply.
[0023] Optionally, the input data further includes demand planning data, production capacity data, supply capacity data and process data of the target product, and the method further includes:
[0024] The demand satisfaction constraint is constructed by using the product quantity variables and demand planning data of each factory producing the target product;
[0025] The capacity constraint is constructed by using the product quantity variables and capacity data of each factory producing the target product;
[0026] The material supply constraints are constructed by using product quantity variables, supply capacity data and process data of each factory producing the target product.
[0027] Optionally, the target model is constructed according to a preset algorithm, the preset algorithm is one of a linear programming algorithm, an integer programming algorithm and a mixed integer programming algorithm, and solving the target model includes:
[0028] The target model is solved by calling a dedicated solver.
[0029] In a second aspect, a production plan optimization device for multiple factories is provided. The device comprises:
[0030] An acquisition unit, configured to acquire input data, wherein the input data is relevant data of target products produced by multiple factories, and the input data includes cost data of the target products, wherein the cost data refers to step-by-step cost data based on the quantity of products;
[0031] A modeling unit, configured to construct a target model based on the input data, wherein the target model includes multiple constraints based on decision variables and an objective function, wherein the decision variables include a product quantity variable for each factory producing the target product and a profit and loss slack variable for each factory, the multiple constraints are used to constrain the profit and loss balance of the multiple factories, and the objective function is used to indicate an optimization target of a production plan for the target product;
[0032] A solving unit is used to solve the target model to obtain an optimization result of the multi-factory production plan.
[0033] In a third aspect, an electronic device is provided, comprising: one or more processors; and one or more memories coupled to the one or more processors and storing instructions thereon, wherein when the instructions are executed by the one or more processors individually or collectively, the electronic device executes a multi-factory production planning optimization method.
[0034] In a fourth aspect, a non-transitory computer-readable storage medium storing machine-executable instructions is provided. When the machine-executable instructions are executed individually or collectively by one or more processors of a machine, the machine executes a multi-factory production planning optimization method.
[0035] In a fifth aspect, a computer program product is provided, comprising machine executable instructions, which, when executed individually or collectively by one or more processors of a machine, cause the machine to perform a multi-factory production planning optimization method.
[0036] The disclosed embodiment improves the input data of the target model, designs cost data in a stepped form based on the specific relationship between the production quantity and cost of the target product, and constructs an objective function using input data including cost data and decision variables to characterize the dynamic profit and loss balance of multiple factories. Combined with multi-dimensional profit and loss balance constraints, it ensures that the final optimization result can support the maximization of indicators such as operating profit under the premise of basic factory operation, so that the production plan optimization method of the disclosed embodiment can take into account scenarios such as supply shortage and oversupply.
[0037] It should be understood that the invention summary is not intended to identify the key or essential features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of some embodiments of the present disclosure in the accompanying drawings, in which:
[0039] Figure 1 The schematic diagram of the principle of production plan optimization based on the idea of operations research optimization is shown;
[0040] Figure 2 A schematic flow chart of a multi-factory production plan optimization method according to some embodiments of the present disclosure is shown;
[0041] Figure 3A schematic diagram showing a production plan optimization process according to some embodiments of the present disclosure;
[0042] Figure 4 A structural block diagram of a production plan optimization device for multiple factories according to some embodiments of the present disclosure is shown;
[0043] Figure 5 A simplified block diagram of a device suitable for implementing the exemplary embodiments of the present disclosure is shown. DETAILED DESCRIPTION
[0044] The principle of the present disclosure will now be described with reference to some embodiments. It should be understood that the description of these embodiments is only for illustrative purposes, and helps those skilled in the art to understand and implement the present disclosure, without any limitation to the scope of the present disclosure. The disclosure described herein can be implemented in a manner different from that described below.
[0045] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
[0046] References in this disclosure to "one embodiment," "an embodiment," "an exemplary embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. In addition, when a particular feature, structure, or characteristic is described in conjunction with an exemplary embodiment, whether or not explicitly described, those skilled in the art will recognize that such feature, structure, or characteristic affects incorporation into other embodiments.
[0047] It should be understood that although the terms "first" and "second" etc. may be used to describe various elements herein, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of the exemplary embodiments, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the listed terms.
[0048] The terms used in this document are only for describing specific embodiments and are not intended to limit the exemplary embodiments. The singular forms "a", "an", and "the" used in this document also include the plural forms unless the context clearly indicates otherwise. The term "a set of elements" or "a collection of elements" used herein is intended to include one or more elements. It should also be understood that the terms "include", "comprise", "have", "possess", "include", and / or "comprise", when used herein, specify the presence of the described features, elements, and / or components, etc., but do not exclude the presence or addition of one or more other features, elements, components, and / or their combinations.
[0049] In view of the problems described in the background art, the embodiments of the present disclosure provide a production plan optimization method for multiple factories, a production plan optimization device for multiple factories, an electronic device, a non-transitory computer-readable storage medium, and a computer program product. The basic technical concept is to introduce a dynamic break-even strategy to ensure that the decision-making results support the basic operation of the factory and maximize indicators such as operating profit, and further, in the scenario of oversupply, it is possible to avoid excessive pursuit of profit, resulting in the infeasibility of the production plan decision-making for multiple factories.
[0050] To make the objectives, features, and beneficial effects of the technical solution more obvious and understandable, the input data and decision variables related to the embodiments of the present disclosure are introduced below:
[0051] (I) Decision variables:
[0052] x f,p,m ≥0, representing the quantity of the target product p produced by factory f in month m;
[0053] s f,m ≥0 represents the break-even slack variable of factory f in month m, used to allow a certain amount of loss.
[0054] (II) Input data:
[0055] P is the set of target products, including all target products, p ∈ P;
[0056] F is the set of factories, including all factories, f ∈ F;
[0057] M is the set of months, including all months, m ∈ M;
[0058] Demand p,m is the demand for the target product p in month m;
[0059] Capacity f,m is the upper limit of the production capacity of factory f in month m;
[0060] Material_Need f,p,mis the quantity of material required per unit of production quantity when factory f produces the target product p;
[0061] Material_Supply m is the material supply in month m;
[0062] Profit p is the marginal profit of the target product p;
[0063] Labor_Cost(f,x) is the labor cost of factory f based on production quantity x;
[0064] Energy_Cost(f,x) is the energy cost of factory f based on the production quantity x;
[0065] w f is the factory penalty coefficient of factory f, which is used to control the profit and loss relaxation cost of factory f;
[0066] MaxLoss is the total loss allowed for all factories.
[0067] Reference now Figure 2 . Figure 2 A flowchart of a multi-factory production plan optimization method 200 according to some embodiments of the present disclosure is shown. The method 200 can be implemented on one device or on distributed devices. It should be understood that the method 200 may include additional steps not shown and / or may omit some of the steps shown, and the scope of the present disclosure is not limited to this.
[0068] like Figure 2 As shown, a multi-factory production plan optimization method 200 in an embodiment of the present disclosure includes at least the following steps S210 to S230:
[0069] Step S210, obtaining input data, wherein the input data is relevant data of target products produced by multiple factories, and the input data includes cost data of the target products, and the cost data refers to cost ladder data based on the quantity of products.
[0070] In some embodiments, for a certain target product, different production quantities require different resources from the factory. For example, to produce a target product in quantities below 1,000, only one production line needs to be started, but to produce more than 1,000, a second production line or more needs to be started. The last newly opened production line may not be at full capacity, but in any case, the personnel cost corresponding to each production line is relatively fixed, and the energy cost is also fixed.
[0071] Table 1:
[0072]
[0073]
[0074] For example, referring to the above-mentioned Chart 1, within the quantity range of a target product, the personnel cost is relatively fixed and the energy cost is also fixed. Based on this, the embodiment of the present disclosure performs hierarchical processing on the cost data based on the product data, and uses the stepped cost data as the data basis for the factory's dynamic profit and loss balance strategy, so that the objective function constructed based on the cost data can characterize the factory's profit and loss balance.
[0075] It is understandable that the input data in this embodiment also includes other relevant data, such as operating data, profit data of the target product, demand planning data, production capacity data, supply capacity data and processing data, factory penalty coefficient, etc.
[0076] Step S220, constructing a target model based on the input data, the target model includes multiple constraints and an objective function based on decision variables, wherein the decision variables include a product quantity variable for each factory producing the target product and a profit and loss slack variable for each factory, the multiple constraints are used to constrain the profit and loss balance of the multiple factories, and the objective function is used to indicate the optimization target of the production plan of the target product.
[0077] The target model of this embodiment is constructed according to a preset algorithm, which is one of a linear programming algorithm, an integer programming algorithm and a mixed integer programming algorithm. In practical applications, the corresponding algorithm model should be selected according to the type of the target product. For example, if the quantity unit of the target product is an integer, an integer programming algorithm can be selected.
[0078] The objective function is constructed through input data and decision variables. Since the cost data in the input data is a stepped data related to the product quantity, the constructed objective function is also a stepped form based on the product quantity. In other words, when the target product is in different quantity ranges, the final optimization result solved based on the constraint conditions is different, showing a dynamic effect, thereby achieving a dynamic profit and loss balance effect.
[0079] Taking the break-even point into consideration in the objective function can assist in making decisions on the profit and loss situation under different product production structures, and can more scientifically assist in making decisions on the production plan quantity of each factory in a dynamic break-even manner. This is more in line with the business conditions of the enterprise than simply setting the minimum production quantity of the factory, because a single minimum production quantity cannot reflect the impact of different types of production products on the break-even point.
[0080] Step S230, solving the target model to obtain an optimization result of the multi-factory production plan.
[0081] This embodiment can solve the target model by calling a dedicated solver to improve the solving efficiency of the model.
[0082] like Figure 2 It can be seen from the production plan optimization method for multiple factories shown that the embodiment of the present disclosure improves the input data of the target model, designs cost data in a step-like form based on the specific relationship between the production quantity and cost of the target product, and constructs the objective function using input data including cost data and decision variables to characterize the dynamic profit and loss balance of multiple factories. Combined with multi-dimensional profit and loss balance constraints, it ensures that the final optimization result can support the maximization of indicators such as operating profit under the premise of basic factory operation, so that the production plan optimization method of the embodiment of the present disclosure can take into account scenarios such as supply shortage and oversupply.
[0083] In some embodiments, the input data includes demand planning data, production capacity data, supply capacity data, process data of the target product, cost data, profit data and preset data of the target product, wherein:
[0084] Demand planning data refers to sales orders from customers and forecasts for customers, including product codes, quantity, and date of demand. p,m Plan data for one demand;
[0085] Capacity data, describing the upper limit of each factory's production capacity, usually monthly in high-level planning. f,m It is a kind of production capacity data;
[0086] Supply capacity data, describing the supply capacity of key materials, usually monthly in high-level planning, Material_Supply in the previous article m It is a kind of supply capacity data;
[0087] Process data, describing the target product's capacity consumption in each factory. In high-level planning, it is usually rough capacity. The Material_Need in the previous article f,p,m It is a kind of process data. In addition, process data also includes bill of materials BOM, etc.
[0088] Cost data, Labor_Cost(f,x) and Energy_Cost(f,x) mentioned above are two types of stepped cost data;
[0089] Profit data, including Profit in the previous article p ;
[0090] Preset data, including w in the previous text f , MaxLoss and other data.
[0091] In practical applications, basic processing can also be performed on the input data, such as processing the data structure so that the structure of the processed input data meets the requirements of the model, and performing necessary business logic processing, such as BOM expansion, process route capacity occupancy calculation, etc. The specific business logic processing can be implemented with reference to the existing technology, and this embodiment will not be described in detail here.
[0092] like Figure 3 As shown, after data acquisition and basic data processing, a mixed integer programming model that satisfies multi-dimensional break-even constraints, production capacity constraints, material constraints, and demand satisfaction constraints and takes profit maximization as the goal can be constructed based on the input data, and decision variables, constraints, and objective functions can be constructed.
[0093] In some embodiments, the step S220 of constructing a target model based on the input data includes:
[0094] The objective function is constructed according to the profit data, the factory penalty coefficient and the decision variables.
[0095] Specifically, according to the profit data Profit of the target product p and the product quantity variable x f,p,m Get the total profit data of the multiple factories
[0096] According to the cost data of the target product Labor_Cost(f,x), Energy_Cost(f,x) and the product quantity variable x f,p,m Obtain the total cost data of the multiple factories
[0097]
[0098] According to the profit and loss slack variable s of each factory f,m and the factory penalty coefficient w f Get the total profit and loss penalty items of the multiple factories
[0099] An objective function is constructed by using the total profit data of the multiple factories, the total cost data of the multiple factories and the total profit and loss penalty items of the multiple factories.
[0100] In some possible implementations of this embodiment, the objective function Z is constructed according to the following formula (1).
[0101]
[0102] In some embodiments, the multiple constraints include a multi-dimensional break-even constraint and a multi-dimensional supply constraint for constraining the break-even of the multiple factories, wherein the multi-dimensional break-even constraint includes a break-even constraint for each factory and a total loss constraint for the multiple factories.
[0103] In some possible implementation schemes of this embodiment, the break-even constraint of each factory is constructed by using the product quantity variable of the target product produced by each factory, the cost data of the target product, the profit data of the target product, and the profit and loss slack variable of each factory. The break-even constraint of each factory is used to constrain that the difference between the profit and cost of each factory must be greater than or equal to zero, and slack is allowed.
[0104] Optionally, the mathematical expression of the break-even constraint of each factory is as shown in the following formula (2):
[0105]
[0106] In some other possible implementation schemes of this embodiment, the total profit and loss slack variables of multiple factories are obtained through the profit and loss slack variables of each factory, and the total loss constraints of the multiple factories are constructed according to the total profit and loss slack variables of the multiple factories and the total loss data allowed by the multiple factories. The total loss constraints of the multiple factories are used to constrain the total losses of all factories not to exceed the allowed total loss data.
[0107] Optionally, the mathematical expression of the total loss constraint of multiple factories is shown in the following formula (3):
[0108]
[0109] In some embodiments, the supply constraint condition includes at least one of the following:
[0110] Demand satisfaction constraint, used to constrain the production volume of the target product to meet the demand plan;
[0111] Capacity constraints, used to constrain the production of each factory to not exceed the upper capacity of the factory;
[0112] The material supply constraint is used to constrain that the total material demand of the multiple factories should not exceed the total supply.
[0113] The mathematical expression of the requirement satisfaction constraint is shown in the following formula (4):
[0114]
[0115] The mathematical expression of capacity constraint is shown in the following formula (5):
[0116]
[0117] The mathematical expression of material supply constraint is shown in the following formula (6):
[0118]
[0119] According to the multi-factory production plan optimization method of the above embodiment of the present disclosure, the technical solution of the embodiment of the present disclosure has at least the following advantages:
[0120] First, the existing business model is improved. In the oversupply scenario, the target model of the embodiment of the present disclosure can provide more feasible planning results;
[0121] Second, the break-even point is taken into account in the objective function to help enterprises make decisions on their profit and loss situations under different product production structures;
[0122] Third, the dynamic profit and loss balance method can help enterprises make more scientific decisions on the production plan of each factory, which is more in line with the business conditions of the enterprise than simply setting the minimum production volume of the factory. A single minimum production volume cannot reflect the impact of different types of production products on the profit and loss balance;
[0123] Fourth, a multi-objective and multi-constraint optimization method is used to help enterprises better perform supply and demand balance calculations, improve the work efficiency of planners, improve the business operations of the enterprise, and make better business decisions in complex scenarios.
[0124] Reference now Figure 4 . Figure 4 FIG. 4 is a schematic diagram showing a structure of a production plan optimization device 400 for multiple factories according to some embodiments of the present disclosure. The production plan optimization device 400 for multiple factories can be implemented on one device or on distributed devices. Figure 4 As shown, the multi-factory production plan optimization device 400 includes at least an acquisition unit 410, a modeling unit 420 and a solution unit 430, wherein:
[0125] An acquisition unit 410 is used to acquire input data, wherein the input data is relevant data of target products produced by multiple factories, and the input data includes cost data of the target products, wherein the cost data refers to cost ladder data based on the number of products;
[0126] A modeling unit 420, configured to construct a target model based on the input data, wherein the target model includes multiple constraints based on decision variables and an objective function, wherein the decision variables include a product quantity variable for each factory producing the target product and a profit and loss slack variable for each factory, the multiple constraints are used to constrain the profit and loss balance of the multiple factories, and the objective function is used to indicate an optimization target of a production plan for the target product;
[0127] The solving unit 430 is used to solve the target model to obtain the optimization result of the multi-factory production plan.
[0128] In some embodiments, the input data also includes profit data of the target product and a factory penalty coefficient, wherein the factory penalty coefficient is used to control the profit and loss relaxation cost of the factory, and the modeling unit 420 is used to construct the objective function based on the profit data, the factory penalty coefficient and the decision variables.
[0129] In some embodiments, the modeling unit 420 is used to obtain the total profit data of the multiple factories based on the profit data of the target product and the product quantity variable; obtain the total cost data of the multiple factories based on the cost data of the target product and the product quantity variable; obtain the total profit and loss penalty items of the multiple factories based on the profit and loss slack variable of each factory and the factory penalty coefficient; and construct an objective function through the total profit data of the multiple factories, the total cost data of the multiple factories, and the total profit and loss penalty items of the multiple factories.
[0130] In some embodiments, the input data also includes the total loss data allowed by the multiple factories, the multiple constraints include the break-even constraint of each factory and the total loss constraint of the multiple factories, and the modeling unit 420 is used to construct the break-even constraint of each factory through the product quantity variable of the target product produced by each factory, the cost data of the target product, the profit data of the target product, and the profit and loss slack variables of each factory; obtain the total profit and loss slack variables of multiple factories through the profit and loss slack variables of each factory, and construct the total loss constraints of the multiple factories according to the total profit and loss slack variables of the multiple factories and the total loss data allowed by the multiple factories.
[0131] In some embodiments, the multiple constraints further include a supply constraint, and the supply constraint includes at least one of the following:
[0132] Demand satisfaction constraint, used to constrain the production volume of the target product to meet the demand plan;
[0133] Capacity constraints, used to constrain the production of each factory to not exceed the upper capacity of the factory;
[0134] The material supply constraint is used to constrain that the total material demand of the multiple factories should not exceed the total supply.
[0135] In some embodiments, the input data also includes demand planning data, production capacity data, supply capacity data and process data of the target product. The modeling unit 420 is used to construct the demand satisfaction constraint through the product quantity variables and demand planning data of each factory producing the target product; to construct the production capacity constraint through the product quantity variables and production capacity data of each factory producing the target product; and to construct the material supply constraint through the product quantity variables, supply capacity data and process data of each factory producing the target product.
[0136] In some embodiments, the target model is constructed according to a preset algorithm, which is one of a linear programming algorithm, an integer programming algorithm, and a mixed integer programming algorithm. The solving unit 430 is used to solve the target model by calling a dedicated solver.
[0137] It can be understood that the above-mentioned production planning optimization device for multiple factories can implement the various steps of the production planning optimization method for multiple factories provided in the aforementioned embodiments, and the relevant explanations about the production planning optimization method for multiple factories are applicable to the production planning optimization device for multiple factories, which will not be repeated here.
[0138] Figure 5 is a simplified block diagram of a device 500 suitable for implementing embodiments of the present disclosure. Figure 5 As shown, the device 500 includes one or more processors 510 , one or more memories 520 coupled to the processors 510 , and one or more communication modules 540 coupled to the processors 510 .
[0139] The communication module 540 is used for two-way communication. The communication module 540 has at least one antenna to facilitate communication. The communication interface may represent any interface necessary for communicating with other network elements.
[0140] Processor 510 may be of any type suitable for the local technology network, and may include, as non-limiting examples, one or more of: a general purpose computer, a special purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Device 500 may have multiple processors, such as application specific integrated circuit chips, which are driven in time to a clock that synchronizes a master processor.
[0141] The memory 520 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 524, electrically programmable read-only memory (EPROM), flash memory, hard disk, optical disc (CD), digital video disc (DVD), and other magnetic and / or optical memories. Examples of volatile memories include, but are not limited to, random access memory (RAM) 522 and other volatile memories that do not persist during a power outage duration.
[0142] The computer program 530 includes computer-executable instructions to be executed by the associated processor 510. The program 530 may be stored in the ROM 524. The processor 510 may execute any appropriate actions and processes by loading the program 530 into the RAM 522.
[0143] Embodiments of the present disclosure may be implemented by the program 530 such that the device 500 may execute any of the processes discussed with reference to Figure 2 Embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.
[0144] In some embodiments, the program 530 may be tangibly embodied in a computer-readable medium, which may be included in the device 500 (e.g., the memory 520) or other storage devices accessible to the device 500. The device 500 may load the program 530 from the computer-readable medium into the RAM 522 for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. The program 330 is stored on the computer-readable medium.
[0145] Generally, various embodiments of the present disclosure may be implemented in hardware or in special-purpose circuits, software, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, a microprocessor, or other computing devices. Although aspects of embodiments of the present disclosure are shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, special-purpose circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.
[0146] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the above-mentioned reference Figure 2The production plan optimization method 200 shown. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of the program modules can be combined or separated between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.
[0147] The program code for executing the disclosed method can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing equipment so that when the program code is executed by the processor or controller, the function / operation specified in the flow chart and / or block diagram is realized. The program code can be executed completely on the machine as an independent software package, partially on the machine, partially on the machine, partially on a remote machine, partially on a remote machine, or all on a remote machine or server.
[0148] In the context of the present disclosure, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform various processes and operations as described above. Examples of carriers include signals, computer readable media, etc.
[0149] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or apparatuses, or any suitable combination of the foregoing. More specific examples of computer readable storage media include an electrical connection with one or more wires, a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0150] In addition, although the operations are described in a specific order, this should not be understood as requiring the specific order or sequence shown to be performed, or performing all the operations shown, to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these details should not be interpreted as limitations on the scope of the present disclosure, but can be interpreted as descriptions of features specific to a particular embodiment. Certain features described in the context of a separate embodiment may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any suitable sub-combination.
[0151] Although the disclosure has been described in language specific to structural features and / or methodological acts, it should be understood that the disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
[0152] It should be fully understood that the use of personally identifiable information should be subject to privacy policies and practices generally recognized as meeting or exceeding industry or government requirements for maintaining user privacy. In particular, personally identifiable information data should be managed and processed to minimize the risk of inadvertent or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.
Claims
1. A production planning optimization method for multiple factories, characterized in that: The method comprises: Acquire input data, wherein the input data is relevant data of target products produced by multiple factories, and the input data includes cost data of the target products, wherein the cost data refers to cost ladder data based on the quantity of products; Building a target model based on the input data, the target model includes multiple constraints based on decision variables and an objective function, wherein the decision variables include a product quantity variable for each factory producing the target product and a profit and loss slack variable for each factory, the multiple constraints are used to constrain the profit and loss balance of the multiple factories, and the objective function is used to indicate an optimization target of a production plan for the target product; The target model is solved to obtain an optimization result of the multi-factory production plan.
2. The method according to claim 1, characterized in that: The input data also includes profit data of the target product and a factory penalty coefficient, wherein the factory penalty coefficient is used to control the profit and loss relaxation cost of the factory. The target model is constructed based on the input data, including: The objective function is constructed according to the profit data, the factory penalty coefficient and the decision variables.
3. The method according to claim 2, characterized in that: The constructing the objective function according to the profit data, the factory penalty coefficient and the decision variables comprises: Acquire total profit data of the plurality of factories according to the profit data of the target product and the product quantity variable; Acquire total cost data of the plurality of factories according to the cost data of the target product and the product quantity variable; Obtaining a total profit and loss penalty item for the plurality of factories according to the profit and loss slack variable of each factory and the factory penalty coefficient; An objective function is constructed by using the total profit data of the multiple factories, the total cost data of the multiple factories and the total profit and loss penalty items of the multiple factories.
4. The method according to claim 2, characterized in that: The input data also includes total loss data allowed by the multiple factories, the multiple constraints include a break-even constraint for each factory and a total loss constraint for the multiple factories, and the method further includes: Constructing the break-even constraint of each factory through the product quantity variable of the target product produced by each factory, the cost data of the target product, the profit data of the target product and the break-even slack variable of each factory; The total profit and loss slack variables of the multiple factories are obtained through the profit and loss slack variables of each factory, and the total loss constraints of the multiple factories are constructed according to the total profit and loss slack variables of the multiple factories and the total loss data allowed by the multiple factories.
5. The method according to claim 4, characterized in that: The multiple constraints also include a supply constraint, and the supply constraint includes at least one of the following: Demand satisfaction constraint, used to constrain the production volume of the target product to meet the demand plan; Capacity constraints, used to constrain the production of each factory to not exceed the upper capacity of the factory; The material supply constraint is used to constrain that the total material demand of the multiple factories should not exceed the total supply.
6. The method according to claim 5, characterized in that: The input data also includes demand planning data, production capacity data, supply capacity data and process data of the target product, and the method further includes: The demand satisfaction constraint is constructed by using the product quantity variables and demand planning data of each factory producing the target product; The capacity constraint is constructed by using the product quantity variables and capacity data of each factory producing the target product; The material supply constraints are constructed by using product quantity variables, supply capacity data and process data of each factory producing the target product.
7. The method according to any one of claims 1 to 6, characterized in that: The target model is constructed according to a preset algorithm, which is one of a linear programming algorithm, an integer programming algorithm, and a mixed integer programming algorithm. Solving the target model includes: The target model is solved by calling a dedicated solver.
8. A production planning optimization device for multiple factories, characterized in that: The device comprises: An acquisition unit, configured to acquire input data, wherein the input data is relevant data of target products produced by multiple factories, and the input data includes cost data of the target products, wherein the cost data refers to step-by-step cost data based on the quantity of products; A modeling unit, configured to construct a target model based on the input data, wherein the target model includes multiple constraints based on decision variables and an objective function, wherein the decision variables include a product quantity variable for each factory producing the target product and a profit and loss slack variable for each factory, the multiple constraints are used to constrain the profit and loss balance of the multiple factories, and the objective function is used to indicate an optimization target of a production plan for the target product; A solving unit is used to solve the target model to obtain an optimization result of the multi-factory production plan.
9. An electronic device, characterized in that: The electronic device comprises: one or more processors; and One or more memories coupled to the one or more processors and storing instructions thereon, which, when the instructions are executed individually or collectively by the one or more processors, enable the electronic device to execute the multi-factory production planning optimization method of any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing machine-executable instructions, which, when executed individually or collectively by one or more processors of a machine, enable the machine to execute the multi-factory production planning optimization method of any one of claims 1 to 7.