Production planning optimization method based on mixed integer model and related equipment

Abstract

The embodiment of the invention belongs to the technical field of production planning, and relates to a mixed integer model-based production planning optimization method, which comprises the following steps of: obtaining production basic data corresponding to a to-be-optimized production line; constructing a production planning optimization model according to the production basic data and preset model configuration information; and solving the production planning optimization model according to a preset solver to obtain a target production plan corresponding to the to-be-optimized production line. The invention further provides a production planning optimization device based on the mixed integer model, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and production basic data and model configuration information can be stored in a block chain. According to the method, the production planning optimization model is constructed by acquiring the production basic data of the to-be-optimized production line and combining the preset model configuration information, and the model is solved by using the preset solver, so that different production requirements and resource constraints can be effectively balanced, and the accuracy and efficiency of production scheduling are improved.

Description

Technical Field

[0001] The present application relates to the technical field of production planning, and in particular to a production planning optimization method based on a mixed integer model and related equipment. Background Art

[0002] In recent years, the home appliance industry, a pillar of my country's manufacturing sector, has demonstrated three key trends: intelligence, green manufacturing, and customization. However, modern home appliance production planning still faces numerous challenges, such as poor coordination among factories, resource waste during new product launches, and complex production scheduling.

[0003] Currently, to address these challenges and optimize production planning, the mainstream approach adopted by the home appliance industry is heuristic scheduling and rule-driven scheduling. However, traditional heuristic scheduling or rule-driven scheduling generally relies on manual experience or fixed rules, lacking flexibility in the face of emergencies, making it difficult to make dynamic adjustments, and struggling to handle complex production plans. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to propose a production planning optimization method and related equipment based on a mixed integer model to solve the technical problem of low production planning optimization efficiency caused by insufficient flexibility of scheduling methods based on heuristic scheduling and rule-driven scheduling.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a production planning optimization method based on a mixed integer model, which adopts the following technical solutions:

[0006] A production planning optimization method based on a mixed integer model comprises the following steps:

[0007] Obtain basic production data corresponding to the production line to be optimized;

[0008] Constructing a production planning optimization model based on the production basic data and preset model configuration information;

[0009] The production planning optimization model is solved according to a preset solver to obtain the target production plan corresponding to the production line to be optimized.

[0010] Furthermore, the step of constructing a production planning optimization model based on the production basic data and the preset model configuration information specifically includes:

[0011] Preprocessing the production basic data to obtain the set and parameters corresponding to the production planning optimization model;

[0012] Determining decision variables, constraints, and objective functions of the production planning optimization model based on the set, the parameters, and the model configuration information;

[0013] A mixed integer model is established according to the decision variables, the constraint conditions and the objective function to obtain the production planning optimization model.

[0014] Furthermore, the optimization goal of the objective function is to minimize the comprehensive cost corresponding to the production line to be optimized, and the comprehensive cost includes order delay cost, forecast delay and cancellation cost, production cost, capacity utilization cost, inventory cost, procurement cost, transportation cost and replacement cost.

[0015] Furthermore, the constraints include: inventory balance constraints, alternative material constraints, delivery constraints, capacity limit constraints, production logic constraints, special scheduling constraints, minimum / maximum production volume constraints, and new product production constraints.

[0016] Furthermore, the inventory balance constraint represents the daily ending inventory amount;

[0017] The alternative material constraint means that if a product has an alternative material group, the production volume of the product is equal to the usage volume of the material group;

[0018] The delivery constraint represents the order delivery quantity and the forecast delivery quantity;

[0019] The capacity limit constraint represents the upper limit of the capacity of the production line to be optimized;

[0020] The production logic constraint represents that the total production volume of the factory is the sum of the production volumes of the production lines to be optimized;

[0021] The special scheduling constraint means that the first product and the second product must be produced on the same day or cannot be produced on the same day;

[0022] The minimum / maximum production constraint represents that the daily production volume is greater than the minimum production capacity and less than the maximum production capacity;

[0023] The new product production constraints include new product production days restriction, new product production quantity matching constraint, and new product production logic constraint.

[0024] Furthermore, after the step of solving the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized, the method further includes:

[0025] When the update information corresponding to the production basic data is received, the production planning optimization model is updated according to the update information, and the step of solving the production planning optimization model according to the preset solver is returned to obtain the target production plan corresponding to the production line to be optimized.

[0026] Furthermore, it is characterized in that, after the step of solving the production planning optimization model according to the preset solver to obtain the target production plan corresponding to the production line to be optimized, it also includes:

[0027] The production basic data and the model configuration information are stored in a preset blockchain node.

[0028] In order to solve the above technical problems, the embodiment of the present application further provides a production planning optimization device based on a mixed integer model, which adopts the following technical solution:

[0029] A production planning optimization device based on a mixed integer model, comprising:

[0030] An acquisition module is used to obtain basic production data corresponding to the production line to be optimized;

[0031] A model building module, used to build a production planning optimization model based on the production basic data and preset model configuration information;

[0032] The model solving module is used to solve the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized.

[0033] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0034] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the production planning optimization method based on the mixed integer model are implemented.

[0035] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0036] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the production planning optimization method based on a mixed integer model as described above.

[0037] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0038] The production planning optimization method based on the mixed integer model disclosed in the present application obtains the basic production data corresponding to the production line to be optimized; constructs a production planning optimization model based on the production basic data and preset model configuration information; solves the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized. The present application obtains the basic production data of the production line to be optimized, and constructs a production planning optimization model in combination with the preset model configuration information, and then solves the model using a preset solver, thereby obtaining the target production plan, thereby effectively balancing different production demands and resource constraints, and improving the accuracy and efficiency of production scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0041] Figure 2 is a flowchart of an embodiment of a production planning optimization method based on a mixed integer model according to the present application;

[0042] Figure 3 1 is a schematic structural diagram of an embodiment of a production planning optimization device based on a mixed integer model according to the present application;

[0043] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0045] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0046] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0047] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0048] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0049] Terminal devices 101, 102, and 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, and the like.

[0050] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0051] It should be noted that the production planning optimization method based on the mixed integer model provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the production planning optimization device based on the mixed integer model is generally set in the server / terminal device.

[0052] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0053] Continue to refer Figure 2 , shows a flow chart of an embodiment of a production planning optimization method based on a mixed integer model according to the present application. The production planning optimization method based on a mixed integer model comprises the following steps:

[0054] Step S201: Obtain basic production data corresponding to the production line to be optimized.

[0055] In this embodiment, the production planning optimization method based on the mixed integer model is run on the electronic device (eg Figure 1 The server / terminal device shown in the figure can send or receive data via a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other wireless connection methods currently known or to be developed in the future.

[0056] In this embodiment, the goal is to collect and organize all necessary basic data related to the production line to be optimized. This data includes basic information about the production line, such as the equipment configuration of the production line, the production capacity of each production line, the factory shift schedule, production time, production demand, etc. This data provides the necessary input for subsequent production planning optimization, ensuring that the model construction reflects the actual production situation. Specifically, basic production data includes but is not limited to: order demand data, production capacity information of each production line, product production cycle, resource usage, production task priority, etc. This step is the prerequisite for the entire optimization process and ensures the effectiveness and accuracy of subsequent optimization.

[0057] Step S202: constructing a production planning optimization model based on the production basic data and preset model configuration information.

[0058] In this embodiment, after obtaining the basic data, this step combines this data with preset model configuration information to construct a production planning optimization model. This model uses a mixed integer programming (MIP) method and can simultaneously process continuous and discrete variables to solve multiple constraints and target optimization problems in production scheduling. The model configuration information includes various business rules and constraints that need to be followed during the production process, such as production line capacity limitations, inventory constraints, order delivery time, changeover costs, minimum and maximum production volumes, etc. Through these constraints, the model can reflect the various limitations and conditions in the real production environment, ensuring that the optimization plan can not only achieve production goals but also meet the actual operational requirements of the factory. The establishment of this model provides a scientific basis for the optimization solution of production planning.

[0059] Step S203: Solve the production planning optimization model according to a preset solver to obtain a target production plan corresponding to the production line to be optimized.

[0060] In this embodiment, after the model is built, the model is solved using a preset solver to obtain the optimal production planning solution. The solver (such as COPT, CPLEX, etc.) will calculate the optimal solution that meets all constraints based on the input basic data, constraints and objective function through a mathematical optimization algorithm. The optimization goals usually include minimizing production costs, maximizing capacity utilization, reducing delivery delays, etc. During the solution process, the solver will automatically adjust various production plans and generate specific production scheduling plans for each production line, including task arrangements, production cycles, and changeover arrangements for each production line. Ultimately, the solution result will provide an optimal production plan to guide the scheduling of production lines and the allocation of production tasks, thereby improving overall production efficiency and ensuring on-time delivery.

[0061] This application obtains the basic production data of the production line to be optimized, and combines it with the preset model configuration information to construct a production planning optimization model, and then uses the preset solver to solve the model to obtain the target production plan, thereby effectively balancing different production needs and resource constraints, and improving the accuracy and efficiency of production scheduling.

[0062] In some optional implementations of this embodiment, the step of constructing a production planning optimization model based on the production basic data and the preset model configuration information specifically includes:

[0063] Preprocessing the production basic data to obtain the set and parameters corresponding to the production planning optimization model;

[0064] Determining decision variables, constraints, and objective functions of the production planning optimization model based on the set, the parameters, and the model configuration information;

[0065] A mixed integer model is established according to the decision variables, the constraint conditions and the objective function to obtain the production planning optimization model.

[0066] In this embodiment, the acquired basic production data is first preprocessed to ensure its accuracy, completeness, and applicability. Preprocessing operations may include data cleaning, missing value filling, and outlier identification and correction. The purpose of this process is to convert the raw data into a format and structure suitable for constructing a production planning optimization model. After preprocessing, the resulting sets and parameters are used for subsequent model construction. A set generally refers to the collection of different elements involved in the optimization model, such as different types of production lines, products, equipment, and processes. Parameters are quantitative information related to the production line and production process, such as production cycle, production capacity, cost, delivery date, and material consumption. Parameters are numerical inputs into the model that influence the calculation of optimization results. Then, based on the obtained sets, parameters, and model configuration information, the core elements of the production planning optimization model are determined: decision variables, constraints, and the objective function. Decision variables are unknown quantities that need to be determined in the optimization model. For example, the allocation of production tasks to different production lines, the production batch size of a product, and the operating status of the equipment are all decision variables. In mixed integer programming, decision variables may include binary variables (such as whether to select a certain piece of equipment) and continuous variables (such as production volume or processing time). Constraints are restrictions on decision variables that ensure that the optimization results meet the physical and operational requirements of actual production. Common constraints include capacity limits (maximum production capacity of each production line), inventory balance constraints (ensuring inventory levels are within a reasonable range), and delivery constraints (ensuring on-time delivery). The objective function defines the model's optimization goal, typically achieved by minimizing or maximizing certain metrics. For example, the optimization goal might be to minimize total production costs, minimize delivery delays, or maximize capacity utilization. The objective function is constructed by the relationship between sets, parameters, and decision variables to form the model's ultimate optimization goal. After defining the decision variables, constraints, and objective function, a mixed integer programming model is constructed based on these elements. The mixed integer model is a mathematical optimization model that can handle decision variables including integers and continuous variables and model the actual production process through constraints. This model can optimize the objective function while satisfying all constraints to obtain the optimal production plan. The actual optimization problem is converted into a mathematical expression. The solver (such as COPT, CPLEX, etc.) will use these expressions to solve and ultimately provide an optimal production planning solution. Model establishment is the core part of the entire production planning optimization process, ensuring that various production conditions and goals can be comprehensively considered, thereby providing an efficient production plan that meets actual operational requirements.

[0067] This application preprocesses the basic production data and generates the required sets and parameters based on these data. Then, according to the preset model configuration information, it determines the decision variables, constraints and objective functions in the production planning optimization model, and finally constructs a complete optimization model, ensuring the accuracy of the model input and avoiding deviations in the optimization results caused by inconsistent or inaccurate data.

[0068] In some optional implementations of this embodiment, the optimization goal of the above-mentioned objective function is to minimize the comprehensive cost corresponding to the production line to be optimized, and the above-mentioned comprehensive cost includes order delay cost, forecast delay and cancellation cost, production cost, capacity utilization cost, inventory cost, procurement cost, transportation cost and replacement cost.

[0069] In this example, the optimization objective is clearly defined: minimizing the overall cost of the production line by adjusting the production plan. The goal is to reduce various costs throughout the production process through precise production scheduling and resource allocation, while satisfying all production and delivery constraints. These cost reductions not only improve production efficiency but also enhance the company's competitiveness. By setting this objective function within a mixed-integer model, the system automatically optimizes costs at each stage to ensure the most cost-effective production plan.The comprehensive costs include the following: Order delay costs refer to the additional costs incurred due to failure to complete production tasks and deliver orders on time. This may include customer fines, loss of reputation, and possible order cancellations due to delayed delivery. This cost reflects the importance of production scheduling in meeting delivery deadlines. Forecast delay and cancellation costs refer to the additional costs caused by inaccurate forecasts, changes in order quantities, or changes in production plans. For example, if the production plan fails to accurately anticipate changes in market demand, it may lead to overproduction or underproduction, resulting in waste of resources or delayed delivery of orders. This cost covers the economic losses caused by plan changes. Production costs generally refer to various expenses directly related to the production process, including raw material costs, labor costs, energy consumption, equipment depreciation, and maintenance costs. Production costs are usually the most important component of total costs, and optimizing production scheduling can effectively reduce these costs. Capacity utilization costs refer to the costs incurred due to the underutilization or overuse of production line resources (such as equipment, workers, etc.). If the production line fails to operate within the optimal production capacity, it will affect the economic benefits of the production line, resulting in waste of resources or overload, thereby increasing equipment loss and maintenance costs. Inventory costs refer to the related expenses incurred by the enterprise to maintain a certain inventory level, including Storage fees, insurance premiums, capital tie-up costs, etc. Excessive inventory not only increases storage costs but can also cause products to become obsolete or expired, impacting cash flow and product quality. Insufficient inventory can lead to production interruptions and delivery delays. Therefore, controlling inventory costs is a key goal in production scheduling. Procurement costs refer to the expenses required to purchase materials such as raw materials and parts. These costs are affected by factors such as market fluctuations, supply chain management efficiency, and procurement timing. Optimizing production plans can not only reduce procurement costs but also ensure timely procurement, thereby avoiding production delays caused by raw material shortages or price fluctuations. Transportation costs refer to the expenses incurred in transporting raw materials, semi-finished products, or finished products from one location to another. Transportation costs include not only the transportation costs themselves but also delays or additional expenses caused by improper transportation arrangements. Proper planning of transportation and delivery arrangements can effectively reduce transportation costs. Substitution costs refer to the additional expenses incurred in using alternative materials or production processes in certain situations. If certain raw materials or production processes for a product are unavailable, alternative materials or equipment may need to be used, and these alternatives may result in increased costs. For example, using lower-quality raw materials may affect product quality, or using uncommon production processes may increase production time and costs.

[0070] By comprehensively considering multiple cost factors, this application model can reduce various expenses and improve economic benefits while ensuring production needs.

[0071] In some optional implementations of this embodiment, the above constraints include: inventory balance constraints, alternative material constraints, delivery constraints, capacity limit constraints, production logic constraints, special scheduling constraints, minimum / maximum production volume constraints, and new product production constraints.

[0072] In this embodiment, the inventory balance constraint ensures that the production plan maintains a reasonable daily inventory level. Specifically, inventory changes are determined by production volume, delivery volume, transportation inflows, and outflows. This constraint ensures that production lines do not suffer from inefficiencies or resource waste due to excessive or insufficient inventory. The substitute material constraint addresses material substitution during product production. This means that when a material becomes unavailable, a pre-set set of substitute materials can be used to continue production. This constraint ensures that the production plan can be adjusted based on actual material availability, preventing production interruptions due to material shortages. The delivery constraint ensures that orders can be delivered to customers within the scheduled timeframe. This constraint ensures that production scheduling meets customer delivery requirements. The capacity limit constraint ensures that the maximum production capacity of each production line is not exceeded during production. This constraint typically calculates the production volume of each production line multiplied by the unit consumption time to limit production capacity. This prevents excessive concentration of production tasks on a single line or during a specific period, which could lead to equipment overload. Production logic constraints ensure that the various operations and arrangements in the production process conform to the actual production process sequence. For example, certain processes must be completed before others, and certain products must be produced on specific production lines. Special scheduling constraints mean that, in certain situations, the production plan must meet specific production scheduling requirements. Minimum / maximum production constraints ensure that the production volume of each product during the production process does not fall below the predetermined minimum production volume and does not exceed the predetermined maximum production volume. This constraint helps control production scale, avoiding overproduction leading to inventory backlogs or underproduction leading to order delays or customer churn. New product production constraints ensure that new product production can be completed within the specified timeframe and meet production requirements. This is especially true for new products in the pilot production phase, where production plans must adhere to a series of special rules.

[0073] Specifically, the capacity limit constraint can be expressed as:

[0074]

[0075] Production logic constraints can be expressed as:

[0076]

[0077] The special scheduling rule - product i and product p must be produced on the same day can be expressed as:

[0078]

[0079] TC means that products can be produced simultaneously on the same day.

[0080] Special scheduling rule - product i and product p cannot be produced on the same day can be expressed as:

[0081]

[0082] NC said it can produce products simultaneously and on the same day.

[0083] The maximum and minimum production can be expressed as:

[0084] Indicates the minimum production batch of the product;

[0085] Indicates the maximum production batch of the product;

[0086] New product production can be expressed as:

[0087] New products must be completed within 7 consecutive days, and each day can only be considered as a certain day.

[0088]

[0089] The production matching constraint can be expressed as:

[0090]

[0091] Production logic constraints can be expressed as:

[0092]

[0093] Among them, X (i,p,t) : the quantity of product i produced by factory p in period t (continuous variable);

[0094] XS (i,k,t) : the number of product i produced by production line k in period t (integer variable);

[0095] Binary variable, whether production line k produces product i|bundle in cycle t);

[0096] Binary variable, whether production line k produces product i|bundle in cycle t);

[0097] YS (i,k,t) : Binary variable, indicating whether production line k produces product i in cycle t;

[0098] C (k,t) : the capacity usage of production line k in period t (continuous variable);

[0099] US (i,p,t): usage of material group i in factory p during period t (continuous variable);

[0100] Y (i,p1,p2,t) : the number of product i shipped from plant p1 to p2 in period t (continuous variable);

[0101] Pr (i,p,t) : the purchase quantity of raw material i in factory p in period t (continuous variable);

[0102] W (i,t) : predicted cancellation volume of product i in period t (continuous variable);

[0103] V (i,t) : The predicted delay of product i in period t (continuous variable);

[0104] N (i,p,t) : Inventory quantity of product i in factory p during period t (continuous variable).

[0105] This application ensures the feasibility of the optimization results in actual production by comprehensively considering different types of constraints.

[0106] In some optional implementations of this embodiment, the above inventory balance constraint represents the end-of-day inventory amount;

[0107] The above-mentioned alternative material constraint means that if an alternative material group exists for a product, the production volume of the said product is equal to the usage volume of the material group;

[0108] The above delivery constraints represent the order delivery quantity and the forecast delivery quantity;

[0109] The above capacity limit constraint represents the upper limit of the capacity of the production line to be optimized;

[0110] The above production logic constraint means that the total production volume of the factory is the sum of the production volumes of the production lines to be optimized;

[0111] The above special scheduling constraint means that the first product and the second product must be produced on the same day or cannot be produced on the same day;

[0112] The above minimum / maximum production constraints mean that the daily production volume is greater than the minimum capacity and less than the maximum capacity;

[0113] The above new product production constraints represent the new product production day limit, new product production volume matching constraint, and new product production logic constraint.

[0114] In this embodiment, the inventory balance constraint ensures that daily inventory levels match actual production conditions, preventing excessive inventory fluctuations from impacting production schedules or supply chain management. Specifically, this constraint calculates the inventory level at the end of each day. Inventory fluctuations depend on the day's production, delivery, and material inflows and outflows. In this way, the model ensures effective inventory management, reducing excess inventory and stock-outs. The substitute material constraint ensures that when raw materials are insufficient, production can continue using a predefined substitute material group, and the production volume must be equal to the usage of the substitute material. This constraint helps the model flexibly adjust the production plan in the event of material shortages. If a product has a substitute material group, the product's production volume must be equal to the usage of that material group. This constraint ensures flexible material supply substitution during the production process, ensuring uninterrupted production. The delivery constraint ensures that the delivery volume of an order is consistent with the forecasted delivery volume. The delivery constraint takes into account not only the actual delivery volume but also the forecasted delivery volume. These constraints ensure that the production plan meets customer delivery requirements. Through this constraint, the system can accurately predict and adjust delivery times to avoid delivery delays. The capacity limit constraint ensures that the production plan does not exceed the maximum capacity of the production line. This constraint sets a maximum output limit for each production line, ensuring that production output does not exceed the line's capacity. This constraint ensures that the production process does not lead to equipment overload or resource waste, balancing production capacity with actual demand. Production logic constraints ensure that the production plan conforms to actual production process requirements and sequencing. For example, the total production output of a factory should be equal to the sum of the production outputs of all production lines to be optimized. This means that the coordination between different production lines requires consistency through logic constraints. Specifically, the total production output of the factory is the sum of the production outputs of all production lines. This constraint ensures the coordination of different production lines and ensures that the total production volume is consistent with the production capacity and scheduling plan of each line. Special scheduling constraints involve production scheduling requirements for certain products. For example, certain products must be produced on the same day, or must not be produced on the same day. Specifically, they must be produced on the same day: Products 1 and 2 must be produced on the same day, perhaps due to product combination production requirements. Products 1 and 2 cannot be produced on the same day: Products 1 and 2 cannot be produced on the same day, perhaps due to resource conflicts or production process limitations. This constraint ensures that the production plan is feasible in practice and can meet special production scheduling requirements. Minimum / maximum production constraints ensure that daily production falls within a predetermined range, preventing overproduction or underproduction. Specifically, daily production must be greater than the minimum production capacity and less than the maximum production capacity. For certain specialized products, the minimum production quantity can be set to 1, allowing for small batches. These constraints ensure production flexibility and avoid inventory backlogs or resource waste caused by over- or under-production. New product production constraints encompass multiple aspects, ensuring a smooth transition from pilot production to full-scale production.Specifically, there are the following constraints: New product production day limit: Production of new products must be completed within a specified number of days, and can only be produced once a day, typically for seven consecutive days. New product production volume matching constraint: The total production volume of a new product is equal to the cumulative sum of daily production volumes. New product production logic constraint: New product production must proceed according to plan. If a production line is unavailable, the production schedule is postponed. These constraints help ensure a smooth transition to new product production and avoid quality issues or resource waste caused by improper production.

[0115] This application further clarifies how to control various parameters in the production process through specific constraints such as inventory balance constraints, alternative material constraints, and delivery constraints. These constraints provide detailed operating standards for the optimization model and ensure the feasibility of the production plan.

[0116] In some optional implementations of this embodiment, after the step of solving the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized, the following steps are further included:

[0117] When the update information corresponding to the production basic data is received, the production planning optimization model is updated according to the update information, and the step of solving the production planning optimization model according to the preset solver is returned to obtain the target production plan corresponding to the production line to be optimized.

[0118] In this embodiment, during the actual production process, basic production data (such as order demand, production capacity, material supply, etc.) will change over time. These changes may come from an increase or decrease in the number of orders, a failure of the production line, a change in material supply, or the influence of other external factors. When the system detects changes in these data (called "update information"), it will automatically or manually receive and process these update information. After receiving the update information, the production planning optimization model needs to be updated accordingly. The goal of this update process is to enable the original production planning optimization model to reflect the current actual production situation and ensure that the model can still generate the optimal production plan under the new data conditions. The update steps generally include the following aspects:

[0119] Data update: Input new production basic data (such as updated order information, production line information, material status, etc.) into the system to ensure that the model uses the latest input data.

[0120] Model Adjustment: Based on new input data, some model parameters, decision variables, or constraints may need to be adjusted. For example, if the order volume changes, the target production volume and delivery time may need to be replanned.

[0121] After updating the production planning optimization model, the system reruns the solution process, using the pre-set solver to re-solve the updated production planning optimization model. This process calculates a new target production plan based on the new data and constraints. Upon completion, the system returns the updated target production plan—the optimal production plan under the current constraints and production requirements. This means that scheduling, line allocation, and resource allocation in the production process are re-optimized based on the latest production baseline data, ensuring efficient and smooth production line operation.

[0122] By introducing a data update mechanism, this application can adjust the production plan in real time, quickly respond to changes that may occur in the production process, and ensure that the production plan is always consistent with actual demand.

[0123] In some optional implementations of this embodiment, after the step of solving the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized, the following steps are further included:

[0124] The production basic data and the model configuration information are stored in a preset blockchain node.

[0125] In this embodiment, a large amount of data (such as production basic data, order information, production capacity, material usage, etc.) is involved in the production planning optimization process. These data play an important role in production management and need to be guaranteed to be secure, tamper-proof and traceable. Blockchain technology can provide a decentralized, transparent and secure data storage solution. Therefore, in the present invention, the production basic data and model configuration information will be stored in the blockchain nodes to ensure the integrity and reliability of the data. Blockchain is a distributed database technology in which data is stored on multiple nodes, each of which stores the same copy of the data. Encryption technology is used to ensure the security and tamper-proof of the data. In this step, the production basic data and model configuration information will be sent and stored to the nodes in the pre-set blockchain network. The blockchain node is part of the blockchain system and is responsible for data storage and verification. The blockchain nodes can be distributed in different locations and form a decentralized database through network collaboration to ensure data consistency and reliability.

[0126] This application uses blockchain technology to store data, which can ensure that all information in the production process cannot be tampered with, increase the transparency and traceability of the production process, and provide guarantees for future quality monitoring and auditing.

[0127] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned production basic data and the above-mentioned model configuration information, the above-mentioned production basic data and the above-mentioned model configuration information can also be stored in a node of a blockchain.

[0128] The blockchain referred to in this application is a new application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0129] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0130] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0131] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0132] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0133] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a production planning optimization device based on a mixed integer model. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0134] like Figure 3 As shown, the production planning optimization device 300 based on the mixed integer model described in this embodiment includes: an acquisition module 301, a model construction module 302 and a model solution module 303.

[0135] An acquisition module 301 is used to acquire basic production data corresponding to the production line to be optimized;

[0136] A model building module 302 is used to build a production planning optimization model based on the production basic data and preset model configuration information;

[0137] The model solving module 303 is used to solve the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized.

[0138] The production planning optimization device based on the mixed integer model provided in this application obtains the basic production data of the production line to be optimized and constructs a production planning optimization model in combination with preset model configuration information. The model is then solved using a preset solver to obtain the target production plan, thereby effectively balancing different production demands and resource constraints and improving the accuracy and efficiency of production scheduling.

[0139] In some optional implementations of this embodiment, the model building module 302 is further configured to:

[0140] Preprocessing the production basic data to obtain the set and parameters corresponding to the production planning optimization model;

[0141] Determining decision variables, constraints, and objective functions of the production planning optimization model based on the set, the parameters, and the model configuration information;

[0142] A mixed integer model is established according to the decision variables, the constraint conditions and the objective function to obtain the production planning optimization model.

[0143] The production planning optimization device based on the mixed integer model provided in the present application preprocesses the basic production data and generates the required sets and parameters based on these data. Then, according to the preset model configuration information, it determines the decision variables, constraints and objective functions in the production planning optimization model, and finally constructs a complete optimization model, thereby ensuring the accuracy of the model input and avoiding deviations in the optimization results caused by inconsistent or inaccurate data.

[0144] In some optional implementations of this embodiment, in the model building module 302, the optimization goal of the objective function is to minimize the comprehensive cost corresponding to the production line to be optimized, and the comprehensive cost includes order delay cost, forecast delay and cancellation cost, production cost, capacity utilization cost, inventory cost, procurement cost, transportation cost and replacement cost.

[0145] The production planning optimization device based on the mixed integer model provided in this application comprehensively considers multiple cost factors. The model can reduce various expenses and improve economic benefits while ensuring production needs.

[0146] In some optional implementations of this embodiment, the constraints described in the model building module 302 include: inventory balance constraints, alternative material constraints, delivery constraints, capacity limit constraints, production logic constraints, special scheduling constraints, minimum / maximum production volume constraints, and new product production constraints.

[0147] The production planning optimization device based on the mixed integer model provided in this application ensures the feasibility of the optimization results in actual production by comprehensively considering different types of constraints.

[0148] In some optional implementations of this embodiment, in the model building module 302: the inventory balance constraint represents the end-of-day inventory amount;

[0149] The alternative material constraint means that if a product has an alternative material group, the production volume of the product is equal to the usage volume of the material group;

[0150] The delivery constraint represents the order delivery quantity and the forecast delivery quantity;

[0151] The capacity limit constraint represents the upper limit of the capacity of the production line to be optimized;

[0152] The production logic constraint represents that the total production volume of the factory is the sum of the production volumes of the production lines to be optimized;

[0153] The special scheduling constraint means that the first product and the second product must be produced on the same day or cannot be produced on the same day;

[0154] The minimum / maximum production constraint represents that the daily production volume is greater than the minimum production capacity and less than the maximum production capacity;

[0155] The new product production constraints include new product production days restriction, new product production quantity matching constraint, and new product production logic constraint.

[0156] The production planning optimization device based on the mixed integer model provided in this application further clarifies how to control various parameters in the production process through these constraints by listing in detail specific constraints such as inventory balance constraints, alternative material constraints, and delivery constraints. These constraints provide detailed operating standards for the optimization model and ensure the feasibility of the production plan.

[0157] In some optional implementations of this embodiment, the model solving module 303 is further configured to:

[0158] When the update information corresponding to the production basic data is received, the production planning optimization model is updated according to the update information, and the step of solving the production planning optimization model according to the preset solver is returned to obtain the target production plan corresponding to the production line to be optimized.

[0159] The production planning optimization device based on the mixed integer model provided in this application can adjust the production plan in real time by introducing a data update mechanism, quickly respond to changes that may occur in the production process, and ensure that the production plan is always consistent with actual demand.

[0160] In some optional implementations of this embodiment, the model solving module 303 is further configured to:

[0161] The production basic data and the model configuration information are stored in a preset blockchain node.

[0162] The production planning optimization device based on the mixed integer model provided in this application stores data through blockchain technology, which can ensure that all information in the production process cannot be tampered with, increases the transparency and traceability of the production process, and provides guarantees for future quality monitoring, auditing, etc.

[0163] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0164] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0165] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0166] The memory 41 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for the production planning optimization method based on a mixed integer model. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0167] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for executing the mixed-integer model-based production planning optimization method.

[0168] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0169] The computer device provided in this application obtains the basic production data of the production line to be optimized, and combines it with preset model configuration information to construct a production planning optimization model, and then uses a preset solver to solve the model, so as to obtain the target production plan, thereby effectively balancing different production needs and resource constraints, and improving the accuracy and efficiency of production scheduling.

[0170] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the production planning optimization method based on the mixed integer model as described above.

[0171] The computer-readable storage medium provided in this application obtains the basic production data of the production line to be optimized and constructs a production planning optimization model in combination with preset model configuration information. The model is then solved using a preset solver to obtain the target production plan, thereby effectively balancing different production demands and resource constraints and improving the accuracy and efficiency of production scheduling.

[0172] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0173] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A production planning optimization method based on a mixed integer model, characterized in that: The steps include: Obtain basic production data corresponding to the production line to be optimized; Constructing a production planning optimization model based on the production basic data and preset model configuration information; The production planning optimization model is solved according to a preset solver to obtain the target production plan corresponding to the production line to be optimized.

2. The production planning optimization method based on the mixed integer model according to claim 1, characterized in that: The step of constructing a production planning optimization model based on the production basic data and the preset model configuration information specifically includes: Preprocessing the production basic data to obtain the set and parameters corresponding to the production planning optimization model; Determining decision variables, constraints, and objective functions of the production planning optimization model based on the set, the parameters, and the model configuration information; A mixed integer model is established according to the decision variables, the constraint conditions and the objective function to obtain the production planning optimization model.

3. The production planning optimization method based on the mixed integer model according to claim 2, characterized in that: The optimization goal of the objective function is to minimize the comprehensive cost corresponding to the production line to be optimized, and the comprehensive cost includes order delay cost, forecast delay and cancellation cost, production cost, capacity utilization cost, inventory cost, procurement cost, transportation cost and replacement cost.

4. The production planning optimization method based on the mixed integer model according to claim 2, characterized in that: The constraints include: inventory balance constraints, alternative material constraints, delivery constraints, capacity limit constraints, production logic constraints, special scheduling constraints, minimum / maximum production volume constraints, and new product production constraints.

5. The production planning optimization method based on the mixed integer model according to claim 4, characterized in that: The inventory balance constraint represents the end-of-day inventory level; The alternative material constraint means that if a product has an alternative material group, the production volume of the product is equal to the usage volume of the material group; The delivery constraint represents the order delivery quantity and the forecast delivery quantity; The capacity limit constraint represents the upper limit of the capacity of the production line to be optimized; The production logic constraint represents that the total production volume of the factory is the sum of the production volumes of the production lines to be optimized; The special scheduling constraint means that the first product and the second product must be produced on the same day or cannot be produced on the same day; The minimum / maximum production constraint represents that the daily production volume is greater than the minimum production capacity and less than the maximum production capacity; The new product production constraints include new product production days restriction, new product production quantity matching constraint, and new product production logic constraint.

6. The production planning optimization method based on mixed integer model according to claim 1, characterized in that: After the step of solving the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized, the method further includes: When the update information corresponding to the production basic data is received, the production planning optimization model is updated according to the update information, and the step of solving the production planning optimization model according to the preset solver is returned to obtain the target production plan corresponding to the production line to be optimized.

7. The production planning optimization method based on a mixed integer model according to any one of claims 1 to 6, characterized in that: After the step of solving the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized, the method further includes: The production basic data and the model configuration information are stored in a preset blockchain node.

8. A production planning optimization device based on a mixed integer model, characterized in that: include: An acquisition module is used to obtain basic production data corresponding to the production line to be optimized; A model building module, used to build a production planning optimization model based on the production basic data and preset model configuration information; The model solving module is used to solve the production planning optimization model according to a preset solver to obtain the target production plan corresponding to the production line to be optimized.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the production planning optimization method based on the mixed integer model as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the production planning optimization method based on a mixed integer model according to any one of claims 1 to 7.

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