Intra-group cross-factory collaborative production planning method and system

Abstract

The invention discloses a cross-plant collaborative production planning method and system in a group. The method comprises the following steps: constructing a group overall production model according to group overall cost and group overall income; according to the transportation cost of interactive supply between different factories in the same group, a cross-factory mutual supply production model is constructed; constructing a factory self-production model according to the cost and income of independent production of the factory; constructing a mixed integer programming model of a three-layer collaborative architecture according to the group overall planning production model, the cross-factory mutual supply production model and the factory self-production model; and solving the mixed integer programming model by taking maximization of the overall profit of the group as an objective function, and outputting an overall production plan, a cross-factory material mutual supply plan and a factory production scheduling instruction when the model is optimal. The problems of high purchase cost, sales internal consumption and insufficient resource collaboration caused by traditional single factory optimization are solved, and the group operation benefits are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of chemical production planning optimization technology, and in particular to a method and system for cross-plant collaborative production planning within a group. Background Technology

[0002] The chemical industry is characterized by complex production processes, strong dependence on raw materials, and a high proportion of logistics costs. Traditional production planning models typically use a single factory as the optimization unit, with each factory independently procuring bulk raw materials and utilities. Therefore, traditional chemical enterprise production planning systems only optimize the production schedule within a single factory, neglecting cross-factory collaboration and material sharing among multiple factories. They fail to leverage economies of scale at the group level and lack the bargaining power of a large-scale group. Furthermore, the limited sales channels and differing pricing systems may lead to internal competition and reduce overall profits. Simultaneously, the lack of inter-factory coordination prevents the consideration of overall production planning solutions such as cross-factory collaboration and material sharing. Summary of the Invention

[0003] This application provides a method and system for cross-plant collaborative production planning within a group, in order to solve the problems of chaotic multi-factory collaborative production and lack of overall planning schemes under the current group operation.

[0004] In a first aspect, embodiments of this application provide a cross-plant collaborative production planning method within a group, including:

[0005] A group-wide integrated production model is constructed based on the group's integrated costs and integrated revenues.

[0006] Based on the transportation costs of cross-factory supply between different factories within the same group, a cross-factory supply production model is constructed.

[0007] Construct a factory self-production model based on the cost and revenue of factory production on its own.

[0008] Based on the group-wide production model, the cross-plant mutual supply production model, and the factory self-production model, a three-layer collaborative architecture hybrid integer programming model is constructed.

[0009] Solve the mixed-integer programming model with the objective function of maximizing the overall profit of the group, and output the overall production plan, inter-plant material supply plan and plant production scheduling instructions when the model is optimal.

[0010] In one possible embodiment, the step of constructing a group-wide production model based on group-wide planning costs and group-wide planning revenue includes:

[0011] Obtain the first input parameters, and the first output parameters include: overall price cost parameters, centralized procurement parameters, centralized sales parameters, and production batch parameters;

[0012] The first decision variable is defined based on the first input parameter. The first decision variable includes: centralized procurement quantity, centralized sales quantity, production batch, centralized procurement quantity of materials allocated from the group to the factory, and centralized sales quantity of materials allocated from the factory to the group.

[0013] The group-wide production model is constructed based on the first input parameters and the first decision variables.

[0014] In one possible embodiment, the step of constructing a cross-plant mutual supply production model based on the transportation costs of mutual supply between different factories within the same group includes:

[0015] Obtain the unit quantity transportation cost parameters and upper and lower limits of transportation volume parameters of materials between different factories within the same group as the second input parameters;

[0016] The volume of material transportation between different factories within the same group is defined as the second decision variable.

[0017] The cross-plant mutual supply production model is constructed based on the second input parameters and the second decision variables.

[0018] In one possible embodiment, the step of constructing a factory self-production model based on the costs and revenues of factory-owned production includes:

[0019] Obtain the third input parameters, the third output parameters include: processing scheme parameters, self-production price cost parameters, processing capacity parameters, inventory parameters, self-procurement parameters, and self-sales parameters;

[0020] The third decision variable is defined based on the third input parameter. The third decision variable includes: consumption, output, baseline material quantity, beginning inventory, ending inventory, self-procurement quantity, self-sold quantity, and waste material quantity.

[0021] A factory self-production model is constructed based on the third input parameter and the third decision variable.

[0022] In one possible embodiment, the step of solving the mixed-integer programming model with the objective function of maximizing the overall group profit, and outputting the overall production plan, inter-plant material supply plan, and plant production scheduling instructions when the model's optimal solution is achieved, includes:

[0023] Constraints are established based on the mixed integer programming model of the three-layer collaborative architecture, including: production batch coordination constraints, material balance constraints, processing capacity constraints, inventory boundary constraints, quantitative constraints of dual-channel procurement, quantitative constraints of dual-channel sales, and inter-plant supply constraints.

[0024] In one possible embodiment, the objective function is formulated as follows:

[0025] Total Profit = Revenue from Centralized Sales Products - Cost of Centralized Raw Materials + Revenue from Self-Sold Products - Cost of Self-Sold Raw Materials - Transportation Cost of Centralized Raw Materials - Distribution Cost of Centralized Sales Products - Distribution Cost Between Factories + Ending Inventory Value - Beginning Inventory Value - Inventory Cost

[0026] Secondly, embodiments of this application provide a cross-plant collaborative production planning system within a group, including:

[0027] The first construction module is used to build a group-wide integrated production model based on the group's integrated costs and integrated revenues.

[0028] The second building module is used to build a cross-factory mutual supply production model based on the transportation costs of mutual supply between different factories within the same group.

[0029] The third building module is used to build a factory self-production model based on the cost and revenue of the factory producing on its own.

[0030] The fourth construction module is used to construct a three-layer collaborative architecture hybrid integer programming model based on the group-wide coordinated production model, the cross-plant mutual supply production model, and the factory self-production model.

[0031] The output module is used to solve the mixed integer programming model with the objective function of maximizing the overall profit of the group, and outputs the overall production plan, inter-plant material supply plan and plant production scheduling instructions when the model is optimal.

[0032] Thirdly, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the intra-group cross-plant collaborative production planning method described in the first aspect above.

[0033] Fourthly, this application provides a computer-readable storage medium including program code, which, when the storage medium is run on an electronic device, causes the electronic device to execute the intra-group cross-plant collaborative production planning method described in the first aspect above.

[0034] Fifthly, an embodiment of this application provides a computer program product, which includes computer instructions stored in a computer-readable storage medium; when the processor of an electronic device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the electronic device to perform the intra-group cross-plant collaborative production planning method described in the first aspect above.

[0035] The beneficial effects of this application are as follows:

[0036] This application provides a method and system for cross-plant collaborative production planning within a group. By constructing a group-wide coordinated production model, a cross-plant mutual supply production model, and a factory self-production model, a two-tiered operational architecture is formed, layered from the group level to the factory level. Based on this, a hybrid integer programming model is constructed, integrating a three-tiered collaborative architecture of group coordination, cross-plant mutual supply, and factory self-production. This model integrates material procurement, production scheduling, inventory management, logistics and distribution, and dual-channel sales decisions, performing global optimization with the goal of maximizing overall profit. This solves the problems of high procurement costs, internal sales friction, and insufficient resource coordination caused by traditional single-plant optimization, significantly improving the group's operational efficiency.

[0037] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of an application scenario in the embodiments of this application;

[0040] Figure 2 This is a schematic diagram of the intra-group cross-plant collaborative production planning method in the embodiments of this application;

[0041] Figure 3 This is a schematic diagram of the framework of the intra-group cross-plant collaborative production planning system in the embodiments of this application;

[0042] Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0044] The design concept of the embodiments of this application is briefly introduced below:

[0045] The chemical industry is characterized by complex production processes, strong dependence on raw materials, and a high proportion of logistics costs. In existing technologies, production planning models typically use a single plant as the optimization unit, with each plant independently procuring bulk raw materials and utilities, and the group level not directly involved in the production process. This model has the following problems: independent procurement of bulk raw materials and utilities by each plant fails to leverage the group's economies of scale and bargaining power; limited sales channels and differing pricing systems may lead to internal competition and reduce overall profits; and there is a lack of coordination between plants, neglecting cross-plant material supply and capacity collaboration, resulting in low resource utilization.

[0046] In view of this, embodiments of this application provide a method and system for cross-plant collaborative production planning within a group, through...

[0047] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0048] like Figure 1 The diagram shown illustrates an application scenario according to an embodiment of this application. The application scenario diagram includes a terminal device 101 and a server 102. The terminal device 101 and the server 102 communicate via a communication network.

[0049] Terminal device 101 is an electronic device used by the target entity. This electronic device can be a personal computer, mobile phone, tablet computer, laptop, e-book reader, vehicle terminal, etc. Furthermore, terminal device 101 can be equipped with a client for the group's cross-plant collaborative production planning system and a display screen that allows for user interaction. This client allows for the input of model parameters, the definition of decision variables, the setting of constraints, and the display of model output results. The target entity can use terminal device 101 to input model parameters, define decision variables, set constraints, and view model output results. It can also control terminal device 101 to display the overall production plan, cross-plant material supply plan, and factory production scheduling instructions output after the model solution is solved. The target entities in this application scenario primarily include decision-makers or managers within a chemical plant who use the system to formulate production planning schemes.

[0050] Server 102 can be a standalone physical server, an edge device 102 in the field of cloud computing, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, cloud functions, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0051] There is no limit to the number of the aforementioned terminal devices 101 and / or servers 102.

[0052] It should be noted that the intra-group cross-plant collaborative production planning method in this embodiment is jointly executed by terminal device 101 and server 102. Server 102 receives parameters from the target object via terminal device 101, including group-wide overall costs and revenues, costs and revenues of interactive production between different factories within the same group, and costs and revenues of independent production by individual factories. Server 102 defines decision variables, and terminal device 101 generates a group-wide coordinated production model, a cross-plant mutual supply production model, and a factory self-production model. Based on these three models, a three-layer collaborative architecture mixed-integer programming model is constructed. The mixed-integer programming model is solved with the objective function of maximizing the overall group profit, and the optimal solution is output as the coordinated production plan, cross-plant material mutual supply plan, and factory production scheduling instructions. The target object can view the model output results via terminal device 101 and can also adjust input parameters, change decision variables, or modify constraints according to the actual usage scenario.

[0053] The following describes the cross-plant collaborative production planning method within a group provided by the exemplary embodiments of this application, in conjunction with the above application scenarios and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way.

[0054] like Figure 2 The diagram shown is an implementation flowchart of a cross-plant collaborative production planning method within a group, provided in an embodiment of this application. The specific implementation process of this method is as follows:

[0055] S201. Construct a group-wide production model based on group-wide integrated costs and group-wide integrated revenues.

[0056] In this embodiment, group-wide coordinated costs include, but are not limited to: the price cost of materials procured by the group based on the production capacity and inventory capacity of all factories within the group, and the logistics costs of material transportation and product distribution during the group-wide coordinated production process. Group-wide coordinated revenue refers to the revenue generated from the unified sale of products produced by each factory within the group to external parties. In this embodiment, the constructed group-wide coordinated production model is specifically manifested in the group's ability to centrally coordinate the procurement of bulk materials and utilities and allocate them to each factory, dynamically allocate intermediate products according to the capacity and demand of each factory to achieve cross-factory supply, and establish direct sales channels for core customers for coordinated sales. The two extreme states of this model are zero group-wide coordinated production and all production achieved by the group. Specifically, when the group-wide coordinated production is zero, it means that all production processes are allocated to each factory within the group, which is a traditional process where a single factory is the optimization unit, and each factory independently procures bulk raw materials and utilities and independently conducts channel sales. When all production is centrally managed by the group, individual factories within the group do not engage in independent procurement or sales; they are only responsible for the transportation and storage of materials. All production processes, including procurement, sales, and logistics, are centrally managed by the group. During the solution process of the mixed-integer programming model, the production volume allocated to the group level is adjusted according to the constraints. In this process, the group-managed production model participates in the adjustment of the overall production planning scheme based on the profit-optimal solution at the group level.

[0057] In some implementations, the step of constructing a group-wide production model based on group-wide planning costs and group-wide planning revenue in step S201 includes:

[0058] Obtain the first input parameters, and the first output parameters include: overall price cost parameters, centralized procurement parameters, centralized sales parameters, and production batch parameters;

[0059] The first decision variable is defined based on the first input parameter. The first decision variable includes: centralized procurement quantity, centralized sales quantity, production batch, centralized procurement quantity of materials allocated from the group to the factory, and centralized sales quantity of materials allocated from the factory to the group.

[0060] The group-wide production model is constructed based on the first input parameters and the first decision variables.

[0061] In this embodiment, the first input parameters of the group-wide coordinated production model are specifically defined, and are divided into coordinated price and cost parameters, centralized procurement parameters, centralized sales parameters, and production batch parameters based on parameter attributes. Specifically, in a specific application scenario, material m is purchased from market k, produced by factory r to obtain products, and simultaneously, changes occur in inventory and transportation volume within the factory. To ensure data uniformity and standardization, data from input period t is typically selected as the input parameter. Based on this, the coordinated price and cost parameters include: the unit coordinated sales price of material m in market k during period t, the unit centralized procurement price of material m in market k during period t, the unit transportation cost of material m from the group to factory r during period t, and the unit transportation cost of material m from factory r to the group during period t. The centralized procurement parameters include: the upper limit of the centralized procurement quantity of material m from market k during period t, the lower limit of the centralized procurement quantity of material m from market k during period t, the upper limit of the transportation volume of centralized procurement material m from the group to factory r during period t, and the lower limit of the transportation volume of centralized procurement material m from the group to factory r during period t. The centralized sales parameters include: the upper limit of the sales volume of centralized sales material m to market k in period t, the lower limit of the sales volume of centralized sales material m to market k in period t, the upper limit of the distribution volume of centralized sales material m from factory r to the group in period t, and the lower limit of the distribution volume of centralized sales material m from factory r to the group in period t. The production batch parameters include: the unit quantity of centralized sales material m purchased in market k in a single batch in period t, and the unit quantity of centralized sales material m in market k sold in a single batch in period t.

[0062] In practice, before inputting parameters, a parameter set can be defined in advance to facilitate the preprocessing of input parameters. The definable parameter set includes:

[0063] The set of all factories is R, r∈R;

[0064] The set of all materials M, including utilities, where m∈M;

[0065] The set of devices E, e∈E;

[0066] The set of time periods is T, t∈T;

[0067] Market set K, k∈K;

[0068] The set of processing schemes P for device e e , p∈Pe ;

[0069] The set of processing capabilities is H, where h∈H;

[0070] Overall Material Collection M G ,m∈M G ;

[0071] The set of consumable materials corresponding to processing scheme p The set of output materials corresponding to processing scheme p The set of devices E involved in the device processing capacity h h ,e∈E h .

[0072] Furthermore, in the same scenario described above, the first decision variables defined according to the first input parameters can be expressed as follows: Centralized procurement quantity (i.e., the purchase quantity of centrally procured material m in market k during period t), centralized sales quantity (i.e., the sales quantity of centrally sold material m in market k during period t), production batches (including the purchase batches of centrally procured material m in market k during period t and the sales batches of centrally sold material m to market k during period t), centralized procurement quantity of materials allocated from the group to the factory (i.e., the quantity of centrally procured material m allocated from the group to factory r during period t), and centralized sales quantity of materials allocated from the factory to the group (i.e., the quantity of centrally sold material m allocated from factory r to the group during period t). Among these, centralized procurement quantity, centralized sales quantity, centralized procurement quantity of materials allocated from the group to the factory, and centralized sales quantity of materials allocated from the factory to the group are all continuous variables, while production batches are integer variables.

[0073] This embodiment constructs a mixed integer programming model based on the group-wide integrated generation model, the cross-plant mutual supply production model, and the factory self-production model. It realizes integrated operation based on the group, leverages the synergistic advantages of multiple plants, generates the overall procurement, sales, and production plans of the group and multiple plants, breaks the isolated single-plant model, and achieves resource synergy and cost savings.

[0074] S202. Construct a cross-plant mutual supply production model based on the transportation costs of mutual supply between different factories within the same group.

[0075] In this embodiment, cross-factory mutual supply refers to the dynamic allocation of intermediate products based on the capacity and demand of each factory, realizing cross-factory collaboration and material mutual supply among multiple factories. The transportation cost of mutual supply between different factories within the same group is represented in a specific application scenario as: the total unit transportation cost of factory r transporting material m to factory r′ in cycle t; the upper limit of the mutual supply transportation volume of factory r transporting material m to factory r′ in cycle t; and the lower limit of the mutual supply transportation volume of factory r transporting material m to factory r′ in cycle t.

[0076] In some implementations, the step S202, which involves constructing a cross-plant supply production model based on the transportation costs of inter-plant supply within the same group, includes:

[0077] Obtain the unit quantity transportation cost parameters and upper and lower limits of transportation volume parameters of materials between different factories within the same group as the second input parameters;

[0078] The volume of material transportation between different factories within the same group is defined as the second decision variable.

[0079] The cross-plant mutual supply production model is constructed based on the second input parameters and the second decision variables.

[0080] This embodiment constructs a cross-plant mutual supply production model, which can reflect the cross-plant mutual supply transportation cost through specific transportation volume. This is beneficial for solving the problem using a mathematical programming solver, and the cross-plant mutual supply cost can be used as a factor to consider in optimizing group collaborative production.

[0081] S203. Construct a factory self-production model based on the cost and revenue of the factory producing on its own.

[0082] In this embodiment, the costs and revenues of a factory's independent production refer to the costs and revenues incurred during the factory's independent production process, where the factory procures and sells its own products. Specifically, the costs of independent production include the cost of self-procured raw materials and inventory costs, while the revenues include the revenue from products sold through direct sales channels. In this embodiment, the constructed factory self-production model represents the profit obtained by the factory at the factory level after independently completing the entire production process based on its own processing plan, processing capacity, and inventory capacity. This model has two extreme states: first, the factory is only responsible for receiving materials and processing finished products, without procurement; second, all production is achieved independently by the factory. Specifically, when the factory is only responsible for receiving materials and processing finished products, without procurement, it means that all production processes are coordinated within the group. When all production is achieved independently by the factory, the group does not conduct centralized procurement and sales; each factory procures and sells independently, and all production processes, including procurement, sales, and logistics, are achieved independently by each factory. In the process of solving the mixed integer programming model, the production volume allocated to the factory level is adjusted according to the constraints. During this process, the group-wide production planning model participates in the adjustment of the overall production planning scheme based on the profit-optimal solution at the factory level.

[0083] In some implementations, the step of constructing a factory self-production model based on the cost and revenue of factory self-production in step S203 includes:

[0084] Obtain the third input parameters, the third output parameters include: processing scheme parameters, self-production price cost parameters, processing capacity parameters, inventory parameters, self-procurement parameters, and self-sales parameters;

[0085] The third decision variable is defined based on the third input parameter. The third decision variable includes: consumption, output, baseline material quantity, beginning inventory, ending inventory, self-procurement quantity, self-sold quantity, and waste material quantity.

[0086] A factory self-production model is constructed based on the third input parameter and the third decision variable.

[0087] In this embodiment, the third input parameter of the factory self-production model is specifically defined, and is divided into processing scheme parameters, self-production price cost parameters, processing capacity parameters, inventory parameters, self-procurement parameters, and self-sales parameters according to parameter attributes. Specifically, in a specific application scenario, the processing scheme parameters are specifically expressed as: the quality balance coefficient of material m in processing scheme p of plant e of plant r relative to the baseline material quantity, and the coefficient of processing capacity h of plant r in processing scheme p of plant e; the self-production price cost parameters are specifically expressed as: the unit sales price of material m of plant r to market k in period t, and the unit self-procurement price of material m of plant r to market k in period t; the processing capacity parameters are specifically expressed as: the upper limit of processing capacity h of plant r in period t, and the lower limit of processing capacity h of plant r in period t; the inventory parameters are specifically expressed as: the material of plant r... The parameters are: the unit beginning inventory value of material m in factory r, the unit ending inventory value of material m in factory r, the unit inventory cost of material m in factory r during period t, the beginning inventory of material m in factory r, the ending inventory of material m in factory r, the upper limit of the inventory of material m in factory r during period t, and the lower limit of the inventory of material m in factory r during period t; the self-procurement parameters are specifically expressed as: the upper limit of the purchase quantity of material m in factory r from market k during period t, and the lower limit of the purchase quantity of material m in factory r from market k during period t; the self-sales parameters are specifically expressed as: the upper limit of the sales quantity of material m in factory r from market k during period t, and the lower limit of the sales quantity of material m in factory r from market k during period t.

[0088] This embodiment uses a factory self-production model to optimize production plans and inventory levels within the factory, while coordinating independent factory production and group-wide production. Based on integrated group operations, it leverages the synergistic advantages of multiple factories to generate overall procurement, sales, and production plans for the group and its multiple factories.

[0089] S204. Construct a three-layer collaborative architecture hybrid integer programming model based on the group-wide production model, the cross-plant mutual supply production model, and the factory self-production model.

[0090] Mixed-integer programming is a mathematical optimization model that combines the characteristics of linear programming with a key constraint: some or all decision variables must take integer values. It is used to solve complex problems that simultaneously involve discrete decisions (represented by integer variables) and continuous decisions (represented by continuous variables). By enforcing integer values ​​for some variables on top of linear programming, it accurately models the combined planning problem of allocating and coordinating production and individual factory production in real-world intra-group production processes.

[0091] The architecture of the mixed-integer programming model is a three-layer collaborative optimization system that coordinates decision-making at three levels: the group level, the mutual supply level, and the factory level. The group level makes overall decisions, allocates the quantity for centralized procurement and sales, and achieves the ultimate goal of maximizing global profits by optimizing procurement costs. The mutual supply level is responsible for the flow of resources between factories within the group, and maintains the capacity balance between factories through cross-factory scheduling to achieve the ultimate goal of maximizing global profits. The factory level considers local decisions, and achieves the ultimate goal of maximizing global profits by splitting processing devices, optimizing processing schemes, and controlling self-procurement and self-sales costs.

[0092] S205. Solve the mixed integer programming model with the objective function of maximizing the overall profit of the group, and output the overall production plan, inter-plant material supply plan and plant production scheduling instructions when the model is optimal.

[0093] Specifically, step S205 further includes: establishing constraints based on the mixed integer programming model of the three-layer collaborative architecture, the constraints including: overall production batch constraints, material balance constraints, processing capacity constraints, inventory boundary constraints, quantitative constraints of dual-channel procurement, quantitative constraints of dual-channel sales, and inter-plant mutual supply constraints.

[0094] In a specific application scenario, the above constraints, combined with the specific details of the actual situation, are as follows:

[0095] The specific manifestation of the production batch constraint is: the relationship between the production batch and the total purchase / sales volume, that is, for any material m in any factory r and any market k in any period t, the total purchase / sales volume = the total purchase / sales batch * the single batch purchase / sales volume.

[0096] Material balance constraints are specifically manifested as follows: material inflow and outflow balance for each plant in each period, that is, for any plant r and any material m in any period t, the beginning inventory of material m + the total purchase quantity of material m + the total amount of material m delivered from other plants + the output of all units for material m = the ending inventory of material m + the total sales quantity of material m + the material m delivered to other plants + the consumption of material m by all units + the slack of material m; beginning and ending inventory balance, that is, for any plant r and any material m, the beginning inventory in period t+1 is equal to the ending inventory in period t.

[0097] The processing capacity constraint is specifically manifested as follows: the material processing / output of each processing scheme of the device, that is, for any device e of any factory r in any period t, the material processing / output of each scheme is equal to the product of the material quality balance coefficient and the reference material quantity; the upper and lower limits of the device processing capacity, that is, for any processing capacity h of any factory r in any period t, the product of the reference material quantity and the corresponding capacity coefficient of all processing schemes is within the upper and lower limits of the device processing capacity.

[0098] The specific manifestations of inventory boundary constraints are: upper and lower limits of material inventory, that is, for any material m in any factory r, the inventory level is within the specified upper and lower limits in any period t; beginning inventory of materials, that is, for any material m in any factory r, the beginning inventory is assigned in period 1; ending inventory of materials, that is, for any material m in any factory r, the ending inventory in the last period must be equal to the required ending inventory.

[0099] The quantitative constraints of the dual-channel procurement are specifically manifested as follows: Total raw material procurement volume per factory: For any factory r and any material m in any period t, the total raw material procurement volume equals the overall allocation volume plus the single-factory procurement volume; Total raw material procurement volume of the group and allocation to a single factory: For any material m in any period t, the total procurement volume of overall raw materials in all markets equals the sum of the quantities allocated to all factories; Upper and lower limits of group-wide procurement raw material procurement volume: For any centrally procured material m in any period t, the procurement volume in each market is within the specified upper and lower limits; Upper and lower limits of single-factory raw material procurement volume: For any factory r and any material m in any period t, the procurement volume in each market is within the specified upper and lower limits; Upper and lower limits of group-wide procurement raw material transportation volume: For any centrally procured material m in any period t, the transportation volume from the group to factory r is within the specified upper and lower limits.

[0100] The quantitative constraints of the dual-channel sales system are specifically manifested as follows: Total sales volume per factory: For any factory r and any material m in any period t, the total sales volume equals the total sales volume allocated to the group plus the sales volume per factory; Total sales volume of group-wide distributed products and single-factory allocation: For any material m in any period t, the total sales volume of distributed products in all markets equals the sum of the quantities allocated to the group by all factories; Upper and lower limits for distributed product sales volume: For any distributed material m in any period t, the sales volume in each market is within the specified upper and lower limits; Upper and lower limits for single-factory product sales volume: For any factory r and any material m in any period t and market k, the sales volume is within the specified upper and lower limits; Upper and lower limits for distributed product delivery volume: For any distributed material m in any period t, the delivery volume from factory r to the group is within the specified upper and lower limits.

[0101] Inter-plant supply constraints are specifically manifested as: upper and lower limits on the material distribution volume between plants, that is, for any plant r and r′, the material m, in any period t, the distribution volume is within the specified upper and lower limits.

[0102] In some implementations, the formula for the objective function in step S205 is:

[0103] Total Profit = Revenue from Centralized Sales Products - Cost of Centralized Raw Materials + Revenue from Self-Sold Products - Cost of Self-Sold Raw Materials - Transportation Cost of Centralized Raw Materials - Distribution Cost of Centralized Sales Products - Distribution Cost Between Factories + Ending Inventory Value - Beginning Inventory Value - Inventory Cost

[0104] This embodiment, targeting a group-based operation scenario, establishes a two-tiered optimized operational architecture of centralized procurement / sales at the group level and localized decision-making at the factory level. A three-tiered collaborative architecture, a mixed-integer programming model, is constructed using the group-wide coordinated production model, the cross-factory mutual supply production model, and the factory self-production model. This model integrates material procurement, production scheduling, inventory management, logistics and distribution, and dual-channel sales decisions, achieving global optimization with the goal of maximizing overall profit. This solves the problems of high procurement costs, internal sales friction, and insufficient resource coordination caused by traditional single-factory optimization, significantly improving the group's operational efficiency.

[0105] Based on the same inventive concept, embodiments of this application also provide a cross-plant collaborative production planning system within a group. For example... Figure 3 As shown, this is a schematic diagram of the 300 framework for the group's cross-plant collaborative production planning system, which may include:

[0106] The first construction module 301 is used to construct a group-wide production model based on the group's overall costs and overall revenue.

[0107] The second building module 302 is used to build a cross-factory mutual supply production model based on the transportation costs of mutual supply between different factories within the same group.

[0108] The third building module 302 is used to build a factory self-production model based on the cost and revenue of the factory producing on its own.

[0109] The fourth construction module 304 is used to construct a three-layer collaborative architecture hybrid integer programming model based on the group-wide coordinated production model, the cross-plant mutual supply production model, and the factory self-production model.

[0110] Output module 305 is used to solve the mixed integer programming model with the objective function of maximizing the overall profit of the group, and outputs the overall production plan, inter-plant material supply plan and factory production scheduling instructions when the model is optimal.

[0111] The cross-factory collaborative production planning system provided in this application embodiment constructs a group-wide coordinated production model through a first construction module 301; a cross-factory mutual supply production model through a second construction module 302; a factory self-production model through a third construction module 302; and finally, a three-layer collaborative architecture hybrid integer programming model is constructed through a fourth construction module 304 based on the group-wide coordinated production model, the cross-factory mutual supply production model, and the factory self-production model. This enables the optimization of multi-factory collaborative production plans for group-based operations. At the group level, bulk materials and utilities can be centrally procured and allocated to various factories. Intermediate products can be dynamically allocated according to the capacity and demand of each factory to achieve cross-factory mutual supply. At the same time, a direct sales channel for core customers can be established for coordinated sales. At the factory level, some materials can be procured locally and sold regionally, or some products can be aggregated and sold uniformly at the group level. Based on a series of constraints such as procurement, sales, production, inventory, and distribution, and considering sales revenue, procurement costs, logistics costs, and inventory costs, a mixed integer programming model is established with the goal of maximizing overall profit. The model is then solved using a mathematical programming solver to achieve global optimization of material procurement, production scheduling, logistics distribution, and sales strategies for the group and multiple plants.

[0112] In some possible implementations, the intra-group cross-plant collaborative production planning system of this application may include at least a processor and a memory. The memory stores program code that, when executed by the processor, causes the processor to perform the steps in the intra-group cross-plant collaborative production planning method according to various exemplary embodiments of this application as described in this specification. For example, the processor may perform actions such as... Figure 2 The steps are shown in the figure.

[0113] Based on the same inventive concept, this application also provides an electronic device that can realize the functions of the aforementioned intra-group cross-plant collaborative production planning method and system. (Refer to...) Figure 4 A schematic diagram of the structure of an electronic device in one embodiment shows that, at the hardware level, the electronic device includes a processor 401, and optionally also includes an internal bus 400, a network interface 403, and a memory 402. The memory 402 may include main memory, such as high-speed random-access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0114] The processor 401, network interface 403, and memory 402 can be interconnected via an internal bus 400. This internal bus 400 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0115] Memory 402 is used to store programs. Specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0116] In one possible implementation, the processor reads the corresponding computer program from non-volatile memory into memory and then runs it. Alternatively, it can obtain the corresponding computer program from other devices to form a cross-plant collaborative production planning system within the group at the logical level. The processor 401 executes the program stored in memory 402 to implement the cross-plant collaborative production planning method within the group provided in any embodiment of this application through the executed program.

[0117] The above is as described in the present invention. Figure 2 The intra-group cross-plant collaborative production planning method provided in the illustrated embodiment can be applied to, or implemented by, processor 401. Processor 401 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed through integrated logic circuits in the hardware of processor 401 or through software instructions. The processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0118] The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0119] Based on the same inventive concept, embodiments of this application also provide a storage medium that stores one or more programs, the one or more programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform the intra-group cross-plant collaborative production planning method provided in any embodiment of this application.

[0120] The systems and modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0121] For ease of description, the above apparatus is described by dividing it into various units or modules according to their functions. Of course, in implementing this invention, the functions of each unit or module can be implemented in one or more software and / or hardware.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0124] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A cross-plant collaborative production planning method within a group, characterized in that, include: Construct a group-wide integrated production model based on group-wide integrated costs and group-wide integrated revenue; Based on the transportation costs of cross-factory supply between different factories within the same group, a cross-factory supply production model is constructed. Construct a factory self-production model based on the cost and revenue of factory production on its own. Based on the group-wide production model, the cross-plant mutual supply production model, and the factory self-production model, a three-layer collaborative architecture hybrid integer programming model is constructed. Solve the mixed-integer programming model with the objective function of maximizing the overall profit of the group, and output the overall production plan, inter-plant material supply plan and plant production scheduling instructions when the model is optimal.

2. The method as described in claim 1, characterized in that, The steps for constructing a group-wide integrated production model based on group-wide integrated costs and group-wide integrated revenue include: Obtain the first input parameters, and the first output parameters include: overall price cost parameters, centralized procurement parameters, centralized sales parameters, and production batch parameters; The first decision variable is defined based on the first input parameter. The first decision variable includes: centralized procurement quantity, centralized sales quantity, production batch, centralized procurement quantity of materials allocated from the group to the factory, and centralized sales quantity of materials allocated from the factory to the group. The group-wide production model is constructed based on the first input parameters and the first decision variables.

3. The method as described in claim 1, characterized in that, The steps for constructing a cross-factory mutual supply production model based on the transportation costs of mutual supply between different factories within the same group include: Obtain the unit quantity transportation cost parameters and upper and lower limits of transportation volume parameters of materials between different factories within the same group as the second input parameters; The volume of material transportation between different factories within the same group is defined as the second decision variable. The cross-plant mutual supply production model is constructed based on the second input parameters and the second decision variables.

4. The method as described in claim 1, characterized in that, The steps for constructing a factory self-production model based on the costs and revenues of factory self-production include: Obtain the third input parameters, the third output parameters include: processing scheme parameters, self-production price cost parameters, processing capacity parameters, inventory parameters, self-procurement parameters, and self-sales parameters; The third decision variable is defined based on the third input parameter. The third decision variable includes: consumption, output, baseline material quantity, beginning inventory, ending inventory, self-procurement quantity, self-sold quantity, and waste material quantity. A factory self-production model is constructed based on the third input parameter and the third decision variable.

5. The method as described in claim 1, characterized in that, The steps for solving the mixed-integer programming model with the objective function of maximizing the overall group profit, and outputting the overall production plan, inter-plant material supply plan, and plant production scheduling instructions when the model achieves its optimal solution, include: Constraints are established based on the mixed integer programming model of the three-layer collaborative architecture, including: production batch coordination constraints, material balance constraints, processing capacity constraints, inventory boundary constraints, quantitative constraints of dual-channel procurement, quantitative constraints of dual-channel sales, and inter-plant supply constraints.

6. The method as described in claim 1, characterized in that, The formula for the objective function is: Total Profit = Revenue from Centralized Sales Products - Cost of Centralized Raw Materials + Revenue from Self-Sold Products - Cost of Self-Sold Raw Materials - Transportation Cost of Centralized Raw Materials - Distribution Cost of Centralized Sales Products - Distribution Cost Between Factories + Ending Inventory Value - Beginning Inventory Value - Inventory Cost 7. A cross-plant collaborative production planning system within a group, characterized in that, include: The first construction module is used to build a group-wide integrated production model based on the group's integrated costs and integrated revenues. The second building module is used to build a cross-factory mutual supply production model based on the transportation costs of mutual supply between different factories within the same group. The third building module is used to build a factory self-production model based on the cost and revenue of the factory producing on its own. The fourth construction module is used to construct a three-layer collaborative architecture hybrid integer programming model based on the group-wide coordinated production model, the cross-plant mutual supply production model, and the factory self-production model. The output module is used to solve the mixed integer programming model with the objective function of maximizing the overall profit of the group, and outputs the overall production plan, inter-plant material supply plan and plant production scheduling instructions when the model is optimal.

8. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores program code that, when executed by the processor, causes the processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Includes program code that, when the storage medium is run on an electronic device, causes the electronic device to perform any of the methods described in claims 1 to 6.

10. A computer program product comprising computer instructions stored in a computer-readable storage medium; wherein when a processor of an electronic device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the electronic device to perform the method of any one of claims 1 to 6.

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