Whole industry chain production and marketing collaborative balance system and method considering value maximization

Abstract

The invention discloses a whole industry chain production and marketing collaborative balance system and method considering value maximization in the technical field of production management. The method comprises the following steps: acquiring supply data, demand data and storage and transportation data; based on the supply data, the demand data and the storage and transportation data, a linear programming model including coal mixing quality cost optimization, multimodal transport path distribution optimization and dynamic inventory balance is constructed with group profit maximization as a target, and an interior point algorithm is adopted for solving to obtain a production and marketing balance plan and operation income prediction in the future N + 12 months; and outputting the production-marketing balance plan and the operation income prediction. According to the method, dual control of quality and cost is realized through a coal mixing quality cost optimization formula, and the problem of excessive or insufficient quality caused by empirical matching in a traditional method is avoided.

Description

Technical Field

[0001] This invention relates to a whole-industry chain production and sales synergy balancing system and method that takes into account value maximization, belonging to the field of production management technology. Background Technology

[0002] Coal is my country's primary energy source. The dramatic fluctuations in coal prices in recent years have made cost control increasingly urgent for coal-related enterprises. Coal costs mainly include procurement costs, transportation costs, and inventory costs. Procurement costs depend primarily on coal market prices and purchase volumes. Due to the variety of coal types and transportation routes, manual calculations are extremely complex. Currently, relying on past logistics experience and customizing procurement and transportation strategies similar to previous years has failed to maximize overall business efficiency for enterprises.

[0003] Traditional coal enterprises rely heavily on experience-based decision-making and static planning for coordinated production and sales management, lacking systematic optimization models. In a complex and ever-changing market economy, this model struggles to achieve efficient resource allocation and maximize overall profits. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a whole-industry chain production and sales collaborative balance system and method that takes into account the maximization of value. It can realize intelligent optimization of multiple links such as production, procurement, transportation, warehousing and sales, and achieve overall resource coordination and dynamic adjustment.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a method for balancing production and sales across the entire industry chain, considering value maximization, comprising:

[0007] Obtain supply data, demand data, and storage and transportation data;

[0008] Based on the aforementioned supply data, demand data, and storage and transportation data, and with the goal of maximizing group profits, a linear programming model is constructed that includes coal blending quality cost optimization, multimodal transport route allocation optimization, and dynamic inventory balance. The model is then solved using an interior point algorithm to obtain the production and sales balance plan and operating income forecast for the next N+12 months.

[0009] Output the production and sales balance plan and operating profit forecast.

[0010] Furthermore, the acquisition of supply data, demand data, and storage and transportation data specifically includes:

[0011] Obtain coal production plans, procurement plans, and cost data for the next 12 months as supply data;

[0012] Obtain coal demand forecasts for the next 12 months, capacity forecasts for the internal coal power and coal chemical sectors, product sales prices and production cost data as demand data;

[0013] Obtain transportation routes, transportation parameters, and storage capacity data as storage and transportation data.

[0014] Furthermore, the coal blending quality cost optimization formula is as follows:

[0015]

[0016] Where: x ij c represents the mixing amount of coal of type i and coal of type j; ij Indicates the unit cost of mixing; n represents the total number of basic coal types available for mixing; m represents the number of types of mixed coal products to be produced; Q k1 Q represents the actual value of the k-th quality index after mixing; k2 λ represents the customer's required value for the k-th quality indicator. k represents the penalty coefficient for quality deviation; p represents the number of coal quality indicators that need to be controlled and optimized.

[0017] Furthermore, the multimodal transport route allocation optimization formula is as follows:

[0018]

[0019] Where: y rt d represents the transport volume of the t-th transport mode on path r; rt f represents the distance of transport mode t along path r; rt This represents the unit distance rate for transport mode t on route r; t rt Indicates the transit time of transit mode t on path r; u rt δ represents the time cost coefficient. rt α represents the path reliability adjustment factor; α, β, and γ are weighting coefficients; R represents the total number of feasible transportation paths defined in the model; and T represents the number of transportation mode segments available on a single path r.

[0020] Furthermore, the dynamic inventory balancing formula is as follows:

[0021]

[0022] Where: h s I represents the unit inventory holding cost at storage point s; sτ b represents the inventory level at storage point s in month τ; s D represents the unit stockout cost at storage point s; sτω represents the projected demand at storage point s in month τ; S represents the total number of storage points in the group's supply chain network that require independent inventory optimization management; τ Indicates the inventory fluctuation penalty coefficient; ΔI sτ This indicates the change in inventory.

[0023] Furthermore, the output of the production and sales balance plan and operating profit forecast includes:

[0024] The production and sales balance plan will be distributed to the production, procurement, logistics and sales departments for implementation, and the operating profit forecast will be submitted to the group management to assist in business decision-making.

[0025] Furthermore, it also includes:

[0026] Based on the aforementioned production and sales balance plan, monthly production and sales execution deviation monitoring indicators are generated for each of the next N+12 months.

[0027] Collect actual production and sales data in real time, and calculate the execution deviation between the actual production and sales data and the production and sales balance plan for the corresponding month;

[0028] When the execution deviation exceeds the preset threshold of the production and sales execution deviation monitoring index, a plan adjustment warning is triggered, and the steps of acquiring data, processing data, and outputting results are re-executed; otherwise, no action is taken.

[0029] Secondly, the present invention provides a full-industry chain production and sales coordination and balance system that considers value maximization, including:

[0030] Data acquisition module: used to acquire supply data, demand data, and storage and transportation data;

[0031] Data processing module: Based on the supply data, demand data, and storage and transportation data, and with the goal of maximizing group profits, it constructs a linear programming model that includes coal blending quality cost optimization, multimodal transport route allocation optimization, and dynamic inventory balance, and uses the interior point algorithm to solve it, to obtain the production and sales balance plan and operating income forecast for the next N+12 months.

[0032] Data output module: Used to output the production and sales balance plan and operating profit forecast.

[0033] Thirdly, the present invention provides a whole-industry chain production and sales synergy balancing device that takes into account value maximization, including a processor and a storage medium;

[0034] The storage medium is used to store instructions;

[0035] The processor is configured to operate according to the instructions to perform the steps of the method according to any of the foregoing.

[0036] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0038] This solution significantly improves the accuracy and practicality of the production-sales coordination model. Compared to traditional technologies, it achieves dual control of quality and cost through a coal blending quality-cost optimization formula, avoiding the quality over- or under-quality issues caused by relying on experience-based blending in traditional methods. The multimodal transport route allocation optimization formula comprehensively considers cost, time, and reliability, breaking the limitations of traditional transportation planning that only focuses on a single mode and enhancing supply chain resilience. The dynamic inventory balancing formula introduces a time dimension and risk factors, effectively mitigating demand fluctuations and reducing inventory costs, whereas traditional static inventory management cannot cope with market changes. Furthermore, the formulas are data-driven, with clear parameter sources and transparent calculation processes, helping the group shift from experience-based to scientific decision-making and improving management efficiency. The model framework is flexible, and the formulas can be adjusted according to business changes, supporting the future integration of new businesses such as new energy, and possessing long-term applicability. Attached Figure Description

[0039] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0040] Figure 1 This is a flowchart illustrating a method for balancing production and sales across the entire industry chain, considering value maximization, as provided in Embodiment 1 of the present invention. Detailed Implementation

[0041] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0042] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0043] Example 1:

[0044] Please see Figure 1This embodiment proposes a full-industry chain production and sales synergy balancing method that considers value maximization. The production and sales synergy balancing model takes maximizing group profits as the solution objective and uses the production and sales plans of coal of different sources and qualities sold to potential downstream customers in the next N+12 months through different transportation methods as solution variables to construct a linear objective function for profit: Profit = Revenue - Cost. The model's application scenarios cover the entire industry chain business scope of the group, including coal, coal power, and coal chemicals. It comprehensively calculates the sales revenue of coal, coal power, and chemical products of the group's enterprises, as well as the actual costs incurred in production, procurement, transportation, and warehousing. Based on a comprehensive consideration of limiting factors such as coal type demand matching, coal blending schemes, accessible transportation routes, customer price margins, loading station coal loading capacity, railway / road and waterway transportation capacity, storage capacity, and unloading station coal unloading capacity, it adopts a linear programming interior-point algorithm and uses a high-performance solver to intelligently calculate millions of parameters to find the optimal allocation scheme that maximizes the group's profits in the potential sales path allocation pattern. The model calculations determine the optimal transportation mode (railway, road, or shipping) for different types and qualities of self-produced or purchased coal to be sold to specific downstream customers in a given month of the next N+12. The model results are validated through revenue and cost accounting and resource constraints across the entire industry chain, ensuring that every resource allocation is the optimal solution, maximizing the value per ton of coal, and ultimately driving a comprehensive improvement in the group's overall economic benefits and resource allocation efficiency.

[0045] I. Design Objectives of the Production-Sales Balance Model

[0046] Based on the production and sales operation mechanism and current situation, this plan specifically designs a production and sales balance model, whose core objective is to maximize the group's economic benefits, which is broken down into the following four sub-objectives:

[0047] (1) Optimize supply and demand matching and increase the sales ratio of high-value customers: By improving the matching degree between production and procurement volume and market demand, market demand can be met at the lowest cost; combined with coal sales volume and cost and price situation in the coal power and coal chemical fields, more resources will be tilted to the high-value demand side to help the group maximize profits.

[0048] (2) Optimize the cost structure of the industrial chain and reduce overall operating costs: balance the proportion of self-produced coal and purchased coal, and achieve the best coal acquisition cost under the premise of complying with the procurement policy; on the basis of meeting production and sales needs, further optimize inventory management and sales path selection, and achieve cost optimization through supply and demand balance.

[0049] (3) Enhance supply chain resilience and mitigate supply chain disruption risks: By relying on models to develop N+12 plans and other methods, we can anticipate and respond to uncertainties such as market changes and production fluctuations in advance, thereby reducing the impact of risks; the built-in scenario simulation function can simulate sudden events or crisis situations and preset solutions to enhance supply chain resilience and ensure the normal operation of enterprises.

[0050] (4) Promote continuous improvement and optimization to enhance production and sales management efficiency: dynamically adjust the production and sales balance model based on market feedback to adapt to changes in the external environment and the needs of enterprise development, and achieve continuous optimization; promote China Coal's transformation from an experience-based management model to a data-driven scientific management and operation model.

[0051] II. Overall Approach to Model Construction

[0052] The production and sales balance model aims to maximize the overall value of the group, with cross-industry collaboration among its various sectors as its core and the safe and stable operation of the system as its guarantee. Based on information from the group's subsidiaries regarding production, transportation, and sales, combined with forecasts of future market prices and demand, it is a multi-objective optimization model constructed using linear programming and interior-point algorithms. By solving this model, the optimized values ​​for production, procurement, transportation, storage, sales, and usage at each stage of the group's operations over the next N+12 periods can be obtained. This achieves comprehensive optimization of the group's production factors across "points, lines, and surfaces," guiding the formulation and dynamic monitoring of the group's annual / monthly / weekly / day plans, and promoting overall linkage, efficient coordination, and rapid response in the scheduling and management of the group's various sectors.

[0053] III. Introduction to the Production-Sales Balance Model Algorithm

[0054] The production and sales balance model algorithm selects linear programming optimization method and uses interior-point algorithm for solution, achieving efficient and accurate problem solving. Optimization problems involve finding an optimal control law, designing an optimal control scheme, or an optimal control system based on various research objects and expected goals. For optimization problems, the method of selecting the scheme and specific measures that meet the requirements to obtain the best result is called an optimization method. If the objective and constraint functions of an optimization problem are linear functions of the decision variables, it is called a linear programming problem. The interior-point method iterates from within the feasible region, effectively handling large-scale linear programming problems, and exhibits excellent convergence speed and numerical stability. By continuously optimizing the iteration points, it gradually approaches the optimal solution, ultimately providing an accurate and efficient solution to the linear programming problem. The steps of using optimization methods to solve practical problems are as follows:

[0055] (1) Based on the proposed optimization problem, establish a mathematical model of the optimization problem, determine the variables, and give the constraints and objective function (or performance index).

[0056] (2) Conduct a detailed analysis and study of the established model, and select an appropriate optimization solution method;

[0057] (3) Based on the algorithm of the optimization method, list the flowchart and write the program, use the computer to find the optimal solution, and evaluate the convergence, universality, simplicity, computational efficiency and error of the algorithm.

[0058] IV. Objective and Function Construction for Solving the Production-Sales Balance Model

[0059] The production and sales synergy balance model aims to maximize the group's profits. By systematically analyzing the cost structure and revenue sources of the group's entire business chain (covering all aspects of "production-procurement-transportation-warehousing-sales-use"), it accurately explores the potential for resource allocation, thereby improving the overall utilization efficiency of coal resources.

[0060] The model defines the variables to be solved as: the production and sales plan for coal of different sources (such as owned coal mines and external suppliers) and of different qualities, transported by different modes of transportation such as rail, road, and waterway, to potential downstream customers (including internal coal power and coal chemical sectors and external market customers) over the next N+12 months. Based on this variable setting, the model constructs a linear objective function with "profit = revenue - cost" as its core logic, comprehensively covering the revenue and expenditure of the entire group's supply chain;

[0061] Maximizing Profit = Expected Revenue of All Businesses Across the Group's Supply Chain in the Next 12 Months – Costs of All Businesses Across the Group's Supply Chain in the Next 12 Months

[0062] The corresponding model objective function can be expressed as:

[0063] Maximize profit = Max[Coal profit + Coal chemical profit + Electricity profit]

[0064] The breakdown of revenue and costs is as follows:

[0065] The Group's expected revenue across its entire business chain over the next 12 months will consist of three main parts: direct coal sales revenue, coal chemical product sales revenue, and electricity sales revenue. These three together constitute the core source of the Group's revenue.

[0066] The costs for all business segments across the entire value chain for the next 12 months cover expenditures throughout the entire process from production to sales: including coal production costs (such as mining and washing fees), coal procurement costs (expenditure on coal from external suppliers), chemical production costs (direct costs in the coal chemical product processing process), power production costs (equipment operation and maintenance, labor, and other expenses in the power generation stage), warehousing and transportation costs (warehousing, loading and unloading, and transportation costs of coal and products), and other sales-related expenses (such as market development and customer service expenditures).

[0067] The profit calculation method for each business segment is further refined as follows:

[0068] Coal profit = Total amount of coal needed for blending × Price of coal needed – Production cost of each coal type – Transportation cost of each transportation route

[0069] Electricity profit = Electricity output × Electricity price – Coal consumption for power generation × Coal supply price – Unit cost of non-coal electricity × Electricity generation

[0070] Coal chemical industry profit = Sales volume of various chemicals × Sales price – Coal consumption of various chemicals × Coal supply price – Unit cost of non-coal chemicals × Sales volume

[0071] Through the construction of the above multi-dimensional functions, the model can comprehensively map the profit logic of the group's various businesses, providing accurate quantitative basis for optimal resource allocation.

[0072] It should be noted that this scheme, based on linear programming theory and combined with interior-point algorithms, constructs a production-sales synergy balance model covering the entire coal, coal-fired power, and coal chemical industry chain. The model aims to maximize group profits and enhances its refinement and adaptability by introducing three key calculation formulas. These three formulas address coal blending optimization, multimodal transport route allocation, and dynamic inventory balancing, respectively, solving the pain points of traditional methods such as inaccurate quality control of blended coal, high transportation costs, and large inventory fluctuations. The formula design ensures that the parameters have unique meanings, clear data sources, and clearly defined calculation subjects, thereby improving the model's practicality and operability.

[0073] (I) Coal Mixing Quality Cost Optimization Formula

[0074] This formula is used to calculate the optimal blending scheme that minimizes the blending cost while meeting the calorific value of the blended coal, using a model solver (such as Gurobi or CPLEX). The formula is as follows:

[0075]

[0076] Where: x ij This represents the mixing quantity (in tons) of type i and type j coal, with data sourced from coal mine production plans and quality inspection reports; c ij The unit cost of blending (unit: yuan / ton) includes washing and processing costs, and the data is sourced from the production cost database; n represents the total number of basic coal types available for blending; m represents the number of types of blended coal products to be produced; Q k1 Q represents the actual value of the k-th quality indicator (such as calorific value) after mixing, calculated by weighted average; k2 λ represents the customer's required value for the k-th quality indicator, with data sourced from customer contracts and demand forecasts;k The penalty coefficient for quality deviation is set by the business department based on customer importance; p represents the number of coal quality indicators that need to be controlled and optimized. The formula calculates the minimum blending cost and the optimal blending scheme.

[0077] (II) Optimization Formula for Multimodal Transport Route Allocation

[0078] This formula is used to optimize the selection of transportation routes for coal from the supply point to the demand point using a model solver, considering multimodal transport including rail, road, and waterway, to minimize total transportation costs and time. The formula is as follows:

[0079]

[0080] Where: y rt This represents the transport volume (in tons) of the t-th transport mode on path r, with data sourced from the transport plan; d rt This represents the distance (in kilometers) of transport mode t along path r, with data sourced from a GIS system; f rt This represents the unit distance rate (unit: yuan / ton·km) for transportation mode t on route r, with data sourced from transportation contracts; t rt This represents the transit time (in days) for transit mode t on path r, with data sourced from historical logistics data; u rt The time cost coefficient (unit: yuan / day) is set by the finance department based on the cost of capital occupation; δ rt The path reliability adjustment factor is calculated based on historical delay rates; α, β, and γ are weighting coefficients set by decision-makers according to cost and time priorities; R represents the total number of feasible transportation paths defined in the model; and T represents the number of transportation mode segments available on a single path r. The formula calculates the optimal capacity allocation scheme and its corresponding total cost.

[0081] (III) Dynamic Inventory Balance Formula

[0082] This formula is used to optimize inventory levels at each storage location using a model solver, balancing inventory holding costs and stockout risk, and taking into account demand fluctuations over time. The formula is as follows:

[0083]

[0084] Where: h s This represents the unit inventory holding cost at storage point s (unit: yuan / ton / month), and the data is sourced from financial data; I sτ This represents the inventory level (in tons) at storage point s in month τ, with data sourced from the inventory management system; b s The unit stockout cost (in yuan / ton) for storage point s is set by the sales department based on customer importance; Dsτ ω represents the predicted demand (in tons) of storage point s in month τ, with data sourced from the demand forecasting system; S represents the total number of storage points in the group's supply chain network that require independent inventory optimization management; τ This represents the inventory fluctuation penalty coefficient, set by the planning department; ΔI sτ This represents the change in inventory, used to smooth out inventory fluctuations. The formula calculates the optimal inventory level plan and the expected inventory cost.

[0085] This solution significantly improves the accuracy and practicality of the production-sales coordination model by introducing three key calculation formulas. Compared to traditional technologies, this solution achieves dual control of quality and cost through a coal blending quality cost optimization formula, avoiding the quality over- or under-quality problems caused by relying on experience-based blending in traditional methods. The multimodal transport route allocation optimization formula comprehensively considers cost, time, and reliability, breaking the limitations of traditional transportation planning that only focuses on a single mode and enhancing supply chain resilience. The dynamic inventory balancing formula introduces a time dimension and risk factors, effectively mitigating demand fluctuations and reducing inventory costs, while traditional static inventory management cannot cope with market changes. In addition, the formulas are data-driven, with clear parameter sources and transparent calculation processes, which helps the group shift from experience-based to scientific decision-making and improve management efficiency. The model framework is flexible, and the formulas can be adjusted according to business changes, supporting the future inclusion of new businesses such as new energy, and has long-term applicability.

[0086] V. Production and Sales Balance Model Framework and Solution Steps

[0087] Solving the production-sales balance model begins with data preparation at the model input layer: updating master data such as products and coal mines; generating unconstrained demand, sales plans, and price lists on the demand side using "M1+12" forecasting; and similarly defining production plans and cost tables on the supply side. The model layer, aiming to maximize profit, constructs an optimized model based on constraints such as production capacity and coal type. The output layer presents constrained operating plans (supply and demand, production, sales, and procurement schemes) and revenue forecasts (group and segment revenue tables). Through the logic of "data preparation - model calculation - result output," production-sales balance and optimal efficiency are achieved.

[0088] The steps for solving the model are as follows:

[0089] (1) Step 1: Data input

[0090] Data input is the foundation for the operation of the production and sales balance model. It is necessary to comprehensively integrate information from the three dimensions of supply, demand, and storage and transportation to ensure the integrity and accuracy of the data and provide a reliable basis for subsequent model calculations.

[0091] Supply data: Coal production and procurement plans for the next 12 months are collected through automatic system extraction or manual entry by business departments. These plans serve as the core foundation for the available coal resources in the model calculations, directly determining the total amount and structure of resources that the model can allocate.

[0092] Demand data: Acquired through a multi-channel integration approach. On one hand, coal demand forecasts for the next 12 months are extracted from the system or submitted by business departments (covering internal coal power and coal chemical sectors as well as external cooperative customers), while corresponding price forecast information is collected simultaneously. On the other hand, capacity forecast data, product sales prices, and production cost details are specifically collected from internal power plants and chemical plants. This data will be used to accurately calculate the revenue scale and profit margin of the electrochemical business, providing support for the value assessment of the demand side.

[0093] Storage and transportation data: Two key types of information are collected. First, the current effective sales and transportation routes, which are used to regulate the scope of coal circulation and ensure that resource allocation is strictly limited within the permitted commercial flow framework. Second, transportation-related parameters (including transportation rates and transportation distances) and warehousing data (warehousing capacity limits). Among them, transportation parameters are used to accurately calculate logistics costs, while warehousing capacity data serves as a hard constraint on inventory allocation.

[0094] (2) Model calculation

[0095] The model calculation phase is goal-oriented, combining constraints to achieve optimal resource allocation. The core objective is clearly defined as "maximizing group profits." Based on this objective, the model will use algorithms to solve coal allocation schemes from coal mines, various suppliers, to different customer groups, ensuring that resource flows create maximum value for the group. Simultaneously, various constraints are systematically analyzed from the input data to form the boundary limits for model calculations. These include monthly and yearly production capacity limits (to avoid resource supply exceeding actual production capacity), supply guarantee requirements (to define the bottom line for demand fulfillment rate and ensure the needs of core customers), warehouse capacity limits (to prevent inventory backlog exceeding storage capacity), transportation capacity limitations (to ensure that transportation resources match the allocated volume), and route constraints (strictly adhering to established circulation route rules), etc., ensuring the feasibility of the scheme through multiple constraints.

[0096] (3) Step 3: Output of model results

[0097] After the model calculation is completed, two types of core results will be output, providing direct reference for business decision-making.

[0098] Production and Sales Balance Plan: This plan is based on the input production and sales plan and various forecast data, taking into full account all constraints, and is generated through algorithmic calculation. The plan has a fine granularity, specifying the resource output of each coal mine, the resource input of each customer group, and the allocation details of different coal products, ultimately achieving the optimal production and sales match for the group's profits.

[0099] Operating revenue forecast: Based on the generated production and sales balance plan, and combined with the price system (including sales price, purchase price, etc.) and cost information (covering production cost, logistics cost, etc.) in the input data, the model will further estimate the potential revenue scale and profit level of the group in each month of the next 12 months, providing data support for the group to predict operating efficiency and formulate financial planning.

[0100] VI. Production and Sales Balance Model Data Indicators

[0101] The data requirements of the production-sales synergy balancing model cover multiple aspects to support its effective operation and accurate decision-making. Key assumptions must be based on predictable demand, stable production capacity, fixed product types, and controllable costs and transportation. Basic data must include master data and dictionary management information such as participating organizational structures, product specifications, suppliers, customers, storage locations, transportation routes and stations. Input data involves coal production and procurement plans, costs, and supplier capabilities on the supply side; storage and transportation capacity, inventory, transportation capacity, and costs; and sales plans, prices, customer satisfaction rates, and capacity, plans, consumption, and prices related to coal, power, and chemical self-use on the demand side. Simultaneously, it must adhere to multiple constraints related to supply, demand, storage, and transportation, and the input data must pass verification rules for prices, delivery points, capacity-to-output relationships, costs, loading stations, procurement volume, and supply capacity. The final output is a balanced production-sales plan and projected operating revenue data.

[0102] (1) Model Assumptions

[0103] Demand forecasting assumptions: It is assumed that market demand and production capacity can be accurately predicted to a certain extent, that is, enterprises can predict market demand and their own production capacity in the future.

[0104] Production capacity assumption: It is assumed that the company's production capacity has a clear upper limit and is relatively stable for a considerable period of time in the future.

[0105] Product variety assumption: It is assumed that the product varieties produced in the future will be the same as those currently provided, and that different varieties of coal can be converted between each other in various links of the industrial chain.

[0106] Production cost assumptions: It is assumed that the production cost per unit of product is predictable and controllable within a certain period of time in the future.

[0107] Transportation cost assumptions: It is assumed that transportation costs are predictable and controllable, and that transportation costs can be reduced by optimizing transportation routes and methods.

[0108] Transportation assumptions: It is assumed that China Coal's destination transportation capacity is subject to certain constraints and can be predicted in the future.

[0109] Granularity assumption: It is assumed that the current production and sales balance model input data is in the monthly dimension, and generates production and sales plans for the next 12 months.

[0110] Business scope assumption: It is assumed that the current industrial chain only involves coal, electricity and chemical businesses.

[0111] (2) Model input data

[0112] Supply side: Coal production (coal production capacity information, coal mine production plan, coal mine production cost), coal procurement (supplier capacity information, supplier procurement plan, coal procurement cost).

[0113] Warehousing and Transportation: Coal warehousing (warehouse location and capacity, warehousing costs, actual inventory records), coal transportation (geographical location of receiving and dispatching goods, route status, route distance, transportation rates).

[0114] Demand side: Coal sales (customer information, coal demand plan, sales price forecast, coal type, customer type and target demand fulfillment rate), coal self-consumption (coal procurement plan, chemical product data, chemical production cost, chemical price forecast, coal-fired power production cost, coal-fired power sales price).

[0115] (3) Model constraints

[0116] Supply constraints: Coal mine capacity constraints (annual coal mine capacity <= planned annual capacity * maximum capacity coefficient, monthly coal mine capacity <= (planned annual capacity / 12) * maximum capacity coefficient), external supply capacity constraints (annual external coal purchase volume <= annual maximum purchase capacity, monthly external coal purchase volume <= monthly maximum purchase capacity), minimum supply constraints (monthly coal mine output >= monthly minimum coal supply, external coal purchase volume >= monthly minimum coal purchase volume).

[0117] Demand constraints: Minimum demand fulfillment constraint (monthly coal supply >= customer demand forecast * minimum demand fulfillment rate (%), annual coal supply >= customer demand forecast * minimum demand fulfillment rate (%)), maximum demand oversupply constraint (monthly coal supply <= customer demand forecast * maximum oversupply rate (%), annual coal supply <= customer demand forecast * maximum oversupply rate (%)).

[0118] Storage and transportation constraints: Sales path constraint (the path status is 1 if there has been a transaction or manual activation in the past 720 days, otherwise it is 0), maximum transportation capacity constraint (the maximum freight volume (tons) that the transportation path can handle per month), and storage capacity constraint (inventory (tons) <= maximum storage capacity (tons), inventory (tons) >= safety stock (tons)).

[0119] Capacity constraint: In any forecast month, coal sales from any coal mine (supplier) are less than the capacity (maximum supply capacity) of that coal mine (supplier).

[0120] Sales path constraint: For sales paths that do not have a pairing relationship, the sales volume of coal originating from the coal mine (supplier) is equal to 0.

[0121] Capacity constraint: In any forecast month, the sum of the transport volume through a certain transport route is less than the maximum capacity of that route.

[0122] Calorific value constraint for blended coal: The calorific value of the blended coal should not be less than the required calorific value of the blended coal.

[0123] Calorific value constraint for blended coal: The calorific value of the blended coal should not be less than the required calorific value of the blended coal.

[0124] (4) Model calculation data verification rules

[0125] Validate the completeness of cross-year data entry (single table validation, second-level unit validation, all sections; if the reporting range involves cross-year data, check whether the current year and the following year's data are entered; check whether the months entered for cross-year data are correct).

[0126] The belt conveyor origin / boarding station / destination station / railway line and capacity constraints are filled in correctly (single form verification, second-level unit, logistics, each belt conveyor origin / boarding station / destination station / railway line corresponds to a capacity constraint per month).

[0127] Check the standardization of the drop-off station name (single form check, second-level unit, logistics, the drop-off station is one station, multiple stations cannot be filled in, and there cannot be spaces or other punctuation marks in the station name).

[0128] Verification of the relationship between coal production plans and capacity plans (multi-table verification, secondary unit, coal, same reporting month and year, reporting unit, secondary unit, tertiary unit, the monthly production plan of a certain coal type should be less than or equal to the monthly maximum production capacity of commercial coal of that coal type).

[0129] Verification of the relationship between the coal procurement plan and the supplier's supply capacity (multi-table verification, second-level unit, marketing, for the same reporting month and year, reporting unit, second-level unit, third-level unit, supplier, and coal name, the coal procurement volume should be less than or equal to the supplier's supply capacity for the corresponding coal type).

[0130] Verification of the relationship between chemical production plans and coal consumption plans (multi-table verification, secondary unit, coal chemical industry, production plan, same reporting month and year, reporting unit, secondary unit, tertiary unit, if the planned production volume of a certain chemical product in a certain month is greater than 0, then the chemical product must have a corresponding planned coal consumption volume in that month, and cannot be empty).

[0131] Verification of the relationship between power generation plan and power coal consumption plan (multi-table verification, secondary unit, power, power generation plan, in the same reporting month and year, reporting unit, secondary unit, if the planned power generation of a certain tertiary unit in a certain month is greater than 0, then the secondary unit must have a corresponding planned power coal consumption in that month, and it cannot be empty).

[0132] Coal supply and demand relationship verification (multi-table verification, the whole group, coal, marketing, the upper limit of total coal supply should be greater than or equal to total demand multiplied by demand satisfaction rate, of which the upper limit of self-produced coal production should be less than total coal demand, so that there is an optimization scheme for production and sales balance).

[0133] VII. Results of the Production-Sales Balance Model

[0134] The balance model integrates data from both the production and sales ends to achieve precise comparison and dynamic adjustment of supply and demand. Specifically, the model first extracts production plan data from production enterprises, and then integrates coal demand data from sales enterprises (covering detailed demand from internal sectors and external customers) and purchased resource data. Through multi-dimensional cross-comparison, it accurately calculates the difference between supply and demand.

[0135] Based on the comparison results, the model will clearly output two key pieces of information: first, the self-production that failed to match demand (i.e., the part of the production plan that was not covered by demand), and second, the self-production that needs to be increased to balance the supply and demand gap (i.e., the demand gap that needs to be made up by increasing internal production).

[0136] These data will serve as the core analytical basis for the production and sales balance meeting. During the meeting, relevant departments such as production, sales, and procurement will conduct an in-depth analysis of the reasons for the discrepancies—for example, they may stem from deviations in market demand forecasts, untimely adjustments to production plans, or poor coordination of externally sourced resources. A detailed report explaining these discrepancies will be generated. Furthermore, based on the causes of the discrepancies and actual business scenarios, the meeting will further discuss and propose targeted solutions, such as adjusting production plans to match demand fluctuations, optimizing the procurement pace of externally sourced resources, and coordinating the demand priorities of various internal sectors. Ultimately, through multi-party collaboration, the meeting aims to promote a dynamic balance between supply and demand and improve resource allocation efficiency.

[0137] In practical applications, the production and sales balance model realizes the collaborative optimization of the entire production and sales system by integrating data from various links such as production, transportation, sales, coal chemical industry, and coal-electricity joint operation. Through comprehensive consideration and precise decision-making on each link, it effectively improves the operation efficiency of enterprises, reduces costs, enhances the competitiveness of enterprises in the market, and provides a solid guarantee for the sustainable development of enterprises.

[0138] Based on detailed historical data, relying on the repeatedly verified comprehensive algorithm logic and precise model operation system, the model verification successfully constructs a linkage mechanism covering the entire production and sales chain, achieving efficient connection of the production and sales links. Throughout this process, ensuring the stable supply of social energy and safe production has always been the core premise. On this basis, multiple measures are taken to comprehensively push the business results to a new level. Specifically, by introducing a scientific production and sales balance model and deeply applying it, a detailed before-and-after comparison analysis of various indicators of production and sales is carried out. Based on the analysis results, on the premise of strictly following the production capacity control requirements, the production volume is precisely adjusted, and it is predicted that the production volume will increase, which not only ensures the rationality of the production rhythm but also fully taps the production capacity potential. At the same time, for the sales link, efforts are made to optimize the sales flow layout. By accurately docking with market demand, adjusting regional sales strategies, etc., the sales efficiency and benefits are effectively improved, and it is expected to drive the revenue to increase, injecting strong impetus into the improvement of the overall business performance.

[0139] This solution realizes cross-industry collaborative optimization by integrating data resources across the entire industrial chain, significantly improving the efficiency of data processing and analysis. In the traditional operation mode of energy enterprises, links such as coal mining, coal-electricity production, coal chemical industry, and coal sales often act independently, with inconsistent data standards and serious information island phenomena. This not only leads to fragmented data processing but also restricts the enterprise's comprehensive understanding of the operation status of the entire industrial chain. This solution breaks down this data barrier, unifies and processes the data of each link in the entire industrial chain, and constructs a comprehensive and complete data system. Through data cleaning, transformation, and standardization processing, the accuracy and consistency of the data are ensured, providing a solid foundation for subsequent analysis and optimization.

[0140] The realization of cross-industry collaborative optimization further improves the data processing efficiency. This invention adopts advanced data processing technologies and algorithms to efficiently analyze and mine the integrated data. Through multi-dimensional data alignment and dynamic analysis, the correlation and influence between each business unit can be captured in real time, providing strong support for decision-making. This collaborative optimization method not only avoids the information island phenomenon but also improves the comprehensiveness and timeliness of data processing, enabling enterprises to respond to market changes more quickly and accurately.

[0141] This solution effectively reduces resource waste and lowers operating costs by optimizing resource allocation. In the operation of energy companies, the rationality of resource allocation directly impacts costs and profits. Traditional resource allocation methods are often based on experience or static assumptions, making it difficult to adapt to dynamic market changes. This solution establishes a profit optimization function based on key parameters such as the cost structure of each business unit, market demand, and price fluctuations. Using operations research methods, the optimal resource allocation scheme is calculated, ensuring efficient resource utilization and profit maximization.

[0142] Specifically, this plan conducts an in-depth analysis of the cost structure of coal mining, coal-fired power generation, coal chemical industry, and coal sales, clarifying the cost composition and influencing factors of each stage. Simultaneously, it establishes a dynamic cost forecasting model by incorporating external factors such as market demand and price fluctuations. Based on this, operations research methods are used to find the optimal resource allocation scheme. This plan not only considers the synergistic effects between business units but also fully takes into account dynamic market changes, ensuring the rationality and effectiveness of resource allocation. By implementing this plan, enterprises can effectively reduce unnecessary resource consumption and cost expenditures, thereby improving overall profitability.

[0143] This solution, by constructing decision variables and constraints, ensures the feasibility and effectiveness of the optimization scheme in practical operation, significantly enhancing the system's stability. In constructing the optimization problem, this solution fully considers various practical factors such as coal mine constraints, external procurement constraints, loading station constraints, railway constraints, and sales area constraints. These constraints reflect the actual limitations and conditions in the enterprise's operation, ensuring the stability and reliability of the optimization scheme in practical applications.

[0144] Specifically, this solution conducts an in-depth analysis of factors such as coal mine production capacity, the supply capacity of purchased coal, the transportation capacity of loading stations, the transportation capacity of railways, and market demand in sales regions, clarifying the constraints and scope of influence of each factor. Based on this, a system of decision variables and constraints is constructed to ensure the feasibility and effectiveness of the optimization solution in practical operation. Furthermore, this solution employs advanced optimization algorithms and solution techniques to efficiently solve the optimization problem. Through continuous iteration and optimization, the stability and reliability of the optimization solution are ensured, providing strong decision support for enterprises.

[0145] This solution utilizes intelligent algorithms for multi-dimensional data alignment and dynamic analysis, significantly improving the system's adaptability and flexibility to market changes. In the context of a rapidly changing energy market, enterprises need the ability to respond quickly to market shifts. Traditional optimization algorithms are often based on static assumptions and struggle to adapt to dynamic market changes. The intelligent algorithms employed in this solution can capture real-time market dynamics and perform multi-dimensional data alignment and dynamic analysis. Through continuous learning and optimization, the solution can be quickly adjusted to meet the demands of changing markets.

[0146] Specifically, this solution employs advanced machine learning algorithms and big data analytics to collect and analyze market data in real time. By mining and analyzing historical data, the patterns and trends of market changes are clearly identified. Based on this, a dynamic market forecasting model is constructed, providing strong market support for the optimization plan. Simultaneously, this solution also utilizes multi-objective optimization techniques to comprehensively consider and weigh multiple optimization objectives. By solving the multi-objective optimization problem, an optimized solution satisfying multiple objectives is obtained, thereby improving the company's overall profitability and market competitiveness.

[0147] This solution, through multi-objective optimization, found the optimal solution to maximize value under various conditions, significantly improving the company's overall profit margin and market competitiveness. In constructing the optimization problem, this solution fully considered the company's overall interests and long-term development. By constructing multiple objective functions, including a model for maximizing transportation volume, minimizing cost, maximizing profit, and a multi-objective optimal model, the optimization problem was comprehensively considered and weighed. By solving for the optimal values ​​of these objective functions, the company's value was maximized.

[0148] Specifically, this solution constructs a multi-objective optimization model by comprehensively considering and balancing multiple objectives such as transportation volume, cost, and profit. Advanced optimization algorithms and solution techniques are employed to efficiently solve the multi-objective optimization problem. Through continuous iteration and optimization, an optimal solution satisfying multiple objectives is obtained. This solution not only considers the company's short-term interests but also fully considers its long-term development. By implementing this solution, the company can achieve optimal allocation and efficient utilization of resources, improving overall profitability and market competitiveness. Simultaneously, this solution provides strong decision support for the company, helping it better cope with market changes and challenges, and achieve sustainable development.

[0149] Example 2:

[0150] A value-maximizing, supply chain-wide production and sales synergy balancing system, which can realize the value-maximizing, supply chain-wide production and sales synergy balancing method described in Example 1, includes:

[0151] Data acquisition module: used to acquire supply data, demand data, and storage and transportation data;

[0152] Data processing module: Based on the supply data, demand data, and storage and transportation data, and with the goal of maximizing group profits, it constructs a linear programming model that includes coal blending quality cost optimization, multimodal transport route allocation optimization, and dynamic inventory balance, and uses the interior point algorithm to solve it, to obtain the production and sales balance plan and operating income forecast for the next N+12 months.

[0153] Data output module: Used to output the production and sales balance plan and operating profit forecast.

[0154] Example 3:

[0155] This invention also provides a full-chain production and sales synergy balancing device that considers maximizing value, which can realize the full-chain production and sales synergy balancing method that considers maximizing value as described in Embodiment 1, including a processor and a storage medium.

[0156] The storage medium is used to store instructions;

[0157] The processor is configured to operate according to the instructions to perform the steps of the following method:

[0158] Obtain supply data, demand data, and storage and transportation data;

[0159] Based on the aforementioned supply data, demand data, and storage and transportation data, and with the goal of maximizing group profits, a linear programming model is constructed that includes coal blending quality cost optimization, multimodal transport route allocation optimization, and dynamic inventory balance. The model is then solved using an interior point algorithm to obtain the production and sales balance plan and operating income forecast for the next N+12 months.

[0160] Output the production and sales balance plan and operating profit forecast.

[0161] Example 4:

[0162] This invention also provides a computer-readable storage medium that can implement the value-maximizing, supply chain-wide production and sales synergy balancing method described in Embodiment 1. The medium stores a computer program that, when executed by a processor, performs the following steps:

[0163] Obtain supply data, demand data, and storage and transportation data;

[0164] Based on the aforementioned supply data, demand data, and storage and transportation data, and with the goal of maximizing group profits, a linear programming model is constructed that includes coal blending quality cost optimization, multimodal transport route allocation optimization, and dynamic inventory balance. The model is then solved using an interior point algorithm to obtain the production and sales balance plan and operating income forecast for the next N+12 months.

[0165] Output the production and sales balance plan and operating profit forecast.

[0166] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative and not exhaustive. All modifications within the scope of this invention or its equivalents are included in this invention.

[0167] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0168] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for achieving synergistic balance between production and sales across the entire industry chain, considering value maximization, characterized by: include: Obtain supply data, demand data, and storage and transportation data; Based on the aforementioned supply data, demand data, and storage and transportation data, and with the goal of maximizing group profits, a linear programming model is constructed that includes coal blending quality cost optimization, multimodal transport route allocation optimization, and dynamic inventory balance. The model is then solved using an interior point algorithm to obtain the production and sales balance plan and operating income forecast for the next N+12 months. Output the production and sales balance plan and operating profit forecast.

2. The method for balancing production and sales across the entire industry chain, considering value maximization, as described in claim 1, is characterized in that... The acquisition of supply data, demand data, and storage and transportation data specifically includes: Obtain coal production plans, procurement plans, and cost data for the next 12 months as supply data; Obtain coal demand forecasts for the next 12 months, capacity forecasts for the internal coal power and coal chemical sectors, product sales prices and production cost data as demand data; Obtain transportation routes, transportation parameters, and storage capacity data as storage and transportation data.

3. The method for balancing production and sales across the entire industry chain, considering value maximization, as described in claim 1, is characterized in that... The formula for optimizing the cost of coal blending quality is as follows: Where: x ij c represents the mixing amount of coal of type i and coal of type j; ij Indicates the unit cost of mixing; n represents the total number of basic coal types available for mixing; m represents the number of types of mixed coal products to be produced; Q k1 Q represents the actual value of the k-th quality index after mixing; k2 λ represents the customer's required value for the k-th quality indicator. k represents the penalty coefficient for quality deviation; p represents the number of coal quality indicators that need to be controlled and optimized.

4. The method for balancing production and sales across the entire industry chain, considering value maximization, as described in claim 1, is characterized in that... The multimodal transport route allocation optimization formula is as follows: Where: y rt d represents the transport volume of the t-th transport mode on path r; rt f represents the distance of transport mode t along path r; rt This represents the unit distance rate for transport mode t on route r; t rt Indicates the transit time of transit mode t on path r; u rt δ represents the time cost coefficient. rt α represents the path reliability adjustment factor; α, β, and γ are weighting coefficients; R represents the total number of feasible transportation paths defined in the model; and T represents the number of transportation mode segments available on a single path r.

5. The method for balancing production and sales across the entire industry chain, considering value maximization, as described in claim 1, is characterized in that... The dynamic inventory balancing formula is as follows: Where: h s I represents the unit inventory holding cost at storage point s; sτ b represents the inventory level at storage point s in month τ; s D represents the unit stockout cost at storage point s; sτ ω represents the projected demand at storage point s in month τ; S represents the total number of storage points in the group's supply chain network that require independent inventory optimization management; τ Indicates the inventory fluctuation penalty coefficient; ΔI sτ This indicates the change in inventory.

6. The method for balancing production and sales across the entire industry chain, considering value maximization, as described in claim 1, is characterized in that... The output of the production and sales balance plan and operating profit forecast includes: The production and sales balance plan will be distributed to the production, procurement, logistics and sales departments for implementation, and the operating profit forecast will be submitted to the group management to assist in business decision-making.

7. The method for balancing production and sales across the entire industry chain, considering value maximization, as described in claim 1, is characterized in that it also... include: Based on the aforementioned production and sales balance plan, monthly production and sales execution deviation monitoring indicators are generated for each of the next N+12 months. Collect actual production and sales data in real time, and calculate the execution deviation between the actual production and sales data and the production and sales balance plan for the corresponding month; When the execution deviation exceeds the preset threshold of the production and sales execution deviation monitoring index, a plan adjustment warning is triggered, and the steps of acquiring data, processing data, and outputting results are re-executed; otherwise, no action is taken.

8. A full-chain production and sales synergy balance system considering value maximization, characterized by: include: Data acquisition module: used to acquire supply data, demand data, and storage and transportation data; Data processing module: Based on the supply data, demand data, and storage and transportation data, and with the goal of maximizing group profits, it constructs a linear programming model that includes coal blending quality cost optimization, multimodal transport route allocation optimization, and dynamic inventory balance, and uses the interior point algorithm to solve it, to obtain the production and sales balance plan and operating income forecast for the next N+12 months. Data output module: Used to output the production and sales balance plan and operating profit forecast.

9. A full-chain production and sales synergy balancing device that considers value maximization, characterized in that: Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 7.

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