A logistics network balancing optimization method considering whole-chain coordination of production, transportation, marketing, storage and use

Abstract

The application discloses a kind of logistics network balance optimization methods considering production, transportation, marketing, storage and use whole chain cooperation, comprising the following steps: obtaining the basic information of each board industry model in the integration range of relevant enterprises;Obtain the coal logistics network balance optimization model data mapping of production, transportation, marketing, storage and use whole chain cooperation;Based on the foregoing basic information and data mapping, the decision variables and constraint conditions of optimization problem are constructed, the constraint conditions include coal mine constraints, outsourcing constraints, loading platform constraints, railway constraints and sales area constraints;The objective function of optimization problem is constructed, and the objective function includes the maximum model of transportation quantity, the minimum model of cost, the maximum model of profit and multi-objective optimal model;Based on the decision variables and constraint conditions, the optimal value of each objective function is solved respectively.

Description

Technical Field

[0001] This invention relates to a logistics network balancing optimization method that considers the coordination of the entire chain of production, transportation, sales, storage and use, and belongs to the field of logistics engineering 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 diverse types of coal and multiple 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 operational efficiency for enterprises. Based on intelligent optimization models, by formulating reasonable procurement and transportation strategies, purchasing the appropriate quantity at the right time, and using the optimal transportation routes, while also considering the use of surplus transportation capacity for other types of products, large energy enterprises can maximize their overall operational efficiency.

[0003] For large energy enterprises, an integrated operation model is designed to achieve "large and comprehensive" business operations. "Comprehensive" refers to covering the entire upstream and downstream of the coal industry, while "large" signifies the full coordination of all sectors involved in coal production, transportation, and sales. The former represents the integrated development of large energy enterprises. Under the assumption of fixed external market conditions, vertical integration and horizontal integration provide predictable potential profitability or a potential minimum cost level for the internal production and operation of large energy enterprises. The latter refers to how the vertical control and horizontal coordination implemented by large energy enterprises determine the extent of the gap between the actual production and operation results and the aforementioned potential profitability or potential minimum cost level.

[0004] With the goal of maximizing the overall value of integrated coal-power projects in large energy enterprises, and focusing on the coordinated scheduling of production, transportation, sales, storage, and utilization, this paper constructs an industrial balance model system for large energy enterprises. This model can provide a reference for the formulation of annual plans for large energy enterprises, supporting intelligent scheduling and scientific decision-making. The model constructs intervals and sets model variables based on reported data after conflict resolution, allowing each variable to vary within a certain interval. Simultaneously, with the goal of maximizing the overall value of large energy enterprises, the model's objective function and various constraints are constructed. It simulates and calculates annual values ​​for key nodes of large energy enterprises in the following year, such as output, transportation volume, optimal transportation routes, and sales volume, providing a reference for the formulation of annual plans for large energy enterprises. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a logistics network balancing optimization method that considers the coordination of the entire chain of production, transportation, sales, storage, and use. This method solves the problem in existing technologies where it is difficult to manually calculate and customize the most efficient procurement and transportation strategies due to the variety of coal types and transportation routes.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain includes the following steps:

[0008] Obtain basic information on the industrial models of each segment within the integrated scope of relevant enterprises;

[0009] Obtain data mapping of a balanced optimization model for the entire coal logistics network, encompassing production, transportation, sales, storage, and consumption;

[0010] Based on the aforementioned basic information and data mapping, the decision variables and constraints for the optimization problem are constructed. The constraints include coal mine constraints, external procurement constraints, loading station constraints, railway constraints, and sales area constraints.

[0011] Construct the objective function for the optimization problem, which includes the model of maximizing transportation volume, minimizing cost, maximizing profit, and multi-objective optimality.

[0012] Based on the decision variables and constraints, the optimal values ​​of each objective function are solved separately.

[0013] Furthermore, the basic information of the industrial models of each sector within the integrated scope of the aforementioned relevant enterprises includes: coal subsidiaries and their third-level units, coal trading companies, railway lines, loading lines, railway stations, sales regions, and the basic network information and operational capabilities of the power plant sector balance models.

[0014] Furthermore, the data mapping of the aforementioned coal logistics network balance optimization model that coordinates the entire production, transportation, sales, storage, and consumption chain includes:

[0015] Annual output of self-produced coal, annual total production cost of self-produced coal, annual purchasing capacity of purchased coal, annual purchasing cost of purchased coal, annual demand capacity of sales area, annual unit selling price of sales area, annual unit sales cost of sales area, length of railway line section, annual unit freight rate of railway line, annual transportation cost of railway line, annual transportation capacity of railway line, coal transportation line, annual transportation capacity of large logistics, large logistics transportation line, annual transportation capacity of local coal, and local coal transportation line.

[0016] Furthermore, the aforementioned decision variables include the annual output of each coal mine. Coal sales volume at coal mines The amount of coal transported from each coal mine to each loading station The amount of purchased coal transported by each trading company to each loading station The amount of self-produced coal transported from each loading station to each unloading station via each transportation route Purchased coal volume transported from each loading station to each unloading station via various transportation routes Coal volume transported from each railway unloading station to each sales region

[0017] Furthermore, the conditions for constructing the aforementioned coal mine constraints include:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] In the formula: For coal mine output; n v This represents the total number of loading platforms; This refers to the amount of coal used by the coal mine itself. This refers to the amount of coal at the mine mouth. This refers to the volume of coal sold by the coal mine. Let m be the amount of coal at the kth loading station corresponding to the mth coal mine. Let be the association matrix of the coal mine and the loading station, which is a 0-1 variable, where 1 indicates that the m-th coal mine is connected to the k-th loading station, and 0 indicates that they are not connected. The reported coal production of the m-th coal mine; These represent the coal production relaxation amount, lower limit of relaxation amount, and upper limit of relaxation amount for the m-th coal mine, respectively. The approved coal production capacity for the m-th coal mine. This represents the amount of coal sold locally by the m-th coal mine. Let represent the relaxation amount, lower limit, and upper limit of the local coal sales of the m-th coal mine.

[0025] Furthermore, the conditions for establishing the aforementioned external purchase constraints include:

[0026]

[0027]

[0028]

[0029] In the formula: Let O be the amount of coal purchased by the ith company at the kth loading station. This represents the amount of purchased coal reported by the o-th company at the k-th loading station. Let $\frac{0}{k}$ represent the amount of coal that can be loosened from external sources at the $k$ loading station for the $o$-th company, including the lower limit and upper limit of the loosening amount.

[0030] Furthermore, the aforementioned constraints for constructing the loading platform include:

[0031]

[0032]

[0033]

[0034] Where: n m n represents the total number of coal mines. p The total number of externally acquired companies; Let be the amount of coal at the k-th loading station; Let m be the amount of coal at the kth loading station corresponding to the mth coal mine. Let be the association matrix of the coal mine and the loading station, which is a 0-1 variable, where 1 indicates that the m-th coal mine is connected to the k-th loading station, and 0 indicates that they are not connected. Let K be the local coal quantity at the k-th loading station. The amount of coal purchased by the o-th company at the k-th loading station; The association matrix of the o-th company corresponding to the k-th loading station is a 0-1 variable, where 1 indicates that the o-th company purchases external coal at the k-th loading station, and 0 indicates that it does not purchase coal. Let n be the loading capacity of the k-th loading platform; rl This represents the total number of railway loading stations. Let be the association matrix between the k-th loading platform and the i-th railway loading station, which is a 0-1 variable, where 1 indicates that the k-th loading platform is connected to the i-th railway loading station, and 0 indicates that they are not connected; The amount of coal produced by the i-th railway loading station corresponds to the k-th loading platform; The amount of coal purchased from outside the railway loading station corresponds to the kth loading platform.

[0035] Furthermore, the aforementioned railway constraints include constraints on railway loading stations, railway line sections, and railway unloading stations;

[0036] The constraints for railway loading stations are as follows:

[0037]

[0038]

[0039]

[0040]

[0041] Where: n rr n represents the total number of railway transport routes. ru n represents the total number of railway unloading stations. v This represents the total number of loading platforms; The amount of self-produced coal transported from the i-th railway loading station to the j-th railway unloading station via the l-th transportation route; The amount of purchased coal transported from the i-th railway loading station to the j-th railway unloading station via the l-th transportation route; The total amount of coal transported from the i-th railway loading station to the j-th railway unloading station via the l-th transportation route; Let i be the loading capacity of the i-th railway loading station;

[0042] The constraints for constructing railway line sections are as follows:

[0043]

[0044]

[0045] In the formula: The amount of coal transported via the l-th railway route; Let be the association matrix of the railway line section a corresponding to the l-th transportation route. It is a 0-1 variable, where 1 indicates that the railway line section a belongs to the l-th transportation route, and 0 indicates that it does not belong to the l-th transportation route. The reported transport capacity for the railway line section a;

[0046] The constraints for constructing railway unloading stations are as follows:

[0047]

[0048] In the formula: Let J be the amount of coal unloaded at the j-th railway unloading station. Let be the unloading capacity of the j-th railway unloading station.

[0049] Furthermore, the aforementioned sales territory constraint conditions include:

[0050]

[0051]

[0052]

[0053]

[0054] Where: n s Total number of sales regions; Let J represent the amount of coal at the j-th railway unloading station corresponding to the s-th sales region. Let be the association matrix of the s-th sales region and the j-th railway unloading station, which is a 0-1 variable, where 1 indicates that the s-th sales region and the j-th railway unloading station are connected, and 0 indicates that they are not connected; Let be the amount of coal in the s-th sales region; The coal demand reported by the s-th sales region; The demand capacity of the s-th sales region; Let slack, lower limit, and upper limit be the customer demand slack for the s-th sales region.

[0055] Furthermore, the aforementioned maximum transport volume expression is as follows:

[0056]

[0057] Furthermore, the aforementioned expression for minimum cost is:

[0058]

[0059] In the formula: Let m be the unit total production cost of the m-th coal mine; Let $ be the unit purchase cost of coal purchased by the $o$-th company at the $k$-th loading station. This represents the unit freight rate for the l-th transport route.

[0060] Furthermore, the aforementioned expression for maximizing profit is:

[0061]

[0062] In the formula: C is the average selling price per unit.

[0063] Furthermore, the aforementioned multi-objective optimal model is as follows:

[0064] maxαf1+βf2+γf3

[0065] Where α, β, and γ are the weight values ​​given in the maximum transport volume model, the minimum cost model, and the maximum profit model, and α+β+γ=1.

[0066] The beneficial effects achieved by this invention are as follows:

[0067] By constructing intelligent optimization models, reasonable procurement and transportation strategies can be formulated to ensure that the right amount of coal is purchased at the right time and the optimal transportation route is adopted. At the same time, the surplus transportation capacity can be used for other types of products, thereby maximizing the overall operating efficiency of large energy enterprises. Attached Figure Description

[0068] Figure 1 These are the input variables and objective function of the model in this invention. Detailed Implementation

[0069] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0070] This embodiment discloses a logistics network balancing optimization method for large energy enterprises that considers the coordination of the entire chain of production, transportation, sales, storage and use of multiple types of logistics products.

[0071] To achieve the above objectives, the following implementation steps are included:

[0072] Step 1: Analyze the basic information of the industrial model of each sector of coal, railway, and port within the integrated scope of large energy enterprises, including coal subsidiaries and their third-level units, coal trading companies, railway lines, loading lines, railway stations, sales regions, and the basic network information and operational capabilities of the power plant sector balance model.

[0073] Step 2: Consider the data mapping of the coal logistics network balance optimization model that coordinates the entire chain of production, transportation, sales, storage and use, including annual output of self-produced coal, annual total production cost of self-produced coal, annual procurement capacity of purchased coal, annual procurement cost of purchased coal, annual demand capacity of sales area, annual unit selling price of sales area, annual unit sales cost of sales area, railway line section length, annual unit freight rate of railway line, annual transportation cost of railway line, annual transportation capacity of railway line, coal transportation line, annual transportation capacity of large logistics, large logistics transportation line, annual transportation capacity of local coal, and local coal transportation line.

[0074] Step 3: Construct the decision variables and constraints for the optimization problem.

[0075] Decision variables include the annual output of each coal mine. Coal sales volume at coal mines The amount of coal transported from each coal mine to each loading station The amount of purchased coal transported by each trading company to each loading station The amount of self-produced coal transported from each loading station to each unloading station via each transportation route Purchased coal volume transported from each loading station to each unloading station via various transportation routes Coal volume transported from each railway unloading station to each sales region

[0076] The constraints include coal mine constraints, external procurement constraints, loading platform constraints, railway constraints, and sales area constraints. Railway constraints further include railway loading station constraints, railway line section constraints, and railway unloading station constraints.

[0077] 3.1 Coal Mine Constraints

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] In the formula: For coal mine output; n v This represents the total number of loading platforms; This refers to the amount of coal used by the coal mine itself. This refers to the amount of coal at the mine mouth. This refers to the volume of coal sold by the coal mine. Let m be the amount of coal at the kth loading station corresponding to the mth coal mine. Let be the association matrix of the coal mine and the loading station, which is a 0-1 variable, where 1 indicates that the m-th coal mine is connected to the k-th loading station, and 0 indicates that they are not connected. The reported coal production of the m-th coal mine; These represent the coal production relaxation amount, lower limit of relaxation amount, and upper limit of relaxation amount for the m-th coal mine, respectively. The approved coal production capacity for the m-th coal mine. This represents the amount of coal sold locally by the m-th coal mine. Let represent the relaxation amount, lower limit, and upper limit of the local coal sales of the m-th coal mine.

[0085] 3.2. External Purchase Restrictions

[0086]

[0087]

[0088]

[0089] In the formula: Let O be the amount of coal purchased by the ith company at the kth loading station. This represents the amount of purchased coal reported by the o-th company at the k-th loading station. Let $\frac{0}{k}$ represent the amount of coal that can be loosened from external sources at the $k$ loading station for the $o$-th company, including the lower limit and upper limit of the loosening amount.

[0090] 3.3 Loading platform constraints

[0091]

[0092] Where: n m n represents the total number of coal mines. p The total number of externally acquired companies; Let be the amount of coal at the k-th loading station; Let m be the amount of coal at the kth loading station corresponding to the mth coal mine. Let be the association matrix of the coal mine and the loading station, which is a 0-1 variable, where 1 indicates that the m-th coal mine is connected to the k-th loading station, and 0 indicates that they are not connected. Let K be the local coal quantity at the k-th loading station. The amount of coal purchased by the o-th company at the k-th loading station; The association matrix of the o-th company corresponding to the k-th loading station is a 0-1 variable, where 1 indicates that the o-th company purchases external coal at the k-th loading station, and 0 indicates that it does not purchase coal. Let be the loading capacity of the k-th loading platform.

[0093]

[0094]

[0095] Where: n rl This represents the total number of railway loading stations. Let be the association matrix between the k-th loading platform and the i-th railway loading station, which is a 0-1 variable, where 1 indicates that the k-th loading platform is connected to the i-th railway loading station, and 0 indicates that they are not connected; The amount of coal produced by the i-th railway loading station corresponds to the k-th loading platform; The amount of coal purchased by the kth loading station corresponds to the amount of coal transported to the i-th railway loading station. Equation (11) indicates that the amount of coal transported from the coal mine to the loading station is equal to the amount of coal transported from the loading station to the railway loading station; Equation (12) indicates that the amount of coal purchased by the loading station is equal to the amount of coal transported from the loading station to the railway loading station.

[0096] 3.4 Railway Constraints

[0097] 3.4.1) Railway loading station constraints

[0098]

[0099]

[0100]

[0101]

[0102] Where: n rr n represents the total number of railway transport routes. ru n represents the total number of railway unloading stations. v This represents the total number of loading platforms; The amount of self-produced coal transported from the i-th railway loading station to the j-th railway unloading station via the l-th transportation route; The amount of purchased coal transported from the i-th railway loading station to the j-th railway unloading station via the l-th transportation route; The total amount of coal transported from the i-th railway loading station to the j-th railway unloading station via the l-th transportation route; Let be the loading capacity of the i-th railway loading station. Equation (13) indicates that the amount of self-produced coal transported from the loading station to the railway loading station is equal to the amount of self-produced coal transported from the railway loading station to each railway unloading station via each transportation route; Equation (14) indicates that the amount of purchased coal transported from the loading station to the railway loading station is equal to the amount of purchased coal transported from the railway loading station to each railway unloading station via each transportation route.

[0103] 3.4.2) Railway line section constraints

[0104]

[0105]

[0106] In the formula: The amount of coal transported via the l-th railway route; Let be the association matrix of the railway line section a corresponding to the l-th transportation route. It is a 0-1 variable, where 1 indicates that the railway line section a belongs to the l-th transportation route, and 0 indicates that it does not belong to the l-th transportation route. This refers to the reported transport capacity of the railway line section a.

[0107] 3.4.3) Constraints on railway unloading sites

[0108]

[0109] In the formula: Let J be the amount of coal unloaded at the j-th railway unloading station. Let be the unloading capacity of the j-th railway unloading station.

[0110] 3.5 Sales Area (including power plants along the route and border exits) Constraints

[0111]

[0112]

[0113]

[0114]

[0115] Where: n s Total number of sales regions; Let J represent the amount of coal at the j-th railway unloading station corresponding to the s-th sales region. Let be the association matrix of the s-th sales region and the j-th railway unloading station, which is a 0-1 variable, where 1 indicates that the s-th sales region and the j-th railway unloading station are connected, and 0 indicates that they are not connected; Let be the amount of coal in the s-th sales region; The coal demand reported by the s-th sales region; The demand capacity of the s-th sales region; Let s be the customer demand slack, lower limit of slack, and upper limit of slack for the s-th sales region. Equation (20) indicates that the amount of coal transported from the railway loading station to each railway unloading station via each transportation route is equal to the amount of coal transported from the railway unloading station to each sales region.

[0116] Step 4: Construct the objective function for the optimization problem.

[0117] The constructed objective functions include the model for maximizing transportation volume, the model for minimizing cost, the model for maximizing profit, and the multi-objective optimal model.

[0118] 4.1) Largest transport volume

[0119]

[0120] 4.2) Minimum cost

[0121]

[0122] In the formula: Let m be the unit total production cost of the m-th coal mine; Let $ be the unit purchase cost of coal purchased by the $o$-th company at the $k$-th loading station. This represents the unit freight rate for the l-th transport route.

[0123] 4.3) Maximizing Profit

[0124]

[0125] In the formula: C is the average selling price per unit.

[0126] 4.4) Multi-objective optimal model

[0127] maxαf1+βf2+γf3

[0128] Where α, β, and γ are the weight values ​​given in the maximum transport volume model, the minimum cost model, and the maximum profit model, and α+β+γ=1.

[0129] Step 5: Solve the optimization problem. Based on the constructed decision variables and constraints, use the interior point method to calculate the optimal value of the problem under different individual objective functions.

[0130] Using all the decision variables and constraints constructed in step 3, and selecting one of the three objective functions constructed in step 4—maximizing transportation volume, minimizing cost, and maximizing profit—to solve for the minimum objective function, the optimization problem is determined to be a mixed-integer linear programming problem. An open-source solver can quickly obtain the optimal values ​​of each decision variable under the condition of minimizing the objective function, thus yielding the production, transportation, sales, storage, and utilization balance optimization strategy.

[0131] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for balancing and optimizing a logistics network that considers the coordination of the entire production, transportation, sales, storage, and consumption chain, characterized in that: Includes the following steps: Obtain basic information on the industrial models of each segment within the integrated scope of relevant enterprises; Obtain data mapping of a balanced optimization model for the entire coal logistics network, encompassing production, transportation, sales, storage, and consumption; Based on the aforementioned basic information and data mapping, the decision variables and constraints for the optimization problem are constructed. The constraints include coal mine constraints, external purchase constraints, loading station constraints, railway constraints, and sales area constraints. The conditions for constructing the coal mine constraints include: ； ； ； ； ； ； In the formula: For coal mine output; This represents the total number of loading platforms; This refers to the amount of coal used by the coal mine itself. This refers to the amount of coal at the mine mouth. This refers to the volume of coal sold by the coal mine. For the first The number of coal mines corresponds to the number 1 The amount of coal at each loading station; This is the correlation matrix of the coal mine and its corresponding loading station, with variables ranging from 0 to 1, where 1 represents the... The coal mine and the first 0 indicates that the loading platforms are connected; 0 indicates that they are not connected. For the first The coal production reported by each coal mine; , , The first The coal production relaxation amount, lower limit of relaxation amount, and upper limit of relaxation amount for each coal mine; For the first The approved coal production capacity of each coal mine; For the first The amount of coal sold locally by each coal mine; , , For the first The amount of coal loosening for local sales at each coal mine, the lower limit of loosening, and the upper limit of loosening; The conditions for constructing the external purchase constraint include: ； ； ； In the formula: For the first Company corresponding to the first The amount of coal purchased by each loading station; For the first Company corresponding to the first The amount of purchased coal reported by each loading station; , , For the first Company corresponding to the first The amount of loosened coal purchased for each loading station, including the lower limit and upper limit of the loosened amount; The constraints for constructing the loading platform include: ； ； ； In the formula: The total number of coal mines; The total number of externally acquired companies; For the first The amount of coal at each loading station; For the first The number of coal mines corresponds to the number 1 The amount of coal at each loading station; This is the correlation matrix of the coal mine and its corresponding loading station, with variables ranging from 0 to 1, where 1 represents the... The coal mine and the first 0 indicates that the loading platforms are connected; 0 indicates that they are not connected. For the first The amount of coal at each loading station platform; No. Company corresponding to the first The amount of coal purchased by each loading station; No. Company corresponding to the first The correlation matrix of the loading platform is a 0-1 variable, where 1 represents the first loading platform. The company in Each loading station purchases externally sourced coal; 0 indicates no purchases. For the first The loading capacity of each loading station platform; This represents the total number of railway loading stations. For the first The loading platform corresponds to the first The correlation matrix of the railway loading stations is a set of 0-1 variables, where 1 represents the th... The loading station and the first 0 indicates that the railway loading stations are connected; 0 indicates that they are not connected. No. The loading platform corresponds to the first The amount of coal produced by each railway loading station; No. The loading platform corresponds to the first The amount of coal purchased by each railway loading station; The railway constraints include railway loading station constraints, railway line section constraints, and railway unloading station constraints. The constraints for the railway loading station are as follows: ； ； ； ； In the formula: This represents the total number of railway transport routes. This represents the total number of railway unloading stations. This represents the total number of loading platforms; For the first The railway loading station passed the first The transport route is to the first The amount of coal produced by each railway unloading station; For the first The railway loading station passed the first The transport route is to the first The amount of coal purchased at each railway unloading station; For the first The railway loading station passed the first The transport route is to the first Total coal volume at each railway unloading station; For the first The loading capacity of each railway loading station; The constraint conditions for the railway line section are as follows: ； ； In the formula: For the first The amount of coal transported along each railway route; For the first Section of railway line corresponding to the first The correlation matrix of the transportation routes is a set of 0-1 variables, where 1 represents the first... The section of the railway line belongs to the first There are 10 transport routes, with 0 indicating that the route is not assigned to any of them. For the first Reported transport capacity for sections of railway lines; The constraints for the railway unloading station are as follows: ； In the formula: For the first Coal unloading volume at each railway unloading station; For the first The unloading capacity of each railway unloading station; The sales region constraint construction conditions include: ； ； ； ； In the formula: Total number of sales regions; For the first The sales region corresponds to the first The amount of coal at each railway unloading station; For the first The sales region corresponds to the first The correlation matrix of the railway unloading stations is a set of 0-1 variables, where 1 represents the... The sales region and the first 0 indicates that the railway unloading stations are connected; 0 indicates that they are not connected. For the first Coal volume in each sales region; For the first Coal demand reported by each sales region; For the first The demand capacity of each sales region; , , For the first Customer demand slack, lower limit of slack, and upper limit of slack for each sales region; Construct an objective function for the optimization problem, which includes a model for maximizing transportation volume, a model for minimizing cost, a model for maximizing profit, and a multi-objective optimal model. Based on the decision variables and constraints, the optimal values ​​of each objective function are solved separately.

2. The logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain as described in claim 1, is characterized in that... The basic information of the industrial models of each sector within the scope of the integrated enterprise mentioned above includes: coal subsidiaries and their third-level units, coal trading companies, railway lines, loading lines, railway stations, sales regions, and basic network information and operational capabilities of the power plant sector balance model.

3. The logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain as described in claim 1, is characterized in that... The data mapping for the coal logistics network balance optimization model that coordinates the entire production, transportation, sales, storage, and use chain includes: Annual output of self-produced coal, annual total production cost of self-produced coal, annual purchasing capacity of purchased coal, annual purchasing cost of purchased coal, annual demand capacity of sales area, annual unit selling price of sales area, annual unit sales cost of sales area, length of railway line section, annual unit freight rate of railway line, annual transportation cost of railway line, annual transportation capacity of railway line, coal transportation line, annual transportation capacity of large logistics, large logistics transportation line, annual transportation capacity of local coal, and local coal transportation line.

4. The logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain as described in claim 1, characterized in that, The decision variables include the annual output of each coal mine. Coal sales volume at coal mines The amount of coal transported from each coal mine to each loading station The amount of purchased coal transported by each trading company to each loading station The amount of self-produced coal transported from each loading station to each unloading station via each transportation route. The amount of purchased coal transported from each loading station to each unloading station via each transportation route. The amount of coal transported from each railway unloading station to each sales region .

5. The logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain as described in claim 1, characterized in that, The maximum expression for the transport volume is: 。 6. The logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain as described in claim 5, is characterized in that... The expression for minimizing cost is: ； In the formula: For the first The unit total production cost of a coal mine; For the first Company corresponding to the first The cost of purchasing coal for each loading station; For the first The unit freight rate for each transportation route.

7. The logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain as described in claim 6, is characterized in that... The expression for maximizing profit is: ； In the formula: Average selling price per unit.

8. The logistics network balancing optimization method considering the coordination of the entire production, transportation, sales, storage, and consumption chain as described in claim 7, is characterized in that... The multi-objective optimal model is: ； in , , The weight values ​​are given in the models of maximizing transport volume, minimizing cost, and maximizing profit. .

Images