Global optimization method and device for refinery enterprise supply chain

Abstract

The invention provides a global optimization method and device for a refinery enterprise supply chain, and the method comprises the steps: obtaining a supplier set, a sales company set, a refinery plant set, a transportation set, a product set and a spatial position set of a refinery enterprise, and constructing a supply chain model, represented by a network graph, of the refinery enterprise; simplifying the supply chain model represented by the network graph by using a graph sampling algorithm to obtain a sampled supply chain model, constructing an effective lower bound function of a supply chain optimization problem, and solving a first optimal solution according to the effective lower bound function and the sampled supply chain model; reducing the supply chain model represented by the network graph by using a graph reduction algorithm to obtain a reduced supply chain model, constructing an effective upper bound function of a supply chain optimization problem, and solving a second optimal solution according to the effective upper bound function and the reduced supply chain model; and generating a global optimal solution of the supply chain model according to the first optimal solution and the second optimal solution.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum refining, and in particular to a global optimization method and device for a supply chain of a refining enterprise. Background Art

[0002] Currently, large-scale oil refining and chemical companies are highly globalized, connecting a large number of upstream suppliers and downstream sales companies, and playing a crucial role in the petroleum and petrochemical industry supply chain. Refining and chemical companies cover a wide range of sectors, produce a wide variety of products, have lengthy production processes, and involve complex procurement and sales processes. These constraints make it difficult for these companies' supply chains to maintain efficient operation. Furthermore, given their immense demand for raw materials, optimizing the corresponding supply chain can significantly reduce procurement costs and significantly improve service quality at the sales end. Therefore, optimized supply chain management is crucial for oil and petrochemical companies.

[0003] As traditional petrochemical industries, most refining and chemical companies now have large material supply volumes, purchase a wide variety of materials, and have high requirements for quality, safety, and technology. How to provide qualified products to sales companies and improve transportation efficiency through supply chain management and optimization, minimize the cycle from procurement, production to sales, shorten the spatial distance from the supply end to the sales end, reduce transportation costs and other expenses in the overall logistics process, reduce product and raw material inventory, and at the same time consider the different production and operation conditions of each refinery to save and optimize various resources. All of the above problems need to be solved through the optimization of the enterprise supply chain.

[0004] Currently, most domestic refining and chemical companies rely on manual experience to make planning decisions for their supply chain management. However, for large and very large enterprises, with numerous suppliers and product specifications, business processes frequently change. The optimization of supply chain management plans requires coordination and resource adjustment across various departments and links. Manually preparing supply chain plans is labor-intensive, time-consuming, and complex, requiring inter-departmental communication and coordination. Consequently, it is difficult to quickly achieve optimized results, and the accuracy of the plans cannot be quantitatively analyzed and measured. Summary of the Invention

[0005] The present invention provides a global optimization method and device for the supply chain of a refinery enterprise, which is used to solve the defects of the prior art that manual supply chain management is time-consuming and labor-intensive and cannot guarantee accuracy.

[0006] The present invention provides a global optimization method for the supply chain of a refinery enterprise, comprising:

[0007] Obtaining a set of suppliers, sales companies, refineries, and transportation companies, as well as a set of products and spatial locations for a refinery, and constructing a supply chain model for the refinery represented by a network graph. The supply chain model includes an objective equation for total profit, and by maximizing total profit, the optimal solution for supplier supply, sales company demand, refinery processing capacity, and transportation capacity of transportation lines is found.

[0008] Using a graph sampling algorithm to simplify the supply chain model represented by the network graph to obtain a sampled supply chain model, constructing an effective lower bound function for the supply chain optimization problem, and obtaining a first optimal solution based on the effective lower bound function and the sampled supply chain model;

[0009] Using a graph reduction algorithm to reduce the supply chain model represented by the network graph to obtain a reduced supply chain model, constructing an effective upper bound function for the supply chain optimization problem, and obtaining a second optimal solution based on the effective upper bound function and the reduced supply chain model;

[0010] A global optimal solution of the supply chain model is generated according to the first optimal solution and the second optimal solution.

[0011] According to a global optimization method provided by the present invention, the supply chain model is expressed as follows:

[0012]

[0013]

[0014]

[0015] Each supplier of the refining enterprise is denoted as i, and each supplier i∈supplier set S. Each supplier i includes the following attributes: supply quantity s i , and the supply quantity s i ∈R + , supply goods p(i)∈P, maximum supply Spatial position n(i)∈N, supplier cost And supplier costs

[0016] Each sales company of the refining enterprise is denoted as j, and each sales company j∈sales company set C. Each sales company j includes the following attributes: demand c j , and the demand quantity c j ∈R + , demand product p(j)∈P, maximum demand Spatial position n(j)∈N, the cost of the sales company The cost of selling the company

[0017] Each refinery of the refining enterprise is denoted as t, and each refinery t∈refinery set T. Each refinery t includes the following attributes: product output coefficient γ t,P , and the product output coefficient γ t,p ∈R + , spatial position n(t)∈spatial position set N, crude oil purchased p(t)∈product set P, total crude oil purchased is ξ t , and the total amount of crude oil purchased t ∈R + , the maximum processing capacity of the refinery R + , operating costs And operating costs

[0018] Each transport line of the refinery is denoted as l, and each transport line l∈transport set L. Each transport line l includes the following attributes: supply f l , and the supply quantity f l ∈R + , transport goods p(l)∈ product set P, maximum transport volume Transport starting position n s (l)∈spatial location set N, transport destination location n r (l)∈ spatial location set N, transportation cost And transportation costs

[0019] The supply chain model is provided with data by four elements: suppliers, sales companies, refineries and transportation lines, including costs and upper limits. The costs include supplier costs α s , Sales company cost α c , operating costs α ξ and transportation cost α f , the upper limit includes the maximum supply Maximum demand Maximum processing capacity of the refinery and maximum transport volume

[0020] The supply chain model seeks the optimal solution of supply quantity s, demand quantity c, processing quantity ξ, and transportation quantity f by maximizing total profit.

[0021] According to a global optimization method provided by the present invention, the network graph includes a plurality of transportation lines;

[0022] The supply chain model represented by the network graph is simplified using a graph sampling algorithm to obtain a sampled supply chain model, and an effective lower bound function of the supply chain optimization problem is constructed. The first optimal solution is obtained based on the effective lower bound function and the sampled supply chain model, including:

[0023] A graph sampling algorithm is used to randomly sample a group of valid transportation lines from the network graph of the supply chain model, wherein the transportation volume of the valid transportation lines is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is equivalent to assuming that the transportation volume of these transportation lines is 0, so as to provide a constraint on the transportation volume of this sampling;

[0024] Determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after sampling and the supply chain model;

[0025] Continue to use the graph sampling algorithm to randomly sample and select the next set of valid transportation lines in the network graph of the supply chain model, determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after this sampling and the supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold, and the economic benefits corresponding to the supply chain model after this sampling are used as the first optimal solution.

[0026] According to a global optimization method provided by the present invention, the corresponding economic benefits are determined based on the effective lower bound function after the sampling and the supply chain model, which is achieved by the following formula:

[0027] The supply chain model includes:

[0028]

[0029]

[0030] The effective lower bound functions after this sampling include:

[0031]

[0032] Among them, c j is the demand of the sales company, is the maximum demand of the sales company, s i is the supplier’s supply quantity, is the supplier’s maximum supply, f l is the supply of the transport line, is the maximum supply of the transport line, ξ t is the processing volume of the refinery, is the maximum processing capacity of the refinery, Determine the corresponding economic benefits for the supply chain model, L a is the selected transport line, L r The transport lines that are not selected.

[0033] According to a global optimization method provided by the present invention, a network graph includes a plurality of nodes;

[0034] The supply chain model represented by the network graph is reduced by using a graph reduction algorithm to obtain a reduced supply chain model, and an effective upper bound function of the supply chain optimization problem is constructed. The second optimal solution is obtained based on the effective upper bound function and the reduced supply chain model, including:

[0035] A set of valid nodes is randomly sampled from the network graph of the supply chain model using a graph reduction algorithm. The edges connecting the valid nodes are used as selected transportation lines. The transportation volume of the selected transportation lines is less than or equal to the maximum transportation volume. The transportation volume of other unselected transportation lines is assumed to be 0, thereby providing a constraint on the transportation volume of the sampling.

[0036] Determining an effective upper bound function for the supply chain optimization problem based on the selected transportation lines, determining a reduced supply chain model based on the effective upper bound function after the sampling, and determining corresponding economic benefits based on the effective upper bound function after the sampling and the reduced supply chain model;

[0037] Continue to use the graph reduction algorithm to randomly sample a set of valid node sets in the network graph of the supply chain model, use the edges connecting the valid node sets as selected transportation lines, determine the effective upper bound function of the supply chain optimization problem based on the selected transportation lines, determine the reduced graph supply chain model based on the effective upper bound function after this sampling, and determine the corresponding economic benefits based on the effective upper bound function after this sampling and the reduced graph supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold value, and use the economic benefits corresponding to the supply chain model after this sampling as the second optimal solution.

[0038] According to a global optimization method provided by the present invention, a reduced supply chain model is determined based on the effective upper bound function after the sampling, and corresponding economic benefits are determined based on the effective upper bound function after the sampling and the reduced supply chain model, including:

[0039] The supply chain model includes:

[0040]

[0041]

[0042] The effective lower bound functions after this sampling include:

[0043]

[0044] Among them, c j is the demand of the sales company, is the maximum demand of the sales company, s i is the supplier’s supply quantity, is the supplier’s maximum supply quantity, is the supply of the transport line, is the maximum supply of the transport line, K represents the set of valid edges selected after the graph is reduced, k represents a set of valid edges selected, y t Indicates the number of units used in the refinery, represents the number of units available in the refinery, ξ t is the processing volume of the refinery, The maximum processing capacity of the refinery.

[0045] The present invention also provides a global optimization device for a supply chain of a refinery enterprise, comprising:

[0046] A model building module is used to obtain a set of suppliers, sales companies, refineries, and transportation companies of a refinery, a set of products, and a set of spatial locations, and to construct a supply chain model of the refinery represented by a network graph. The supply chain model includes an objective equation for total profit, and the optimal solution for supplier supply, sales company demand, refinery processing capacity, and transportation capacity of transportation lines is found by maximizing total profit.

[0047] a lower bound optimization module, configured to simplify the supply chain model represented by the network graph using a graph sampling algorithm to obtain a sampled supply chain model, construct an effective lower bound function for the supply chain optimization problem, and obtain a first optimal solution based on the effective lower bound function and the sampled supply chain model;

[0048] an upper bound optimization module, configured to reduce the supply chain model represented by the network graph using a graph reduction algorithm to obtain a reduced supply chain model, construct an effective upper bound function for the supply chain optimization problem, and obtain a second optimal solution based on the effective upper bound function and the reduced supply chain model;

[0049] An optimal solution determination module is used to generate a global optimal solution of the supply chain model based on the first optimal solution and the second optimal solution.

[0050] According to a global optimization device provided by the present invention, the network graph includes a plurality of transportation lines;

[0051] The lower bound optimization module is specifically used to:

[0052] A graph sampling algorithm is used to randomly sample a group of valid transportation lines from the network graph of the supply chain model, wherein the transportation volume of the valid transportation lines is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is equivalent to assuming that the transportation volume of these transportation lines is 0, so as to provide a constraint on the transportation volume of this sampling;

[0053] Determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after sampling and the supply chain model;

[0054] Continue to use the graph sampling algorithm to randomly sample and select the next set of valid transportation lines in the network graph of the supply chain model, determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after this sampling and the supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold, and the economic benefits corresponding to the supply chain model after this sampling are used as the first optimal solution.

[0055] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the global optimization method for the supply chain of a refining enterprise as described above are implemented.

[0056] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the global optimization method for the supply chain of a refining enterprise as described in any one of the above.

[0057] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for global optimization of the supply chain of a refinery.

[0058] The global optimization method and device for the supply chain of a refinery provided by the present invention, after constructing a supply chain model of the refinery represented by a network graph, respectively simplify the supply chain model using a graph sampling algorithm and construct an effective lower bound function of the optimization problem to obtain a first optimal solution, and simplify the supply chain model using a graph reduction algorithm and construct an effective upper bound function of the optimization problem to obtain a second optimal solution, thereby achieving the effective lower bound function and the effective upper bound function after narrowing the range by simplifying the supply chain model, and searching for the global optimal solution within the range, and finally obtaining the global optimal solution of the original supply chain model based on the first optimal solution and the second optimal solution, which can obtain a value close to the theoretical optimal value, save computing resources, and improve computing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 This is one of the flow diagrams of the global optimization method for the supply chain of a refinery enterprise provided by an embodiment of the present invention;

[0061] Figure 2 is a schematic diagram of a supply chain model provided by an embodiment of the present invention;

[0062] Figure 3 This is the second flow chart of the global optimization method for the supply chain of a refinery enterprise provided by an embodiment of the present invention;

[0063] Figure 4 is a schematic diagram of a single selection valid line set provided by an embodiment of the present invention;

[0064] Figure 5 This is a sampling effect diagram of the final network graph provided by an embodiment of the present invention;

[0065] Figure 6 This is the second flow chart of the global optimization method for the supply chain of a refinery enterprise provided by an embodiment of the present invention;

[0066] Figure 7 is a schematic diagram of a single selection of a valid node set provided by an embodiment of the present invention;

[0067] Figure 8 This is a sampling effect diagram of the final network graph provided by an embodiment of the present invention;

[0068] Figure 9 This is a schematic diagram of the structure of the global optimization device for the supply chain of a refinery enterprise provided by the present invention;

[0069] Figure 10 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0070] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0071] The following combination Figures 1-8 The present invention describes a global optimization method and apparatus for a supply chain of a refinery enterprise according to an embodiment of the present invention.

[0072] An embodiment of the present invention provides a global optimization method for the supply chain of a refinery. The global optimization algorithm for the supply chain of large and super-large refineries is based on a graph reduction algorithm and a graph sampling algorithm. By calculating the upper and lower boundaries of a large-scale supply chain model, the complexity of the problem can be reduced, the calculation time and memory usage can be significantly shortened, a large amount of computing power can be saved, and the optimization efficiency can be greatly improved. At the same time, the theoretically optimal result can be obtained, and a supply chain optimization plan for the refinery can be quickly provided.

[0073] Specifically, see Figure 1 , the method of this embodiment includes:

[0074] 101. Obtain a set of suppliers, a set of sales companies, a set of refineries, a set of transportation units, a set of products, and a set of spatial locations of a refining and chemical enterprise, and construct a supply chain model of the refining and chemical enterprise represented by a network graph; wherein the supply chain model includes an objective equation for total profit, and finds the optimal solution for the supplier's supply quantity, the sales company's demand quantity, the refinery's processing quantity, and the transportation quantity of the transportation line by maximizing the total profit.

[0075] See also Figure 2 , Figure 2 The supply chain model in this embodiment is shown. Figure 2 As can be seen, a supply chain model for a refinery typically includes the following elements: a spatial location set (point set) N, a supplier set S, a sales company set C, a product set P, a technology set (refinery processing) T, and a transportation set (line set) L. The spatial locations include the locations of suppliers, sales companies, refineries, and transported goods; the product set includes supplied goods, demanded goods, the refinery's purchased crude oil, and the transported goods along the transportation lines. The transportation lines in the transportation set connect the points to form a network diagram.

[0076] Specifically, the supply chain model is expressed as the following formula group (1):

[0077]

[0078]

[0079]

[0080] Each supplier of the refining enterprise is denoted as i, and each supplier i∈supplier set S. Each supplier i includes the following attributes: supply quantity s i , and the supply quantity s i∈R+, supply goods p(i)∈P, maximum supply Spatial position n(i)∈N, supplier cost And supplier costs

[0081] Each sales company of the refining enterprise is denoted as j, and each sales company j∈sales company set C. Each sales company j includes the following attributes: demand c j , and the demand quantity c j ∈R + , demand product p(j)∈P, maximum demand Spatial position n(j)∈N, the cost of the sales company The cost of selling the company

[0082] Each refinery of the refining enterprise is denoted as t, and each refinery t∈refinery set T. Each refinery t includes the following attributes: product output coefficient γ t,p , and the product output coefficient γ t,p ∈R + , spatial position n(t)∈spatial position set N, crude oil purchased p(t)∈product set P, total crude oil purchased is ξ t , and the total amount of crude oil purchased t ∈R + , the maximum processing capacity of the refinery Operating costs And operating costs

[0083] Each transport line of the refinery is denoted as l, and each transport line l∈transport set L. Each transport line l includes the following attributes: supply f l , and the supply quantity f l ∈R + , transport goods p(l)∈ product set P, maximum transport volume Transport starting position n s (l)∈spatial location set N, transport destination location n r (l)∈ spatial location set N, transportation cost And transportation costs

[0084] The supply chain model is provided with data by four elements: suppliers, sales companies, refineries and transportation lines, including costs and upper limits. The costs include supplier costs α s , Sales company cost α c , operating costs α ξ and transportation cost α f, the upper limit includes the maximum supply Maximum demand Maximum processing capacity of the refinery and maximum transport volume

[0085] The supply chain model seeks the optimal solution of supply quantity s, demand quantity c, processing quantity ξ, and transportation quantity f by maximizing total profit.

[0086] 102. Use a graph sampling algorithm to simplify the supply chain model represented by the network graph to obtain a sampled supply chain model, and construct an effective lower bound function for the supply chain optimization problem. According to the effective lower bound function and the sampled supply chain model, a first optimal solution is obtained.

[0087] In this step, the supply chain model is sampled and simplified by a graph sampling algorithm to obtain a simplified supply chain model represented by a network graph to reduce the amount of calculation.

[0088] The simplification in this embodiment can be performed multiple times, and each time the network graph corresponding to the supply chain model is sampled to obtain a set of valid transportation lines. In addition, an effective lower bound function is set for each sampling to implement the constraints on the transportation lines, thereby obtaining the first optimal solution for each sampling. The transportation volume of the valid transportation line is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is equivalent to assuming that the transportation volume of this transportation line is 0, so as to provide the constraints on the transportation volume of this sampling.

[0089] Because each sampling step selects a set of valid transport routes, after repeated sampling, the number of valid transport routes increases, ultimately resulting in an approximate supply chain model. Compared to the original supply chain model, the approximate supply chain model features a much simpler network graph and, consequently, a much smaller computational effort, thus conserving computing resources and improving efficiency.

[0090] 103. Use a graph reduction algorithm to reduce the supply chain model represented by the network graph to obtain a reduced supply chain model, and construct an effective upper bound function for the supply chain optimization problem. According to the effective upper bound function and the reduced supply chain model, a second optimal solution is obtained.

[0091] In this step, the supply chain model is sampled and simplified by a graph reduction algorithm to obtain a simplified supply chain model represented by a network graph to reduce the amount of calculation.

[0092] The simplification in this embodiment can be performed multiple times, each time sampling the network graph corresponding to the supply chain model to obtain a set of valid nodes. The edges connecting this set of valid nodes are used as selected transportation lines. The transportation volume of the selected transportation lines is less than or equal to the maximum transportation volume. The transportation volume of other unselected transportation lines is assumed to be zero, thereby providing a constraint on the transportation volume of the sampling. In addition, an effective upper bound function is set for each sampling to implement the constraint on the transportation lines, thereby obtaining the second optimal solution for each sampling.

[0093] Because each sampling operation selects a set of valid nodes, after repeated sampling, the number of valid transport routes increases, ultimately resulting in an approximate supply chain model. Compared to the original supply chain model, the approximate supply chain model has a much simpler network graph and, accordingly, a much smaller computational load, thus saving computing resources and improving efficiency.

[0094] 104. Generate a global optimal solution of the supply chain model based on the first optimal solution and the second optimal solution.

[0095] Specifically, the global optimal solution of the supply chain model can be obtained by pinching the first and second optimal solutions. Pinching refers to using a graph sampling method to continuously increase the number of selected points to construct a simplified supply chain model that is closer to the actual original problem. The effective upper bound of the first optimal solution constructed in this way decreases as the number of selected points increases. Using a graph reduction method to continuously increase the number of selected edges, the effective lower bound of the second optimal solution constructed in this way increases as the number of selected edges increases. Finally, when the first and second optimal solutions are equal and converge, the global optimal solution of the supply chain model is obtained.

[0096] The global optimization method for the supply chain of a refinery provided by the present invention, after constructing a supply chain model of the refinery represented by a network graph, uses a graph sampling algorithm to simplify the supply chain model and constructs an effective lower bound function of the optimization problem to obtain a first optimal solution, and uses a graph reduction algorithm to simplify the supply chain model and constructs an effective upper bound function of the optimization problem to obtain a second optimal solution, thereby achieving the effective lower bound function and the effective upper bound function after narrowing the range by simplifying the supply chain model, and searching for a global optimal solution within the range, and finally obtaining the global optimal solution of the original supply chain model based on the first optimal solution and the second optimal solution, which can obtain a value close to the theoretical optimal value, save computing resources, and improve computing efficiency.

[0097] Specifically, see Figure 3 , step 102 includes:

[0098] 301. A group of valid transportation lines are randomly sampled from the network graph of the supply chain model using a graph sampling algorithm, wherein the transportation volume of the valid transportation lines is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is equivalent to assuming that the transportation volume of this transportation line is 0, so as to provide a constraint on the transportation volume of this sampling.

[0099] See also Figure 4 , Figure 4 A schematic diagram showing a set of valid lines for a single selection is shown. Figure 4 Three transport lines are selected as the valid transport lines for the current sampling, and the transport volume is less than or equal to the maximum transport volume; if other transport lines are not selected in this sampling, the transport volume is 0.

[0100] The constraints of effective transport lines on transport volume include the following formula (2):

[0101]

[0102] Among them, l is each transportation line, L a is the selected transport line, L r is the transport line that is not selected; f l is the supply of each transport line, is the maximum supply of each transport line.

[0103] 302. Determine an effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after sampling and the supply chain model.

[0104] Specifically, the supply chain model includes the following formula (3):

[0105]

[0106]

[0107] The effective lower bound function after this sampling includes the following formula (4):

[0108]

[0109] Among them, c j is the demand of the sales company, is the maximum demand of the sales company, s i is the supplier’s supply quantity, is the supplier’s maximum supply, f l is the supply of the transport line, is the maximum supply of the transport line, ξ t is the processing volume of the refinery, is the maximum processing capacity of the refinery, Determine the corresponding economic benefits for the supply chain model, L a is the selected transport line, L r The transport lines that are not selected.

[0110] 303. Continue to use the graph sampling algorithm to randomly sample the next set of valid transportation lines in the network graph of the supply chain model, determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after sampling and the supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold, and use the economic benefits corresponding to the supply chain model after sampling as the first optimal solution.

[0111] In this step, if the variance is large, it means that the current sample size is small, so the sample network does not reflect the characteristics of the complete original graph (the graph topology has a large variability), so the sample size needs to be increased. Repeat the sampling process of steps 301 and 302 above to make the simplified supply chain model close to the original supply chain model. Figure 5 , Figure 5 The final network graph is shown in the following figure. Figure 5 It can be seen that as the number of sampling times increases, the formed network graph (the blue marked part) becomes closer and closer to the original network graph.

[0112] In addition, for upper bound optimization of supply chain, see Figure 6 , step 103 includes:

[0113] 601. A set of valid node sets are randomly sampled and selected from the network graph of the supply chain model using a graph reduction algorithm, and the edges connecting the valid node sets are used as selected transportation lines, wherein the transportation volume of the selected transportation lines is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is equivalent to assuming that the transportation volume of this transportation line is 0, so as to provide constraints on the transportation volume of this sampling.

[0114] See also Figure 7 , Figure 7 A schematic diagram showing a single selection of valid node sets. Figure 7 If two nodes are selected as valid nodes and the edge connecting the two valid nodes is selected as the transportation line, the transportation volume of the transportation line is less than or equal to the maximum transportation volume; if the transportation line composed of other nodes is not selected in this sampling, the transportation volume is 0.

[0115] After selecting valid nodes and grouping them, the edges grouped into the same point are recorded as internal edges L loc , and denote the edges connecting valid points as Therefore, the constraint on the transport volume is expressed as the following formula (5):

[0116]

[0117] Among them, l is each transportation line, f l is the supply of each transport line, is the maximum supply of each transportation line, k is the valid node, and K is the total number of nodes in the network graph.

[0118] 602. Determine the effective upper bound function of the supply chain optimization problem based on the selected transportation line, determine the reduced supply chain model based on the effective upper bound function after the sampling, and determine the corresponding economic benefits based on the effective upper bound function after the sampling and the reduced supply chain model.

[0119] Specifically, the supply chain model after sampling includes the following formula (6):

[0120]

[0121]

[0122] The effective lower bound function after this sampling includes the following formula (7):

[0123]

[0124] Among them, c j is the demand of the sales company, is the maximum demand of the sales company, s i is the supplier’s supply quantity, is the supplier’s maximum supply quantity, is the supply of the transport line, is the maximum supply of the transport line, K represents the set of valid edges selected after the graph is reduced, k represents a set of valid edges selected, y t Indicates the number of units used in the refinery, represents the number of units available in the refinery, ξ t is the processing volume of the refinery, The maximum processing capacity of the refinery.

[0125] 603. Continue to use the graph reduction algorithm to randomly sample a set of valid node sets in the network graph of the supply chain model, use the edges connecting the valid node sets as selected transportation lines, determine the effective upper bound function of the supply chain optimization problem based on the selected transportation lines, determine the reduced graph supply chain model based on the effective upper bound function after the sampling, and determine the corresponding economic benefits based on the effective upper bound function after the sampling and the reduced graph supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold value, and use the economic benefits corresponding to the supply chain model after the sampling as the second optimal solution.

[0126] In this step, if the variance is large, it indicates that the current sample size is small. Therefore, the sample network does not adequately reflect the characteristics of the complete original graph (the graph topology has a large variability), so the sample size needs to be increased. Repeat the sampling process in steps 601 and 602 above to make the simplified supply chain model close to the original supply chain model.

[0127] See also Figure 8 , Figure 8 The final network graph is shown in the following figure. Figure 8 It can be seen that as the number of sampling times increases, the number of valid nodes (colored nodes) also increases, and the formed network graph becomes closer and closer to the original network graph.

[0128] The advantages of the embodiments of the present invention are:

[0129] 1) The method of the embodiment of the present invention can realize global resource optimization of complex supply chain models. By simplifying the supply chain model, the effective upper bound and effective lower bound objective functions with a narrowed range are obtained, and the global optimal solution is searched within the range, which greatly saves computing resources, improves computing efficiency, and can obtain a value close to the theoretical optimal value.

[0130] 2) The method of the embodiment of the present invention can realize online real-time optimization of the complex supply chain network of refining enterprises, greatly saving labor costs, adapting to the current frequently changing business processes, improving the accuracy and computational economy of the model, getting closer to the actual situation of the enterprise, and improving the overall economic benefits.

[0131] The global optimization device for the supply chain of a refinery provided by an embodiment of the present invention is described below. The global optimization device for the supply chain of a refinery described below and the global optimization method for the supply chain of a refinery described above can refer to each other.

[0132] See also Figure 9 The embodiment of the present invention discloses a global optimization device for a supply chain of a refinery enterprise, comprising:

[0133] Model building module 901 is used to obtain a set of suppliers, sales companies, refineries, and transportation companies of a refinery, a set of products, and a set of spatial locations, and to construct a supply chain model of the refinery represented by a network graph. The supply chain model includes an objective equation for total profit, and the optimal solution for supplier supply, sales company demand, refinery processing capacity, and transportation capacity of transportation lines is sought by maximizing total profit.

[0134] a lower bound optimization module 902 for simplifying the supply chain model represented by the network graph using a graph sampling algorithm to obtain a sampled supply chain model, constructing an effective lower bound function for the supply chain optimization problem, and obtaining a first optimal solution based on the effective lower bound function and the sampled supply chain model;

[0135] An upper bound optimization module 903 is configured to reduce the supply chain model represented by the network graph using a graph reduction algorithm to obtain a reduced supply chain model, construct an effective upper bound function for the supply chain optimization problem, and obtain a second optimal solution based on the effective upper bound function and the reduced supply chain model.

[0136] The optimal solution determination module 904 is configured to generate a global optimal solution of the supply chain model based on the first optimal solution and the second optimal solution.

[0137] Specifically, the network graph includes multiple transportation lines; the lower bound optimization module 902 is specifically configured to: use a graph sampling algorithm to randomly sample a set of valid transportation lines from the network graph of the supply chain model, wherein the transportation volume of the valid transportation lines is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is assumed to be 0, thereby providing a constraint on the transportation volume of the sampling;

[0138] Determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after sampling and the supply chain model;

[0139] Continue to use the graph sampling algorithm to randomly sample and select the next set of valid transportation lines in the network graph of the supply chain model, determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after this sampling and the supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold, and the economic benefits corresponding to the supply chain model after this sampling are used as the first optimal solution.

[0140] Optionally, the network graph includes a plurality of nodes; the upper bound optimization module 903 is specifically configured to: use a graph reduction algorithm to reduce the supply chain model represented by the network graph to obtain a reduced supply chain model, construct an effective upper bound function for the supply chain optimization problem, and obtain a second optimal solution based on the effective upper bound function and the reduced supply chain model, including:

[0141] A set of valid nodes is randomly sampled from the network graph of the supply chain model using a graph reduction algorithm. The edges connecting the valid nodes are used as selected transportation lines. The transportation volume of the selected transportation lines is less than or equal to the maximum transportation volume. The transportation volume of other unselected transportation lines is assumed to be 0, thereby providing a constraint on the transportation volume of the sampling.

[0142] Determining an effective upper bound function for the supply chain optimization problem based on the selected transportation lines, determining a reduced supply chain model based on the effective upper bound function after the sampling, and determining corresponding economic benefits based on the effective upper bound function after the sampling and the reduced supply chain model;

[0143] Continue to use the graph reduction algorithm to randomly sample a set of valid node sets in the network graph of the supply chain model, use the edges connecting the valid node sets as selected transportation lines, determine the effective upper bound function of the supply chain optimization problem based on the selected transportation lines, determine the reduced graph supply chain model based on the effective upper bound function after this sampling, and determine the corresponding economic benefits based on the effective upper bound function after this sampling and the reduced graph supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold value, and use the economic benefits corresponding to the supply chain model after this sampling as the second optimal solution.

[0144] The global optimization device for the supply chain of a refinery provided by an embodiment of the present invention, after constructing a supply chain model of the refinery represented by a network graph, uses a graph sampling algorithm to simplify the supply chain model and constructs an effective lower bound function of the optimization problem to obtain a first optimal solution, and uses a graph reduction algorithm to simplify the supply chain model and constructs an effective upper bound function of the optimization problem to obtain a second optimal solution, thereby achieving the effective lower bound function and the effective upper bound function after narrowing the range by simplifying the supply chain model, and searching for the global optimal solution within the range, and finally obtaining the global optimal solution of the original supply chain model based on the first optimal solution and the second optimal solution, which can obtain a value close to the theoretical optimal value, save computing resources, and improve computing efficiency.

[0145] Figure 10 An example of a physical structure diagram of an electronic device is shown below. Figure 10As shown, the electronic device may include: a processor (processor) 1010, a communication interface (Communications Interface) 1020, a memory (memory) 1030 and a communication bus 1040, wherein the processor 1010, the communication interface 1020, and the memory 1030 communicate with each other through the communication bus 1040. The processor 1010 can call logic instructions in the memory 1030 to execute a global optimization method for the supply chain of a refinery, including: obtaining a supplier set, a sales company set, a refinery set, a transportation set, a product set, and a spatial location set of the refinery, and constructing a supply chain model of the refinery represented by a network graph; wherein the supply chain model includes an objective equation for total profit, and the optimal solution for the supplier's supply quantity, the sales company's demand quantity, the refinery's processing quantity, and the transportation quantity of the transportation line is found by maximizing the total profit; using a graph sampling algorithm to simplify the supply chain model represented by the network graph to obtain a sampled supply chain model, and constructing an effective lower bound function of the supply chain optimization problem, and obtaining a first optimal solution based on the effective lower bound function and the sampled supply chain model; using a graph reduction algorithm to reduce the supply chain model represented by the network graph to obtain the reduced supply chain model, and constructing an effective upper bound function of the supply chain optimization problem, and obtaining a second optimal solution based on the effective upper bound function and the reduced supply chain model; and generating a global optimal solution of the supply chain model based on the first optimal solution and the second optimal solution.

[0146] In addition, the logic instructions in the above-mentioned memory 1030 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0147] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the global optimization method of the supply chain of the refining enterprise provided by the above methods, including: obtaining the supplier set, sales company set, refinery set and transportation set, product set and spatial location set of the refining enterprise, and constructing a supply chain model of the refining enterprise represented by a network graph; wherein the supply chain model includes the objective equation of total profit, and finds the supply quantity of suppliers, the demand quantity of sales companies, The optimal solution for the processing volume of the refinery and the transportation volume of the transportation line; using a graph sampling algorithm to simplify the supply chain model represented by the network graph to obtain a sampled supply chain model, and constructing an effective lower bound function of the supply chain optimization problem, and obtaining a first optimal solution based on the effective lower bound function and the sampled supply chain model; using a graph reduction algorithm to reduce the supply chain model represented by the network graph to obtain a reduced supply chain model, and constructing an effective upper bound function of the supply chain optimization problem, and obtaining a second optimal solution based on the effective upper bound function and the reduced supply chain model; generating a global optimal solution of the supply chain model based on the first optimal solution and the second optimal solution.

[0148] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the global optimization method for the supply chain of a refinery provided by the above-mentioned methods, including: obtaining a supplier set, a sales company set, a refinery set, a transportation set, a product set, and a spatial location set of the refinery, and constructing a supply chain model of the refinery represented by a network graph; wherein the supply chain model includes an objective equation for total profit, and by maximizing the total profit, the supply quantity of the supplier, the demand quantity of the sales company, the processing quantity of the refinery, and the transportation quantity of the transportation line are found. The optimal solution of the supply chain model represented by the network graph is obtained; the supply chain model represented by the network graph is simplified by using a graph sampling algorithm to obtain a sampled supply chain model, and an effective lower bound function of the supply chain optimization problem is constructed, and a first optimal solution is obtained according to the effective lower bound function and the sampled supply chain model; the supply chain model represented by the network graph is reduced by using a graph reduction algorithm to obtain a reduced supply chain model, and an effective upper bound function of the supply chain optimization problem is constructed, and a second optimal solution is obtained according to the effective upper bound function and the reduced supply chain model; a global optimal solution of the supply chain model is generated according to the first optimal solution and the second optimal solution.

[0149] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0150] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A global optimization method for the supply chain of a refinery, characterized in that: include: Obtaining a set of suppliers, sales companies, refineries, and transportation companies, as well as a set of products and spatial locations for a refinery, and constructing a supply chain model for the refinery represented by a network graph. The supply chain model includes an objective equation for total profit, and by maximizing total profit, the optimal solution for supplier supply, sales company demand, refinery processing capacity, and transportation capacity of transportation lines is found. Using a graph sampling algorithm to simplify the supply chain model represented by the network graph to obtain a sampled supply chain model, constructing an effective lower bound function for the supply chain optimization problem, and obtaining a first optimal solution based on the effective lower bound function and the sampled supply chain model; Using a graph reduction algorithm to reduce the supply chain model represented by the network graph to obtain a reduced supply chain model, constructing an effective upper bound function for the supply chain optimization problem, and obtaining a second optimal solution based on the effective upper bound function and the reduced supply chain model; A global optimal solution of the supply chain model is generated according to the first optimal solution and the second optimal solution.

2. The global optimization method according to claim 1, characterized in that The supply chain model is represented as follows: Each supplier of the refining enterprise is denoted as i, and each supplier i∈supplier set S. Each supplier i includes the following attributes: supply quantity s i , and the supply quantity s i ∈R + , supply goods p(i)∈P, maximum supply Spatial position n(i)∈N, supplier cost And supplier costs Each sales company of the refining enterprise is denoted as j, and each sales company j∈sales company set C. Each sales company j includes the following attributes: demand c j , and the demand quantity c j ∈R + , demand product p(j)∈P, maximum demand Spatial position n(j)∈N, the cost of the sales company The cost of selling the company Each refinery of the refining enterprise is denoted as t, and each refinery t∈refinery set T. Each refinery t includes the following attributes: product output coefficient γ t,p , and the product output coefficient γ t,p ∈R + , spatial position n(t)∈spatial position set N, crude oil purchased p(t)∈product set P, total crude oil purchased is ξ t , and the total amount of crude oil purchased t ∈R + , the maximum processing capacity of the refinery Operating costs And operating costs Each transport line of the refinery is denoted as l, and each transport line l∈transport set L. Each transport line l includes the following attributes: supply f l , and the supply quantity f l ∈R + , transport goods p(l)∈ product set P, maximum transport volume Transport starting position n s (l)∈spatial location set N, transport destination location n r (l)∈ spatial location set N, transportation cost And transportation costs The supply chain model is provided with data by four elements: suppliers, sales companies, refineries and transportation lines, including costs and upper limits. The costs include supplier costs α S , Sales company cost α C , operating costs α ξ and transportation cost α f , the upper limit includes the maximum supply Maximum demand Maximum processing capacity of the refinery and maximum transport volume The supply chain model seeks the optimal solution of supply quantity s, demand quantity c, processing quantity ξ, and transportation quantity f by maximizing total profit.

3. The global optimization method according to claim 1, characterized in that The network graph includes a plurality of transport lines; The supply chain model represented by the network graph is simplified using a graph sampling algorithm to obtain a sampled supply chain model, and an effective lower bound function of the supply chain optimization problem is constructed. The first optimal solution is obtained based on the effective lower bound function and the sampled supply chain model, including: A graph sampling algorithm is used to randomly sample a group of valid transportation lines from the network graph of the supply chain model, wherein the transportation volume of the valid transportation lines is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is equivalent to assuming that the transportation volume of these transportation lines is 0, so as to provide a constraint on the transportation volume of this sampling; Determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after sampling and the supply chain model; Continue to use the graph sampling algorithm to randomly sample and select the next set of valid transportation lines in the network graph of the supply chain model, determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after this sampling and the supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold, and the economic benefits corresponding to the supply chain model after this sampling are used as the first optimal solution.

4. The global optimization method according to claim 3, characterized in that The corresponding economic benefits are determined based on the effective lower bound function after sampling and the supply chain model, which is achieved through the following formula: The supply chain model includes: The effective lower bound functions after this sampling include: Among them, c j is the demand of the sales company, is the maximum demand of the sales company, s i is the supplier’s supply quantity, is the supplier’s maximum supply, f l is the supply of the transport line, is the maximum supply of the transport line, ξ t is the processing volume of the refinery, is the maximum processing capacity of the refinery, Determine the corresponding economic benefits for the supply chain model, L a is the selected transport line, L r The transport lines that are not selected.

5. The global optimization method according to claim 1, characterized in that: The network graph includes a plurality of nodes; The supply chain model represented by the network graph is reduced by using a graph reduction algorithm to obtain a reduced supply chain model, and an effective upper bound function of the supply chain optimization problem is constructed. The second optimal solution is obtained based on the effective upper bound function and the reduced supply chain model, including: A set of valid nodes is randomly sampled from the network graph of the supply chain model using a graph reduction algorithm. The edges connecting the valid nodes are used as selected transportation lines. The transportation volume of the selected transportation lines is less than or equal to the maximum transportation volume. The transportation volume of other unselected transportation lines is assumed to be 0, thereby providing a constraint on the transportation volume of the sampling. Determining an effective upper bound function for the supply chain optimization problem based on the selected transportation lines, determining a reduced supply chain model based on the effective upper bound function after the sampling, and determining corresponding economic benefits based on the effective upper bound function after the sampling and the reduced supply chain model; Continue to use the graph reduction algorithm to randomly sample a set of valid node sets in the network graph of the supply chain model, use the edges connecting the valid node sets as selected transportation lines, determine the effective upper bound function of the supply chain optimization problem based on the selected transportation lines, determine the reduced graph supply chain model based on the effective upper bound function after this sampling, and determine the corresponding economic benefits based on the effective upper bound function after this sampling and the reduced graph supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold value, and use the economic benefits corresponding to the supply chain model after this sampling as the second optimal solution.

6. The global optimization method according to claim 5, characterized in that: Determining a reduced supply chain model based on the effective upper bound function after the sampling, and determining corresponding economic benefits based on the effective upper bound function after the sampling and the reduced supply chain model, including: The supply chain model includes: The effective lower bound functions after this sampling include: Among them, c j is the demand of the sales company, is the maximum demand of the sales company, s i is the supplier’s supply quantity, is the supplier’s maximum supply quantity, is the supply of the transport line, is the maximum supply of the transport line, K represents the set of valid edges selected after the graph is reduced, k represents a set of valid edges selected, y t Indicates the number of units used in the refinery, represents the number of units available in the refinery, ξ t is the processing volume of the refinery, The maximum processing capacity of the refinery.

7. A global optimization device for the supply chain of a refinery enterprise, characterized in that: include: A model building module is used to obtain a set of suppliers, sales companies, refineries, and transportation companies of a refinery, a set of products, and a set of spatial locations, and to construct a supply chain model of the refinery represented by a network graph. The supply chain model includes an objective equation for total profit, and the optimal solution for supplier supply, sales company demand, refinery processing capacity, and transportation capacity of transportation lines is found by maximizing total profit. a lower bound optimization module, configured to simplify the supply chain model represented by the network graph using a graph sampling algorithm to obtain a sampled supply chain model, construct an effective lower bound function for the supply chain optimization problem, and obtain a first optimal solution based on the effective lower bound function and the sampled supply chain model; an upper bound optimization module, configured to reduce the supply chain model represented by the network graph using a graph reduction algorithm to obtain a reduced supply chain model, construct an effective upper bound function for the supply chain optimization problem, and obtain a second optimal solution based on the effective upper bound function and the reduced supply chain model; An optimal solution determination module is used to generate a global optimal solution of the supply chain model based on the first optimal solution and the second optimal solution.

8. The global optimization device according to claim 7, characterized in that: The network graph includes a plurality of transport lines; The lower bound optimization module is specifically used to: A graph sampling algorithm is used to randomly sample a group of valid transportation lines from the network graph of the supply chain model, wherein the transportation volume of the valid transportation lines is less than or equal to the maximum transportation volume, and the transportation volume of other unselected transportation lines is equivalent to assuming that the transportation volume of these transportation lines is 0, so as to provide a constraint on the transportation volume of this sampling; Determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after sampling and the supply chain model; Continue to use the graph sampling algorithm to randomly sample and select the next set of valid transportation lines in the network graph of the supply chain model, determine the effective lower bound function of the supply chain optimization problem based on the selected effective transportation lines, and determine the corresponding economic benefits based on the effective lower bound function after this sampling and the supply chain model, until the variance between the economic benefits determined this time and the economic benefits corresponding to the supply chain model before simplification is less than a threshold, and the economic benefits corresponding to the supply chain model after this sampling are used as the first optimal solution.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the global optimization method for the supply chain of a refinery enterprise as described in any one of claims 1 to 6 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the global optimization method for the supply chain of a refinery enterprise as claimed in any one of claims 1 to 6 are implemented.

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