Joint optimization method for overseas base procurement planning and supplier selection of manufacturing enterprises

Abstract

The present invention provides a method, system, storage medium, and electronic device for jointly optimizing procurement plans and supplier selection for a manufacturing enterprise's overseas bases, relating to the technical field of material procurement. The method comprises obtaining materials and related variables required for the production of products to be procured during the operation of the overseas base; constructing a mixed integer programming model with the goal of minimizing comprehensive procurement costs based on the quantities of materials required for production; and solving the mixed integer programming model using a genetic algorithm with multiple adaptive mechanisms to obtain a procurement plan for the overseas base within a procurement cycle. The plan includes supplier selection and procurement quantities for each material, and the plan optimized by the mathematical model is more scientific and reasonable. Based on the optimization method provided by the present invention, the preferred supplier can be selected and procurement quantities determined for the overseas base in a relatively short period of time, effectively optimizing the overseas base's procurement plan.

Description

Technical Field

[0001] The present invention relates to the technical field of material procurement, and in particular to a method, system, storage medium and electronic equipment for jointly optimizing procurement planning and supplier selection for an overseas base of a manufacturing enterprise. Background Art

[0002] With the increasing internationalization of manufacturing companies and intensified industry competition, more and more manufacturers are establishing overseas production bases to provide better products and services to overseas customers. When operating an overseas base, the proper selection of suppliers and the determination of purchase quantities directly impact the company's operations and profitability. However, procurement plans for overseas bases are often influenced by a variety of factors. In addition to traditional procurement costs such as material and transportation costs, the impact of relevant policies in the country where the overseas base is located must also be considered. Furthermore, compliance with the supplier management strategy of the manufacturing company headquarters is crucial.

[0003] Currently, in the context of cross-regional sourcing, overseas base procurement is influenced by local government policies, particularly tax policies, which directly impact corporate procurement costs. Tax policies refer to preferential tax policies implemented by the host country for foreign companies conducting production and operations in the region. For example, the Brazilian government stipulates that foreign companies that purchase locally or import raw materials from abroad for local processing can receive partial tax exemptions. For overseas bases of local foreign manufacturers, adjusting the ratio of procurement in the host country to that in other countries to meet this policy can achieve tax exemptions and directly reduce the company's overall procurement costs. However, traditional overseas base procurement strategies, which rely on manual experience to determine procurement ratios, often result in ad hoc purchases to meet tax policies, which can lead to the generation of obsolete materials and increase overall procurement costs.

[0004] From the perspective of a company's long-term development strategy, using only conventional procurement costs as an evaluation function could lead to the concentration of material procurement in one or a few large suppliers, gradually weakening the overseas base's bargaining power in the procurement process. Long-term cooperation with the same large supplier creates a significant risk of market monopoly. From the perspective of long-term cost optimization, it is essential for manufacturing companies' overseas bases to develop procurement plans that aim to diversify their sourcing while ensuring material quality.

[0005] In summary, the optimization of procurement plans for overseas bases of manufacturing enterprises has become an important part of the decision-making process of overseas base operations. It is necessary to fully consider the short-term and long-term costs of the enterprise's operations on the basis of traditional procurement costs, formulate reasonable procurement plans for overseas bases of manufacturing enterprises, and select suppliers. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In response to the shortcomings of the existing technology, the present invention provides a method, system, storage medium and electronic equipment for jointly optimizing the procurement plan and supplier selection of a manufacturing enterprise's overseas base, which solves the technical problem that the procurement plan of a manufacturing enterprise's overseas base cannot take into account both short-term and long-term costs in the enterprise's operation process.

[0008] (2) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] A joint optimization method for overseas base procurement planning and supplier selection for a manufacturing enterprise includes:

[0011] S1. Obtain the materials and related variables required for product production that need to be purchased during the operation of overseas bases;

[0012] S2. Constructing a mixed integer programming model with the goal of minimizing the comprehensive procurement cost based on the quantity of materials required for the production of the product and their related variables;

[0013] S3. Use a genetic algorithm to solve the mixed integer programming model to obtain the supplier selection and purchase quantity of each material in a procurement cycle of the overseas base.

[0014] Preferably, the mixed integer programming model in S2 includes:

[0015] The objective function to minimize the comprehensive procurement cost is:

[0016] F=min(f1+f2+f3)

[0017] Where F is the comprehensive procurement cost, f1 is the sum of the regular costs required in the procurement process, f2 is the tax incentive cost, and f3 is the adjustment cost of the procurement cost affected by the supplier's order allocation;

[0018] f1=f 11 +f 12 +f 13 +f 14 +f 15

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] f1 includes the material purchase cost f 11 , Ocean freight cost 12 , local inventory cost f 13 , local processing cost f 14 and customs clearance costs 15 ;

[0025] f 11 It includes the procurement cost of parts and raw materials, where parts i∈{1,2,3,...,I}, raw materials m∈{1,2,3,...,M}, and suppliers j∈{1,2,3,...,J}, where j=0 represents the supplier in the country where the manufacturing company is headquartered. ij is the unit price of purchasing component i from supplier j, x ij is the quantity of parts i purchased from supplier j, q mj is the unit price of raw material m purchased from supplier j, y mj is the quantity of raw material i purchased from supplier j;

[0026] f 12 Including the shipping cost of bulk parts and raw materials, pt i is the unit price of transport of unit part i, x i0 The quantity i of parts purchased from suppliers in the country where the manufacturing company is headquartered is qt m is the transportation unit price of raw material m, y mj is the quantity of raw material m purchased from suppliers in the country where the manufacturing company is headquartered;

[0027] pw mt is the unit storage price of raw material m at time t, t is the average waiting time for raw materials to be processed; pc i is the processing cost of unit raw materials, z i is the number of parts i that need to be made from raw materials through secondary processing; r i 、r m are the insurance costs of unit parts and raw materials, MA im The quantity of raw material m required to produce component i, θ is the tariff rate imposed by the tax policy of the overseas base’s local country;

[0028]

[0029] Among them, Q i is the planned purchase quantity of component i, Per i is the minimum ratio that component i must meet under the tax preferential policy, and π is the tax preferential exemption ratio stipulated by the local policy of the overseas base;

[0030] f3=f 31 +f 32

[0031]

[0032]

[0033]

[0034]

[0035] f3 includes the parts penalty cost f 31 and raw material penalty cost f 32 ;

[0036] H i 、H m Both are Herfindahl indices, representing the market concentration of parts and raw materials, respectively. i d m are the penalty coefficients for parts and raw materials after the market is in a monopoly state, N p The monopoly threshold is used to determine whether the market is in a monopoly state.

[0037] Preferably, the mixed integer programming model in S2 further includes constraints:

[0038]

[0039]

[0040] x ij ≥0,y mj ≥0,x ij ,y mj ∈int constraint (3)

[0041] Constraint (1) indicates that the total quantity of purchased parts is equal to the planned purchase quantity; constraint (2) indicates that the quantity of purchased raw materials is equal to the quantity of raw materials required to produce parts; constraint (3) indicates that the purchase quantities of parts and raw materials are both non-negative integers.

[0042] Preferably, the S3 specifically includes:

[0043] S31. According to the mixed integer programming model, multiple feasible solutions are solved, and each solution is encoded into a chromosome and placed into the initial population, and an initial strategy is selected;

[0044] S32, calculating the fitness value of each chromosome of the current population according to the objective function;

[0045] S33. If the current population is premature, or the number of consecutive iterations of the current selection strategy is greater than the preset number of iterations, the next strategy is switched in sequence; otherwise, the current selection strategy is retained and the selection operation is performed;

[0046] S34, executing an adaptive crossover strategy, selecting a crossover method, and determining a crossover rate;

[0047] S35, executing the adaptive mutation strategy, selecting the mutation method, and determining the mutation rate;

[0048] S36. After performing the mutation operation, the fitness value of each chromosome in the current population is calculated. If the repetition of the population fitness value is greater than the preset repetition, the adaptive restart mechanism with elite retention is used to update the current population. Otherwise, the current population is retained.

[0049] S37. Determine whether the maximum number of iterations has been reached. If so, select the chromosome with the largest fitness value in the current population and decode it to obtain the global optimal solution; otherwise, go to S33.

[0050] Preferably, encoding each solution into a chromosome in S31 includes:

[0051] The chromosome structure is constructed using real number coding. The chromosome is represented by a two-dimensional matrix of (I+M)×J, and the individual fitness value of the chromosome is the value of the model objective function. The rows of the matrix represent different types of materials, the first i rows represent different types of parts, and the last m rows represent different types of raw materials. The columns of the matrix represent different suppliers.

[0052] Preferably, the S34 includes:

[0053] S341. Determine the average, maximum, and minimum values ​​of the population fitness based on the fitness value of each chromosome in the current population;

[0054] S342, according to the relationship between the individual fitness value and the average value of the population fitness, as well as the maximum and minimum values, determine the crossover rate P cross ;

[0055] S343, randomly generate a number r∈(0,1), if r≤P cross , perform crossover operation on the current chromosome, and the crossover method is linear recombination or two-point crossover selected according to the optimal probability.

[0056] Preferably, the S35 includes:

[0057] S351. After performing the crossover operation, determine the average, maximum, and minimum values ​​of the population fitness based on the fitness value of each chromosome in the current population;

[0058] S352, determine the mutation rate P based on the relationship between the individual fitness value and the average value of the population fitness, as well as the maximum and minimum values. mut ;

[0059] S353, randomly generate a number r′∈(0,1), if r≤P mut , continue to compare the individual fitness value f′ with the average value f avg The size of ′,

[0060] If f′ <f avg ′, then two columns of random mutation methods are selected to perform mutation operation;

[0061] If f′≥f avg ′, then choose the reverse mutation method to perform the mutation operation.

[0062] A joint optimization system for overseas base procurement planning and supplier selection for a manufacturing enterprise, comprising:

[0063] The acquisition module is used to obtain the materials and related variables required for product production that need to be purchased during the operation of overseas bases;

[0064] A construction module is used to construct a mixed integer programming model with the goal of minimizing the comprehensive procurement cost based on the quantity of materials required for the production of the product and related variables;

[0065] The solution module is used to solve the mixed integer programming model using a genetic algorithm to obtain the supplier selection and purchase quantity of each material of the overseas base in a procurement cycle.

[0066] A storage medium stores a computer program for joint optimization of procurement plans and supplier selection for overseas bases of a manufacturing enterprise, wherein the computer program enables a computer to execute the above-mentioned joint optimization method for procurement plans and supplier selection for overseas bases of a manufacturing enterprise.

[0067] An electronic device, comprising:

[0068] one or more processors;

[0069] Memory; and

[0070] One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs including a method for executing the joint optimization method of overseas base procurement planning and supplier selection of a manufacturing enterprise as described above.

[0071] (3) Beneficial effects

[0072] The present invention provides a method, system, storage medium, and electronic device for jointly optimizing procurement planning and supplier selection for a manufacturing enterprise's overseas bases. Compared with existing technologies, this method has the following advantages:

[0073] The present invention involves obtaining the materials and related variables required for production during the operation of an overseas base; constructing a mixed integer programming model based on the quantity of materials required for production, with the goal of minimizing the overall procurement cost; and using a genetic algorithm to solve the mixed integer programming model to determine the supplier selection and procurement quantity for each material within a procurement cycle. To optimize procurement plans for manufacturing companies' overseas production bases, the optimization model incorporates tax incentives and supplier equilibrium factors, proposes a comprehensive total procurement cost composed of multiple cost factors, establishes a mixed integer linear programming model, and designs a genetic algorithm to solve it, thereby optimizing the overseas base's procurement plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in 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 only 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.

[0075] Figure 1 A schematic diagram of purchasing order allocation for an overseas base of a manufacturing enterprise provided by an embodiment of the present invention;

[0076] Figure 2 A flowchart of a joint optimization method for overseas base procurement planning and supplier selection for a manufacturing enterprise provided by an embodiment of the present invention;

[0077] Figure 3 A schematic diagram of a MA-GA algorithm flow is provided for an embodiment of the present invention;

[0078] Figure 4 A schematic diagram of a MA-GA chromosome encoding method provided in an embodiment of the present invention;

[0079] Figure 5 A schematic diagram of a two-point crossover method provided by an embodiment of the present invention;

[0080] Figure 6 A schematic diagram of a two-column random mutation method and a reversal mutation method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0081] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0082] The embodiments of the present application solve the technical problem that the overseas base procurement plan of a manufacturing enterprise cannot take into account both the short-term and long-term costs in the enterprise's operation process by providing a joint optimization method, system, storage medium and electronic device for the overseas base procurement plan and supplier selection of a manufacturing enterprise.

[0083] The technical solution in the embodiments of the present application is to solve the above technical problems, and the overall idea is as follows:

[0084] like Figure 1 As shown in the figure, manufacturing companies establish production bases overseas as they expand into overseas markets. These bases must procure materials for production, including parts and raw materials. From a procurement perspective, these bases can source from either the country where the manufacturing company is headquartered or the country where the base is located. In this procurement context, the overall procurement cost of an overseas production base includes not only conventional procurement costs such as material costs (parts and raw materials), shipping costs, customs clearance costs, local inventory costs, and local processing costs, but also must consider cost fluctuations due to tax incentives and supplier balancing.

[0085] The tax policies of the overseas base's home country stipulate that if a base purchases raw materials for processing in the overseas base's home country or purchases materials directly in the overseas base's home country, the base will receive fixed tax incentives. However, if the base further increases this percentage, it will no longer receive additional tax incentives. To enjoy tax incentives and reduce procurement costs, the base can either purchase the required parts or raw materials in the base's home country or purchase the raw materials needed to manufacture the parts in the manufacturing company's headquarters country and process them overseas.

[0086] To avoid the risks posed by supplier monopolies for overseas bases, overseas bases must consider not only short-term procurement costs but also the long-term costs associated with supplier monopolies when selecting suppliers in the local country. In this context, the joint optimization problem of procurement planning and supplier selection for a manufacturing enterprise's overseas base can be described as: Given the diverse material needs of a manufacturing enterprise's overseas base, considering the supply capabilities of multiple suppliers, and considering the impact of tax incentives and supplier equilibrium on procurement costs, with the goal of minimizing overall procurement costs, the optimal solution is to determine the supplier and procurement quantity for each material within a single procurement cycle for the overseas base.

[0087] Based on this, the present application provides a joint optimization method for procurement planning and supplier selection of overseas bases of manufacturing enterprises, including: obtaining the materials and related variables required for the production of products that need to be purchased during the operation of the overseas base; constructing a mixed integer programming model with the goal of minimizing the comprehensive procurement cost based on the quantity of materials required for the production of the products; using a genetic algorithm to solve the mixed integer programming model to obtain the supplier selection and procurement quantity of each material of the overseas base within a procurement cycle.

[0088] Aiming at the problem of optimizing procurement plans for overseas production bases of manufacturing enterprises, tax preferential policies and supplier equilibrium factors are introduced into the optimization model. A comprehensive total procurement cost composed of multiple cost factors is proposed, a mixed integer linear programming model is established, and an improved genetic algorithm is designed to solve it, thereby optimizing the procurement plans of overseas bases.

[0089] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0090] Example:

[0091] like Figure 2 As shown, an embodiment of the present invention provides a method for jointly optimizing procurement planning and supplier selection for overseas bases of a manufacturing enterprise, including:

[0092] S1. Obtain the materials and related variables required for product production that need to be purchased during the operation of overseas bases;

[0093] S2. Constructing a mixed integer programming model with the goal of minimizing the comprehensive procurement cost based on the quantity of materials required for the production of the product and their related variables;

[0094] S3. Use a genetic algorithm to solve the mixed integer programming model to obtain the supplier selection and purchase quantity of each material in a procurement cycle of the overseas base.

[0095] The embodiment of the present invention aims at the problem of optimizing the procurement plan of overseas production bases of manufacturing enterprises. It introduces tax preferential policies and supplier equilibrium factors into the optimization model, proposes a comprehensive total procurement cost composed of multiple cost factors, establishes a mixed integer linear programming model, and designs a genetic algorithm for solving it, thereby optimizing the procurement plan of overseas bases.

[0096] The above technical solutions are described in detail below:

[0097] In step S1, the materials and related variables required for the production of products that need to be purchased during the operation of the overseas base are obtained.

[0098] As shown in Table 1, this step determines the parameter set corresponding to the materials and related variables required for the production of the following products that need to be purchased during the operation of the overseas base:

[0099] Table 1

[0100]

[0101]

[0102] In step S2, a mixed integer programming model is constructed based on the quantity of materials required for the production of the product and related variables thereof, with the goal of minimizing the comprehensive procurement cost.

[0103] The mixed integer programming model includes:

[0104] The objective function to minimize the comprehensive procurement cost is:

[0105] F=min(f1+f2+f3)

[0106] Where F is the comprehensive procurement cost, f1 is the sum of the regular costs required in the procurement process, f2 is the tax incentive cost, and f3 is the adjustment cost of the procurement cost affected by the supplier's order allocation;

[0107] (1) The sum of conventional costs f1

[0108] f1=f 11 +f 12 +f 13 +f 14 +f 15

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] f1 includes the material purchase cost f 11 , Ocean freight cost 12 , local inventory cost f 13 , local processing cost f 14 and customs clearance costs 15 .

[0115] f 11It includes the purchase cost of parts and raw materials, that is, the material purchase cost is composed of the unit price of parts and raw materials and their respective planned purchase quantities. 12 Including the shipping cost of bulk parts and raw materials. 13 Refers to the inventory cost incurred by the waiting time of raw materials before processing, which is calculated by the quantity of raw materials, their unit inventory cost and the average waiting time of raw materials for processing. 15 Refers to the fees paid by overseas bases for import customs clearance when purchasing materials abroad. The customs clearance price is calculated based on the CIF price, which is calculated based on the FOB price, material costs, and insurance.

[0116] (2) Regarding the tax incentive cost f2

[0117] Tax incentive costs (f2) refer to tariff reductions granted by the local government of an overseas base to foreign companies that meet certain tax policies. These reductions are composed of the cost of customs clearance and the percentage of tax reductions.

[0118]

[0119] (2) About the supplier's equilibrium cost f3

[0120] First, it should be noted that supplier equilibrium costs primarily refer to procurement penalty costs resulting from a monopolistic market competition caused by failure to meet the requirements of an ecological procurement chain. This article uses the Herfindahl Index (HHI) to represent the market concentration index. It refers to the sum of the squares of the percentage of total revenue or total assets held by each market competitor in an industry. It is used to measure changes in market share, that is, the dispersion of manufacturer size in the market. In this case, it refers to the market concentration level of a certain material. Its calculation formula is:

[0121]

[0122] Where H i represents the market concentration of material i, and When a supplier in the market is in an oligopoly state, H = 1; when the market is in a state of perfect competition, that is, when n suppliers have equal shares, And when n is larger, H approaches 0.

[0123] Specifically in the embodiment of the present invention, the supplier's equilibrium cost is composed of parts and raw materials:

[0124] f3=f 31 +f 32

[0125]

[0126]

[0127]

[0128]

[0129] H i 、H m Both are Herfindahl indices, representing the market concentration of parts and raw materials, respectively. i d m are the penalty coefficients for parts and raw materials after the market is in a monopoly state, N p is the penalty coefficient after the market is in a monopoly state.

[0130] And the constraints:

[0131]

[0132]

[0133] x ij ≥0,y mj ≥0,x ij ,y mj ∈int constraint (3)

[0134] Constraint (1) indicates that the total quantity of purchased parts is equal to the planned purchase quantity; constraint (2) indicates that the quantity of purchased raw materials is equal to the quantity of raw materials required to produce parts; constraint (3) indicates that the purchase quantities of parts and raw materials are both non-negative integers.

[0135] In step S3, a genetic algorithm is used to solve the mixed integer programming model to obtain the supplier selection and purchase quantity of each material in a procurement cycle of the overseas base.

[0136] For the combinatorial optimization problem with real number parameters and integer constraints, the embodiment of the present invention proposes a genetic algorithm (Multiple Adaptive Genetic Algorithm, MA-GA) based on a multiple adaptive mechanism. The algorithm flow is as follows: Figure 3 First, a selection strategy combining multiple selection methods is proposed. Second, an adaptive crossover and mutation strategy based on population fitness is designed to improve the algorithm's local search capability in the later stages. Finally, an adaptive restart mechanism based on elite retention is adopted to promptly escape from local optimality.

[0137] The S3 specifically includes:

[0138] S31. According to the mixed integer programming model, multiple feasible solutions are solved, and each solution is encoded into a chromosome and placed into the initial population, and an initial strategy is selected;

[0139] Each solution is encoded into a chromosome, including:

[0140] The chromosome structure is constructed using real number coding, such as Figure 4 As shown in Figure 1, the chromosome is represented by a two-dimensional matrix (I+M)×J, and the fitness value of the individual chromosome is the value of the model objective function. The rows of the matrix represent different types of materials, the first i rows represent different types of parts, and the last m rows represent different types of raw materials; the columns of the matrix represent different suppliers. The elements in the matrix represent the quantity of parts i and raw materials m purchased by the manufacturing company's overseas base from j suppliers. For example, x 11 Refers to the quantity of Part 1 planned to be purchased from Supplier 1.

[0141] S32. Calculate the fitness value of each chromosome of the current population according to the objective function.

[0142] S33. If the current population is premature, or the number of consecutive iterations of the current selection strategy is greater than the preset number of iterations, the next strategy is switched in sequence; otherwise, the current selection strategy is retained and the selection operation is performed;

[0143] The embodiment of the present invention adopts a combined selection strategy, which determines whether the current population is in a precocious state by calculating the fitness value of individual chromosomes, and then dynamically switches between different selection methods.

[0144] Corresponding to the above steps S32 and S33, the specific operation of the combined selection strategy is as follows: First, calculate the fitness value of the entire population, and judge whether the current population is in a premature state based on the gap between the fitness values ​​of the best individual and the second best individual (for example, first calculate the gap F between the fitness value of the best individual and the second best individual diff , determine the required gap F diff For the optimal individual fitness value F sub When the proportion is not less than a preset threshold ε max ,Right now Determine that the current population is in a precocious state).

[0145] At the same time, it is determined whether the number of repeated iterations using the same selection strategy reaches a threshold. If any of the above conditions is met, a new selection method is used.

[0146] The embodiments of the present invention provide three interchangeable selection methods: roulette wheel selection, elite selection, and stallion selection. Compared with the single selection method used in traditional genetic algorithms, the strategy of combining multiple selection methods can effectively prevent the algorithm from falling into a local optimum.

[0147] S34: Execute an adaptive crossover strategy, determine a crossover rate, and select a crossover method, including:

[0148] S341. Determine the average, maximum, and minimum values ​​of the population fitness based on the fitness value of each chromosome in the current population;

[0149] S342. Determine the crossover rate based on the relationship between the individual fitness value and the average value of the population fitness, as well as the maximum and minimum values;

[0150]

[0151] P cross is the crossover rate, f represents the fitness value of the current chromosome, f avg 、f max and f min They represent the average, maximum, and minimum values ​​of the population fitness before the crossover operation is performed, respectively. ω1, μ1, and μ2 are all constants ranging from (0, 1). In the embodiment of the present invention, μ1=0.65, μ2=0.9, and ω1=0.05 are exemplarily selected. The crossover rate of the current individual will be continuously adjusted as the above parameters change to ensure that the excellent chromosomes of the parent generation are better retained in the offspring.

[0152] S343, randomly generate a number r∈(0,1), if r≤P cross , perform crossover operation on the current chromosome, and the crossover method is linear recombination or two-point crossover selected according to the optimal probability;

[0153] (1) Linear recombination refers to calculating the individual variable values ​​of the offspring based on a certain proportion of the parent variable interval. The calculation method for generating offspring individuals is as follows:

[0154]

[0155] α is a randomly uniformly selected scaling factor. Previous studies have shown that when its value range is [-0.25, 1.25], it can statistically ensure that the range of the value of the offspring variable will not be reduced. In the embodiment of the present invention, α is selected as 0.25;

[0156] (2) Two-point crossover refers to setting two random crossover points in the chromosome and then exchanging the parts of the two chromosomes between the two crossover points. Since the model in this paper contains integer constraints, the sub-chromosomes after directly performing two-point crossover do not always meet the constraints. Therefore, the sub-chromosomes must be repaired after the two-point crossover. The specific operation is as follows: Figure 5 shown.

[0157] S35, executing the adaptive mutation strategy to determine the mutation rate and select the mutation method; including:

[0158] S351. After performing the crossover operation, the average, maximum, and minimum values ​​of the population fitness are determined based on the fitness value of each chromosome in the current population.

[0159] S352, determining the mutation rate based on the relationship between the individual fitness value and the average value of the population fitness, as well as the maximum and minimum values;

[0160]

[0161] P mut is the mutation rate, f′ represents the fitness value of the current chromosome, f avg ′、f max ′ and f min ′ represents the average, maximum, and minimum values ​​of the population fitness after the crossover operation, ω2, μ3, and μ4 are all constants in the range of (0, 1); in the embodiment of the present invention, μ3 = 0.0095, μ4 = 0.015, and ω2 = 0.0005 are selected as an example. Due to the existence of ω2, when f′ is equal to f max ′, p mut It is not zero to reduce the probability of generating pseudo-excellent individuals in the early stage of iteration and falling into the local optimal solution.

[0162] S353, randomly generate a number r′∈(0,1), if r≤P mut , continue to compare the individual fitness value f′ with the average value f avg The size of ′,

[0163] If f′ <f avg ′, then two columns of random mutation methods are selected to perform mutation operation;

[0164] like Figure 6 As shown, random mutation involves randomly selecting two gene segments on a chromosome and swapping them, resulting in minimal chromosomal variation. When the fitness value of an individual is less than the population mean, it indicates that the chromosome is relatively superior, thus reducing the current mutation rate. Therefore, two random mutations are selected to minimize variation.

[0165] If f′≥f avg ′, then choose the reverse mutation method to perform the mutation operation.

[0166] like Figure 6 As shown, reversal mutation refers to flipping the entire chromosome, resulting in a larger degree of chromosome change. When the individual's fitness value is not less than the population mean, it indicates that the individual's fitness value is relatively poor, thus increasing the current mutation rate. Therefore, reversal mutation is selected to increase the degree of chromosome change.

[0167] S36. After performing the mutation operation, the fitness value of each chromosome in the current population is calculated. If the repetition of the population fitness value is greater than the preset repetition, the adaptive restart mechanism with elite retention is used to update the current population. Otherwise, the current population is retained.

[0168] During population evolution, when population diversity drops to a certain level, the algorithm restarts. The specific process is as follows: assuming the current population consists of M individuals, the optimal individual is retained. Then, the population initialization operation is called to regenerate M-1 new individuals, and iteration continues with these M chromosomes as the current offspring. Furthermore, during the algorithm iteration, if the cumulative number of restarts exceeds a certain threshold, the current mutation rate is increased by 0.01 to further enhance population diversity during the restart process and avoid falling into a local optimum.

[0169] S37. Determine whether the maximum number of iterations has been reached. If so, select the chromosome with the largest fitness value in the current population and decode it to obtain the global optimal solution; otherwise, go to S33.

[0170] An embodiment of the present invention provides a system for joint optimization of procurement planning and supplier selection for overseas bases of a manufacturing enterprise, including:

[0171] The acquisition module is used to obtain the materials and related variables required for product production that need to be purchased during the operation of overseas bases;

[0172] A construction module is used to construct a mixed integer programming model with the goal of minimizing the comprehensive procurement cost based on the quantity of materials required for the production of the product and related variables;

[0173] The solution module is used to solve the mixed integer programming model using a genetic algorithm to obtain the supplier selection and purchase quantity of each material of the overseas base in a procurement cycle.

[0174] An embodiment of the present invention provides a storage medium storing a computer program for jointly optimizing a manufacturing enterprise's overseas base procurement plan and supplier selection, wherein the computer program enables a computer to execute the method for jointly optimizing a manufacturing enterprise's overseas base procurement plan and supplier selection as described above.

[0175] An embodiment of the present invention provides an electronic device, including:

[0176] one or more processors;

[0177] Memory; and

[0178] One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs including a method for executing the joint optimization method of overseas base procurement planning and supplier selection of a manufacturing enterprise as described above.

[0179] It can be understood that the manufacturing enterprise overseas base procurement plan and supplier selection joint optimization system, storage medium and electronic device provided in the embodiment of the present invention correspond to the manufacturing enterprise overseas base procurement plan and supplier selection joint optimization method provided in the embodiment of the present invention. The explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the manufacturing enterprise overseas base procurement plan and supplier selection joint optimization method, and will not be repeated here.

[0180] In summary, compared with the existing technology, the present invention has the following beneficial effects:

[0181] An embodiment of the present invention involves obtaining the materials and related variables required for production during the operation of an overseas base; constructing a mixed integer programming model based on the quantity of materials required for production, with the goal of minimizing the overall procurement cost; and solving the mixed integer programming model using a genetic algorithm to determine the supplier selection and procurement quantity for each material within a procurement cycle. To optimize procurement plans for manufacturing companies' overseas production bases, this approach incorporates tax incentives and supplier equilibrium factors into the optimization model, proposes a comprehensive total procurement cost composed of multiple cost factors, establishes a mixed integer linear programming model, and designs a genetic algorithm to solve it, thereby optimizing the overseas base's procurement plan.

[0182] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0183] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. 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 joint optimization method for overseas base procurement planning and supplier selection for a manufacturing enterprise, characterized by: include: S1. Obtain the materials and related variables required for product production that need to be purchased during the operation of overseas bases; S2. Constructing a mixed integer programming model with the goal of minimizing the comprehensive procurement cost based on the quantity of materials required for the production of the product and their related variables; S3. Using a genetic algorithm to solve the mixed integer programming model, obtain the supplier selection and procurement quantity of each material in a procurement cycle of the overseas base; the mixed integer programming model in S2 includes: The objective function to minimize the comprehensive procurement cost is: F=min(f1+f2+f3) Where F is the comprehensive procurement cost, f1 is the sum of the regular costs required in the procurement process, f2 is the tax preferential cost, and f3 is the adjustment cost of the procurement cost affected by the supplier order allocation; f1=f 11 +f 12 +f 13 +f 14 +f 15 f1 includes the material purchase cost f 11 , Ocean freight cost 12 , local inventory cost f 13 , local processing cost f 14 and customs clearance costs 15 ; f 11 It includes the procurement cost of parts and raw materials, where parts i∈{1, 2, 3, ..., I}, raw materials m∈{1, 2, 3, ..., M}, and suppliers j∈{1, 2, 3, ..., J}, where j=0 represents the supplier in the country where the manufacturing company is headquartered. ij is the unit price of purchasing component i from supplier j, x ij is the quantity of parts i purchased from supplier j, q mj is the unit price of raw material m purchased from supplier j, y mj is the quantity of raw material i purchased from supplier j; f 12 Including the shipping cost of bulk parts and raw materials, pt i is the unit price of transport of unit part i, x i0 The quantity i of parts purchased from suppliers in the country where the manufacturing company is headquartered is qt m is the transportation unit price of raw material m, y mj is the quantity of raw material m purchased from suppliers in the country where the manufacturing company is headquartered; pw mt is the unit storage price of raw material m at time t, t is the average waiting time for raw materials to be processed; pc i is the processing cost of unit raw materials, z i is the number of parts i that need to be made from raw materials through secondary processing; r i 、r m are the insurance costs of unit parts and raw materials, MA im The quantity of raw material m required to produce component i, θ is the tariff rate imposed by the tax policy of the overseas base’s local country; Among them, Q i is the planned purchase quantity of component i, Per i is the minimum ratio that component i must meet under the tax preferential policy, and π is the tax preferential exemption ratio stipulated by the local policy of the overseas base; <h2 style=";text-align:left;direction:ltr">f3=f<h2 style=";text-align:left;direction:ltr"> 31 <h2 style=";text-align:left;direction:ltr"> +f<h2 style=";text-align:left;direction:ltr"> 32 f3 includes the parts penalty cost f 31 and raw material penalty cost f 32 ; H i 、H m Both are Herfindahl indices, representing the market concentration of parts and raw materials, respectively. i d m are the penalty coefficients for parts and raw materials after the market is in a monopoly state, N p The monopoly threshold is used to determine whether the market is in a monopoly state.

2. The method for joint optimization of overseas base procurement planning and supplier selection for a manufacturing enterprise according to claim 1, characterized in that: The mixed integer programming model in S2 also includes constraints: Constraint (1) indicates that the total quantity of purchased parts is equal to the planned purchase quantity; constraint (2) indicates that the quantity of purchased raw materials is equal to the quantity of raw materials required to produce parts; constraint (3) indicates that the purchase quantities of parts and raw materials are both non-negative integers.

3. The method for joint optimization of overseas base procurement planning and supplier selection for a manufacturing enterprise according to claim 1 or 2, characterized in that: The S3 adopts a genetic algorithm with multiple adaptive mechanisms to solve the mixed integer programming model, specifically including: S31. According to the mixed integer programming model, multiple feasible solutions are solved, and each solution is encoded into a chromosome and placed into the initial population, and an initial strategy is selected; S32, calculating the fitness value of each chromosome of the current population according to the objective function; S33. If the current population is premature, or the number of consecutive iterations of the current selection strategy is greater than the preset number of iterations, the next strategy is switched in sequence; otherwise, the current selection strategy is retained and the selection operation is performed; S34, executing an adaptive crossover strategy, selecting a crossover method, and determining a crossover rate; S35, executing the adaptive mutation strategy, selecting the mutation method, and determining the mutation rate; S36. After performing the mutation operation, the fitness value of each chromosome in the current population is calculated. If the repetition of the population fitness value is greater than the preset repetition, the adaptive restart mechanism with elite retention is used to update the current population. Otherwise, the current population is retained. S37. Determine whether the maximum number of iterations has been reached. If so, select the chromosome with the smallest fitness value in the current population and decode it to obtain the global optimal solution; otherwise, go to S33.

4. The method for joint optimization of overseas base procurement planning and supplier selection for a manufacturing enterprise according to claim 3, characterized in that: In the step S31, each solution is encoded into a chromosome, including: The chromosome structure is constructed using real number coding. The chromosome is represented by a two-dimensional matrix of (I+M)×J, and the individual fitness value of the chromosome is the value of the model objective function. The rows of the matrix represent different types of materials, the first i rows represent different types of parts, and the last m rows represent different types of raw materials. The columns of the matrix represent different suppliers.

5. The method for joint optimization of overseas base procurement planning and supplier selection for a manufacturing enterprise according to claim 3, characterized in that: The S34 includes: S341. Determine the average, maximum, and minimum values ​​of the population fitness based on the fitness value of each chromosome in the current population; S342, according to the relationship between the individual fitness value and the average value of the population fitness, as well as the maximum and minimum values, determine the crossover rate P cross ; S343, randomly generate a number r∈(0,1), if r≤P cross , perform crossover operation on the current chromosome, and the crossover method is linear recombination or two-point crossover selected according to the optimal probability.

6. The method for joint optimization of overseas base procurement planning and supplier selection for a manufacturing enterprise according to claim 3, characterized in that: The S35 includes: S351. After performing the crossover operation, determine the average, maximum, and minimum values ​​of the population fitness based on the fitness value of each chromosome in the current population; S352, determine the mutation rate P based on the relationship between the individual fitness value and the average value of the population fitness, as well as the maximum and minimum values. mut ; S353, randomly generate a number r′∈(0,1), if r≤P mut , continue to compare the individual fitness value f′ with the average value f avg The size of ′, If f′<f avg ′, then two columns of random mutation methods are selected to perform mutation operation; If f′≥f avg ′, then choose the reverse mutation method to perform the mutation operation.

7. A joint optimization system for overseas base procurement planning and supplier selection for manufacturing enterprises, characterized by: The method for implementing the joint optimization method for overseas base procurement planning and supplier selection of a manufacturing enterprise as claimed in claim 1 comprises: The acquisition module is used to obtain the materials and related variables required for product production that need to be purchased during the operation of overseas bases; A construction module is used to construct a mixed integer programming model with the goal of minimizing the comprehensive procurement cost based on the quantity of materials required for the production of the product and related variables; The solution module is used to solve the mixed integer programming model using a genetic algorithm to obtain the supplier selection and purchase quantity of each material of the overseas base in a procurement cycle.

8. A storage medium, characterized in that: It stores a computer program for joint optimization of overseas base procurement planning and supplier selection for a manufacturing enterprise, wherein the computer program enables the computer to execute the joint optimization method of overseas base procurement planning and supplier selection for a manufacturing enterprise as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs including a method for executing the joint optimization method for overseas base procurement planning and supplier selection of a manufacturing enterprise as described in any one of claims 1 to 6.

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