Methods and apparatus for optimizing procurement schemes, and computer-readable storage media

Abstract

This invention discloses a method and apparatus for optimizing procurement schemes, as well as a computer-readable storage medium. The method includes: determining a procurement scheme for a target product; determining a first cost based on the procurement scheme, wherein the first cost includes: domestic transportation cost, international transportation cost, and transportation risk cost; and optimizing the first cost using a particle swarm optimization algorithm to obtain a target procurement scheme. This invention solves the technical problem in related technologies where the lack of cost analysis of domestic and international transportation schemes and risks leads to increased enterprise expenditures.

Description

Technical Field

[0001] This invention relates to the field of intelligent operations research, and more specifically, to a method and apparatus for optimizing procurement schemes, as well as a computer-readable storage medium. Background Technology

[0002] Investing in and establishing factories overseas has become a crucial way for manufacturing companies to go global. Companies that source or process materials locally overseas at a minimum rate, as per policy requirements, receive certain tax incentives. However, due to factors such as the geographical location and labor costs of overseas bases, whenever there is a need to procure materials overseas, the minimum policy requirement must be met first. The remaining materials are then procured or processed domestically before being transported to the overseas base. This necessitates considering transportation issues for domestically procured and processed materials. Therefore, companies need to calculate actual transportation costs and delay costs (early or late arrival) based on the time constraints of their domestic procurement plans, while also considering the risks involved in transportation, to devise an optimal solution for domestic procurement and transportation costs and risks.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method and apparatus for optimizing procurement schemes, as well as a computer-readable storage medium, to at least address the technical problem of increased enterprise expenditures due to the lack of cost analysis of domestic and international transportation schemes and risks in related technologies.

[0005] According to one aspect of the present invention, a method for optimizing a procurement scheme is provided, comprising: determining a procurement scheme for a target product; determining a first cost based on the procurement scheme, wherein the first cost includes: domestic transportation cost, foreign transportation cost, and transportation risk cost; and optimizing the first cost using a particle swarm optimization algorithm to obtain a target procurement scheme.

[0006] Optionally, determining the procurement plan for the target product includes: acquiring market-oriented data for the target product, wherein the market-oriented data includes data affecting the sales volume of the target product, and the market-oriented data includes: current sales time, current sales location, historical sales volume, historical selling price, sales strategy, current tax policy, current warehousing capacity information, and current logistics capacity information; and determining the procurement plan based on the market-oriented data, wherein the procurement plan includes: total procurement volume and procurement completion time.

[0007] Optionally, the domestic transportation cost includes at least one of the following: first warehousing cost, first inventory holding cost, first transportation cost, first logistics management cost, and distribution processing cost.

[0008] Optionally, the foreign transportation costs include at least one of the following: second warehousing costs, second inventory holding costs, second transportation costs, second logistics management costs, and delay time costs.

[0009] Optionally, the transportation risk cost is obtained through transportation risk analysis, wherein the transportation risk analysis is to calculate multiple results by multiplying the probability of multiple risk events occurring by the losses caused by the occurrence of the multiple risk events, and select the minimum result among the multiple results as the transportation risk cost. The risk events include at least one of the following: natural disasters, transportation route blockages, and accidents.

[0010] Optionally, determining the second transportation cost according to the procurement plan includes: determining the second transportation cost through a predetermined algorithm, wherein the predetermined algorithm is: T c p represents the second transportation cost. con This indicates the unit price of the containers required for transportation, w con This represents the unit carrying capacity of the container, y i This represents the quantity of raw material i purchased from abroad, where i represents the number of types of raw materials.

[0011] Optionally, optimizing the first cost using the particle swarm optimization algorithm to obtain the target procurement plan includes: inputting the first cost into a procurement cost constraint model to process the first cost using the procurement cost constraint model, obtaining the processing result of the first cost on the procurement cost constraint model, wherein the procurement cost constraint model is constructed based on the total procurement amount and the procurement completion time; iterating the processing result until convergence to obtain the target procurement plan.

[0012] According to another aspect of the present invention, a procurement scheme optimization apparatus is also provided, comprising: a first determining module for determining a procurement scheme for a target product; a second determining module for determining a first cost based on the procurement scheme, wherein the first cost includes: domestic transportation cost, foreign transportation cost, and transportation risk cost; and an optimization module for optimizing the first cost using a particle swarm optimization algorithm to obtain a target procurement scheme.

[0013] Optionally, the first determining module includes: an acquisition unit, configured to acquire market-oriented data of the target product, wherein the market-oriented data includes data affecting the sales volume of the target product, and the market-oriented data includes: current sales time, current sales location, historical sales volume, historical selling price, sales strategy, current tax policy, current warehousing capacity information, and current logistics capacity information; and a first determining unit, configured to determine the procurement plan based on the market-oriented data, wherein the procurement plan includes: total procurement volume and procurement completion time.

[0014] Optionally, the domestic transportation cost includes at least one of the following: first warehousing cost, first inventory holding cost, first transportation cost, first logistics management cost, and distribution processing cost.

[0015] Optionally, the foreign transportation costs include at least one of the following: second warehousing costs, second inventory holding costs, second transportation costs, second logistics management costs, and delay time costs.

[0016] Optionally, the transportation risk cost is obtained through transportation risk analysis, wherein the transportation risk analysis is to calculate multiple results by multiplying the probability of multiple risk events occurring by the losses caused by the occurrence of the multiple risk events, and select the minimum result among the multiple results as the transportation risk cost. The risk events include at least one of the following: natural disasters, transportation route blockages, and accidents.

[0017] Optionally, determining the second transportation cost according to the procurement plan includes: a second determining unit, configured to determine the second transportation cost using a predetermined algorithm, wherein the predetermined algorithm is: T c p represents the second transportation cost. con This indicates the unit price of the containers required for transportation, w con This represents the unit carrying capacity of the container, y i This represents the quantity of raw material i purchased from abroad, where i represents the number of types of raw materials.

[0018] Optionally, the optimization module includes: an input unit, used to input the first cost into the procurement cost constraint model, so as to process the first cost using the procurement cost constraint model to obtain the processing result of the first cost on the procurement cost constraint model, wherein the procurement cost constraint model is constructed based on the total procurement amount and the procurement completion time; and an iteration unit, used to iteratively process the processing result until convergence to obtain the target procurement plan.

[0019] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed by a processor, it controls the device where the computer-readable storage medium is located to perform an optimization method of the procurement scheme described in any one of the above-described embodiments.

[0020] According to another aspect of the present invention, a processor is also provided, the processor being configured to run a computer program, wherein the computer program, when running, executes the optimization method of the procurement scheme described in any one of the above embodiments.

[0021] In this embodiment of the invention, a procurement plan for the target product is determined; a first cost is determined based on the procurement plan, wherein the first cost includes: domestic transportation cost, international transportation cost, and transportation risk cost; the first cost is optimized using a particle swarm optimization algorithm to obtain the target procurement plan. The procurement plan optimization method provided by this embodiment of the invention achieves the goal of determining domestic and international transportation costs and transportation risk costs based on the procurement plan, then optimizing the above costs using a particle swarm optimization algorithm to obtain the final target procurement plan, thereby achieving the technical effect of saving costs for enterprises. This solves the technical problem of increased enterprise expenditures due to the lack of cost analysis under domestic and international transportation plans and risks in related technologies. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0023] Figure 1 This is a flowchart of a procurement scheme optimization method according to an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of a procurement plan optimization method considering transportation costs according to an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of a procurement scheme optimization device according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] According to an embodiment of the present invention, a method embodiment for optimizing a procurement scheme is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] Figure 1 This is a flowchart of a procurement scheme optimization method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0031] Step S102: Determine the procurement plan for the target product.

[0032] Optionally, in the above steps, the first step is to determine the procurement plan for the target product, which refers to choosing whether to procure through a domestic (i.e., my country's domestic market) procurement plan or an overseas (i.e., sales destination) procurement plan based on tax policies. In this embodiment of the invention, a detailed explanation of the cost optimization analysis for the domestic procurement plan is provided.

[0033] Step S104: Determine the first cost based on the procurement plan, wherein the first cost includes: domestic transportation cost, foreign transportation cost, and transportation risk cost.

[0034] Optionally, in the above steps, the initial pre-set purchase quantity and purchase completion time in the procurement plan are obtained, and then the domestic transportation cost, foreign transportation cost and transportation risk cost are analyzed based on the purchase quantity and purchase completion time.

[0035] Step S106: Optimize the first cost using the particle swarm optimization algorithm to obtain the target procurement plan.

[0036] As can be seen from the above, in this embodiment of the invention, the procurement plan for the target product can first be determined; then, a first cost can be determined based on the procurement plan, wherein the first cost includes: domestic transportation cost, international transportation cost, and transportation risk cost; finally, the first cost can be optimized using a particle swarm optimization algorithm to obtain the target procurement plan. The procurement plan optimization method provided by this embodiment of the invention achieves the goal of determining domestic and international transportation costs and transportation risk costs based on the procurement plan, then optimizing the above costs using a particle swarm optimization algorithm to obtain the final target procurement plan, thereby achieving the technical effect of saving costs for enterprises, and thus solving the technical problem of increased enterprise expenditures due to the lack of cost analysis under domestic and international transportation plans and risks in related technologies.

[0037] As an optional embodiment, determining the procurement plan for the target product includes: obtaining market-oriented data for the target product, wherein the market-oriented data includes data that affects the sales volume of the target product, and the market-oriented data includes: current sales time, current sales location, historical sales volume, historical selling price, sales strategy, current tax policy, current warehousing capacity information, and current logistics capacity information; and determining the procurement plan based on the market-oriented data, wherein the procurement plan includes: total procurement volume and procurement completion time.

[0038] In the above optional embodiments, market-oriented data mainly refers to data that affects the sales volume of the target product, such as: the sales season, the sales location, the sales volume of the product in the same period of previous years, the price of products with the same positioning in previous years, and the local sales policies for the target product.

[0039] Furthermore, in the above embodiments, all market-oriented data can be combined and assigned different weights, and a certain algorithm can be used to estimate the total planned procurement volume and procurement completion time for the year.

[0040] As an optional embodiment, domestic transportation costs include at least one of the following: first warehousing costs, first inventory holding costs, first transportation costs, first logistics management costs, and distribution processing costs.

[0041] As an optional embodiment, foreign transportation costs include at least one of the following: second warehousing costs, second inventory holding costs, second transportation costs, second logistics management costs, and delay time costs.

[0042] As an optional embodiment, the transportation risk cost is obtained through transportation risk analysis, which involves multiplying the probability of multiple risk events occurring by the losses caused by the occurrence of multiple risk events to calculate multiple results, and selecting the minimum result among the multiple results as the transportation risk cost. The risk events include at least one of the following: natural disasters, transportation route blockages, and accidents.

[0043] In the above optional embodiments, transportation risk analysis is used to query and analyze the maximum loss and historical accident probability of multiple risk events, such as natural disasters, transportation route blockages and accidents (transport vehicle grounding, port accidents, etc.). The maximum loss caused by the risk event is then multiplied by the corresponding historical accident probability to obtain multiple data. The smallest combination among them is taken as the transportation risk cost.

[0044] As an optional embodiment, determining the second transportation cost according to the procurement plan includes: determining the second transportation cost through a predetermined algorithm, wherein the predetermined algorithm is: T c p represents the second transportation cost. con This indicates the unit price of the containers required for transportation, w con y represents the unit load capacity of a container. i This represents the quantity of raw material i purchased from abroad, where i represents the number of types of raw materials.

[0045] It should be noted that in this embodiment, the sea freight cost from the inland port to the overseas production base port needs to be considered. The specific cost will be determined based on the actual situation. Cross-border sea transport is generally billed in the form of container leasing, so the transportation cost is measured by the number of containers required. The calculation process has been simplified according to the actual situation. The final cost (i.e., the second transportation cost) is obtained by calculating the number of containers required for raw materials (by dividing the purchase quantity of raw materials at the second location by the unit carrying capacity of the container and then rounding up) and multiplying it by the unit price of container leasing (i.e., the unit price of the container required for transportation).

[0046] As an optional embodiment, the first cost is optimized using a particle swarm optimization algorithm to obtain a target procurement plan. This includes: inputting the first cost into a procurement cost constraint model to process the first cost using the procurement cost constraint model, thereby obtaining the processing result of the first cost on the procurement cost constraint model, wherein the procurement cost constraint model is constructed based on the total procurement amount and the procurement completion time; and iteratively processing the processing result until convergence to obtain the target procurement plan.

[0047] In the above optional embodiments, the basic idea of ​​the particle swarm optimization algorithm is to find the optimal solution through cooperation and information sharing among individuals in the swarm. In the embodiments of the present invention, it is used to find the optimal transportation scheme.

[0048] Figure 2 This is a flowchart of a procurement plan optimization method considering transportation costs according to an embodiment of the present invention, such as... Figure 2 As shown below, the procurement optimization plan for transportation costs will be explained in detail.

[0049] Step 1: First, based on tax policies, warehousing capacity, logistics capabilities, and cost considerations, determine that some materials in the procurement plan will be sourced domestically, with transportation costs being the only consideration for domestic procurement.

[0050] Step 2: Next, based on tax policies, warehousing capacity, logistics capabilities, and cost considerations, the procurement plan will specify overseas procurement for some materials, as there are no transportation costs for overseas local procurement.

[0051] Step 3: Based on the determined domestic procurement plan, calculate the procurement quantity and determine the procurement completion time.

[0052] Step 4: Continue to determine the transportation route, which can be roughly divided into domestic transportation and international transportation.

[0053] Domestic transportation: The materials to be purchased (processed and unprocessed) are transported to the international port, and then shipped overseas together. There is a time limit: all materials must arrive at the international port within the scheduled time.

[0054] International Transportation: Transporting all materials to their destination ports overseas.

[0055] First, the arrival time of transportation needs to take into account the time window constraint. Here, the time window mainly refers to the soft time window, that is, the raw materials are delivered within the specified time.

[0056] Step 5: Domestic transportation costs are roughly divided into five types:

[0057] Storage costs: When materials are not being processed or transported, they need to be stored. This creates storage costs, which can be roughly summarized as all expenses incurred in storing materials.

[0058] Inventory holding cost: Here we only calculate its capital cost, also known as interest cost, which refers to the profit that can be generated by comparing the value of this batch of goods with other financial means of production, which is approximately 20% of the value of the materials.

[0059] Distribution processing costs: Costs incurred during processing, including transportation, storage, handling, packaging, etc.

[0060] Transportation costs: Expenses incurred during transportation. Domestic transportation costs mainly consist of the transportation costs incurred in transporting purchased goods to inland international ports. These costs are calculated based on the quantity purchased from each supplier and the distance to the port.

[0061] Logistics management costs: the human, material, and financial resources consumed in logistics management.

[0062] Step 6: The costs of international shipping can be broadly categorized into five types:

[0063] Delay time costs can be broadly categorized into two types: delayed warehousing costs and time costs. Delayed warehousing costs mainly consist of warehousing fees incurred from the completion of procurement to transportation, as well as warehousing fees incurred due to early delivery. Time costs mainly consist of various expenses incurred when purchased goods are not delivered before the stipulated latest time, which can be understood as penalties for not delivering goods on time.

[0064] Step 7: Transportation Risk Analysis: The main risks during cargo transportation are the risk of cargo damage and the risk of delayed arrival. The main factors causing these risks are natural disasters, transportation route blockages, accidents en route, etc. At this point, we need to analyze all transportation routes. We can generate corresponding transportation risk costs (risk data digitization) by conducting risk assessments on the transportation routes. The calculation is as follows: Risk cost equals the probability of an event occurring multiplied by the loss caused after the event occurs.

[0065] Step 8: Iterate the scheme using the particle swarm optimization algorithm to generate the optimal solution.

[0066] The Particle Swarm Optimization (PSO) algorithm initializes with a swarm of random particles (random solutions) and then iteratively finds the optimal solution. In each iteration, a particle updates itself by tracking two "extremes." The first is the optimal solution found by the particle itself, called the individual extreme value (pBest). The other extreme value is the optimal solution found by the entire swarm, called the global extreme value (gBest). Alternatively, instead of the entire swarm, only a subset of the best particles' neighbors can be used; in this case, the extreme value among all neighbors is the local extreme value. Time is then used as a constraint, specifically the time from domestic shipments to the international port and the time from international shipments to the port. All transportation paths are treated as a particle swarm, with each path segment representing a particle. Each particle represents a possible solution to a problem, and each particle may contain intermediate transitional elements (representing processing or transit points during transportation). Through iterative optimization of each particle (transportation route and means), all particles obtain their own optimal solutions, thus achieving the globally optimal state of the particle swarm (lowest procurement and transportation costs).

[0067] Step 9: Based on the domestic and overseas procurement plans, optimize the existing procurement plan.

[0068] As can be seen from the above, in this embodiment of the invention, a procurement plan can be generated based on the needs of overseas bases and in conjunction with tax policies. The plan is divided into overseas local procurement and domestic procurement. For domestic procurement, the optimal transportation scheme is analyzed based on time window constraints to achieve an optimal balance between transportation costs and transportation risks incurred during transportation. The optimal domestic procurement scheme is then formulated. Finally, based on the already determined procurement plan, the existing procurement plan is optimized to obtain the procurement scheme with the lowest procurement cost. Different transportation plans can be adopted by combining different time window constraints and the number of purchase orders to achieve the lowest domestic procurement cost and further optimize the overall procurement plan.

[0069] Example 2

[0070] According to another aspect of the present invention, an apparatus for optimizing a procurement scheme is also provided. Figure 3 This is a schematic diagram of a procurement scheme optimization device according to an embodiment of the present invention, such as... Figure 3 As shown, it includes: a first determining module 31, a second determining module 33, and an optimization module 35. The optimization device for this procurement plan will be described below.

[0071] The first determining module 31 is used to determine the procurement plan for the target product.

[0072] The second determining module 33 is used to determine the first cost based on the procurement plan, wherein the first cost includes: domestic transportation cost, foreign transportation cost and transportation risk cost.

[0073] Optimization module 35 is used to optimize the first cost using particle swarm optimization to obtain the target procurement plan.

[0074] It should be noted that the first determining module 31, the second determining module 33, and the optimization module 35 mentioned above correspond to steps S102 to S106 in Embodiment 1. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules, as part of the device, can be executed in a computer system such as a set of computer-executable instructions.

[0075] As can be seen from the above, in this embodiment of the invention, the first determining module 31 can first determine the procurement plan for the target product; then, the second determining module 33 can determine the first cost based on the procurement plan, wherein the first cost includes: domestic transportation cost, international transportation cost, and transportation risk cost; finally, the optimization module 35 can optimize the first cost using a particle swarm optimization algorithm to obtain the target procurement plan. Through the procurement plan optimization device provided in this embodiment of the invention, the purpose of determining domestic and international transportation costs and transportation risk costs based on the procurement plan, and then optimizing the above costs using a particle swarm optimization algorithm to obtain the final target procurement plan is achieved. This realizes the technical effect of saving costs for enterprises, and solves the technical problem of increased enterprise expenditures due to the lack of cost analysis under domestic and international transportation plans and risks in related technologies.

[0076] Optionally, the first determining module includes: an acquisition unit for acquiring market-oriented data of the target product, wherein the market-oriented data includes data that affects the sales volume of the target product, and the market-oriented data includes: current sales time, current sales location, historical sales volume, historical selling price, sales strategy, current tax policy, current warehousing capacity information, and current logistics capacity information; and a first determining unit for determining a procurement plan based on the market-oriented data, wherein the procurement plan includes: total procurement volume and procurement completion time.

[0077] Optionally, domestic transportation costs include at least one of the following: first warehousing costs, first inventory holding costs, first transportation costs, first logistics management costs, and distribution processing costs.

[0078] Optionally, foreign transportation costs may include at least one of the following: secondary warehousing costs, secondary inventory holding costs, secondary transportation costs, secondary logistics management costs, and delay time costs.

[0079] Optionally, the transportation risk cost is obtained through transportation risk analysis, which involves multiplying the probability of multiple risk events occurring by the losses caused by the occurrence of multiple risk events to calculate multiple results, and selecting the minimum result among the multiple results as the transportation risk cost. The risk events include at least one of the following: natural disasters, transportation route blockages, and accidents.

[0080] Optionally, determining the second transportation cost according to the procurement plan includes: a second determining unit, configured to determine the second transportation cost using a predetermined algorithm, wherein the predetermined algorithm is: T c p represents the second transportation cost. con This indicates the unit price of the containers required for transportation, w con y represents the unit load capacity of a container. iThis represents the quantity of raw material i purchased from abroad, where i represents the number of types of raw materials.

[0081] Optionally, the optimization module includes: an input unit for inputting the first cost into the procurement cost constraint model, so as to process the first cost using the procurement cost constraint model to obtain the processing result of the first cost on the procurement cost constraint model, wherein the procurement cost constraint model is constructed based on the total procurement amount and the procurement completion time; and an iteration unit for iteratively processing the processing result until convergence to obtain the target procurement plan.

[0082] Example 3

[0083] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is run by a processor, the device where the computer-readable storage medium is located executes an optimization method for any of the above-described procurement schemes.

[0084] Example 4

[0085] According to another aspect of the present invention, a processor is also provided, which is used to run a computer program, wherein the computer program executes the optimization method of any of the above-described procurement schemes during runtime.

[0086] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0087] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0091] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0092] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing a procurement scheme, characterized in that, include: Determine the procurement plan for the target products; The first cost is determined according to the procurement plan, wherein the first cost includes: domestic transportation cost, foreign transportation cost and transportation risk cost; The first cost is optimized using a particle swarm optimization algorithm to obtain a target procurement plan; Determining the first cost according to the procurement plan includes: obtaining the procurement quantity and procurement completion time in the procurement plan; analyzing the domestic transportation cost, the foreign transportation cost, and the transportation risk cost based on the procurement quantity and the procurement completion time to determine the first cost. The domestic transportation cost includes at least one of the following: first warehousing cost, first inventory holding cost, first transportation cost, first logistics management cost, and distribution processing cost. The foreign transportation cost includes at least one of the following: second warehousing cost, second inventory holding cost, second transportation cost, second logistics management cost, and delay time cost. The transportation risk cost is obtained through transportation risk analysis, wherein the transportation risk analysis is calculated by multiplying the probability of multiple risk events occurring by the losses caused by the occurrence of the multiple risk events, and selecting the smallest result among the multiple results as the transportation risk cost. The risk events include at least one of the following: natural disasters, transportation route congestion, and accidents. The step of determining the procurement plan for the target product includes: acquiring market-oriented data for the target product, wherein the market-oriented data includes data affecting the sales volume of the target product, and the market-oriented data includes: current sales time, current sales location, historical sales volume, historical selling price, sales strategy, current tax policy, current warehousing capacity information, and current logistics capacity information; and determining the procurement plan based on the market-oriented data, wherein the procurement plan includes: total procurement quantity and procurement completion time. Optimizing the first cost using a particle swarm optimization algorithm to obtain a target procurement plan includes: inputting the first cost into a procurement cost constraint model to process the first cost using the procurement cost constraint model, obtaining the processing result of the first cost on the procurement cost constraint model, wherein the procurement cost constraint model is constructed based on the total procurement amount and the procurement completion time; iterating the processing result until convergence to obtain the target procurement plan.

2. The method according to claim 1, characterized in that, The second transportation cost is determined according to the aforementioned procurement plan, including: The second transportation cost is determined by a predetermined algorithm, wherein the predetermined algorithm is: , The second transportation cost is shown. This indicates the unit price of the containers required for transportation. This indicates the unit carrying capacity of the container. This represents the quantity of raw material i purchased from abroad, where i represents the number of types of raw materials.

3. An optimization device for a procurement scheme, characterized in that, include: The first determination module is used to determine the procurement plan for the target product; The second determining module is used to determine a first cost based on the procurement plan, wherein the first cost includes: domestic transportation cost, foreign transportation cost, and transportation risk cost, including obtaining the procurement quantity and procurement completion time in the procurement plan; analyzing the domestic transportation cost, the foreign transportation cost, and the transportation risk cost based on the procurement quantity and the procurement completion time to determine the first cost, wherein the domestic transportation cost includes at least one of the following: first warehousing cost, first inventory holding cost, first transportation cost, first logistics management cost, and distribution processing cost, and the foreign transportation cost includes at least one of the following: second warehousing cost, second inventory holding cost, second transportation cost, second logistics management cost, and delay time cost, wherein the transportation risk cost is obtained through transportation risk analysis, wherein the transportation risk analysis is calculated by multiplying the probability of occurrence of multiple risk events by the losses caused by the occurrence of the multiple risk events, and selecting the minimum result among the multiple results as the transportation risk cost, wherein the risk events include at least one of the following: natural disasters, transportation route congestion, and accidents; An optimization module is used to optimize the first cost using a particle swarm optimization algorithm to obtain a target procurement plan; The optimization module includes: an input unit that inputs the first cost into a procurement cost constraint model to process the first cost using the procurement cost constraint model, thereby obtaining the processing result of the first cost on the procurement cost constraint model, wherein the procurement cost constraint model is constructed based on the total procurement amount and the procurement completion time; The first determining module includes: an acquisition unit, configured to acquire market-oriented data for the target product, wherein the market-oriented data includes data affecting the sales volume of the target product, and the market-oriented data includes: current sales time, current sales location, historical sales volume, historical selling price, sales strategy, current tax policy, current warehousing capacity information, and current logistics capacity information; and a first determining unit, configured to determine the procurement plan based on the market-oriented data, wherein the procurement plan includes: total procurement quantity and procurement completion time. An iterative unit is used to iteratively process the processing results until convergence, thereby obtaining the target procurement plan.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein the computer program, when executed by a processor, controls the device containing the computer-readable storage medium to perform the optimization method of the procurement scheme as described in claim 1 or 2.

5. A processor, characterized in that, The processor is used to run a computer program, wherein the computer program executes the optimization method of the procurement scheme as described in claim 1 or 2.

Images