CN114240509A - Product purchase analysis method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of computers, and discloses a product purchase analysis method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring historical purchasing data of a product; constructing an objective function and constraint conditions based on the historical purchasing data; constructing a purchasing transportation model according to the objective function and the constraint condition; acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data; and inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier. This application purchases the product in many suppliers department, enables the cost of transportation reduction.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for product purchase analysis, a computer device, and a storage medium.
Background
With the continuous development of society, finished products processed by some factories are more and more complex and various, and raw materials corresponding to the factories are more and more frequently and greatly purchased; in the prior art, raw materials and the like are often purchased by one or more fixed suppliers, the purchase quantity of each supplier is different, and the purchase quantity of each supplier is not accurately calculated by a factory, so that the transportation cost is often too high, and the problem that how to reduce the transportation cost in the raw material purchasing process by the factory is urgently to be solved is solved.
Disclosure of Invention
The application provides a product purchase analysis method and device, computer equipment and a storage medium, which are used for solving the problem that the transportation cost is high due to the existing purchase mode.
In order to solve the above problems, the present application provides a product purchase analysis method, including:
acquiring historical purchasing data of a product;
constructing an objective function and constraint conditions based on the historical purchasing data;
constructing a purchasing transportation model according to the objective function and the constraint condition;
acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data;
and inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier.
Further, the constructing an objective function based on the historical procurement data includes:
constructing the objective function according to the capacity data of the whole factory-transported vehicles from each supplier, the weight data of the products supplied to the factory from each supplier and the unit price of the whole factory-transported vehicles from each supplier in the historical purchasing data, wherein the expression of the objective function is as follows:
where T represents shipping cost, m represents the number of suppliers, n represents the number of plants, xijRepresents the weight of the product supplied by supplier i to plant j, cijRepresents the capacity, p, of the supplier i to transport the entire truck product to plant jijIndicating the unit price of the finished vehicle product transported by supplier i to factory j.
Further, the constraints include supplier constraints, demand constraints, supply constraints, and contract constraints, and the building constraints based on the historical procurement data includes:
determining the supplier constraint according to the following formula:
xij-Mzij≤0
zij-Mxij≤0
wherein, z isijIs represented by the following formulaij>0, then equal to 1, if xijWhen the value is less than or equal to 0, the value is equal to 0, and M represents the maximum number;
determining the demand constraint according to the following formula:
wherein v represents any of 0.1 to 0.9;
determining the feed constraint according to the following formula:
wherein s isiIndicates the weight of the product available from supplier i, djRepresents the demand of plant j;
determining the contractual constraints according to the following formula:
if there is no contract between supplier i and plant j, xijX is 0, otherwiseij>0。
Further, the constructing the procurement transportation model according to the objective function and the constraint condition comprises:
according to the objective function and the constraint conditions, constructing a purchasing transportation model by adopting the following formula:
wherein, cij(yij-1)-xij≤0，cijyij-xij≥0，yijIndicating the number of vehicles transported by supplier i to plant j.
Further, the inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and the outputting the data of purchasing products from each supplier includes:
and inputting the product demand data, the supply data and the finished automobile transportation data into a purchasing transportation model, solving by adopting a linear solver, and outputting data of products purchased to each supplier.
Further, before the acquiring the finished product requirement data, the method further includes:
constructing a product demand model according to product demand data in the historical purchasing data, product data required by production of each finished product, existing product data and product safety library data, wherein the expression of the product demand model is as follows:
wherein k represents the kth product, f represents the number of products, dkIndicating the required number of finished products k, stockkIndicating the existing stock quantity, pet, of finished product kkRepresents the amount of product required to produce a finished product k, before _ pet represents the inventory of existing product, safe _ stock _ pet represents the safe inventory of product, demiRepresenting the total amount of product purchase at plant i;
the converting the finished product demand data into product demand data comprises:
and converting the finished product demand data into product demand data by using the product demand model.
Further, before the acquiring the finished product requirement data, the method further includes:
and predicting the demand of the finished product in the future preset time by utilizing historical finished product sales data according to a demand prediction model to obtain the demand prediction quantity in the finished product demand data, wherein the demand prediction model is obtained by training based on an ANN-ARMA model.
In order to solve the above problem, the present application further provides a product purchase analysis device, including:
the first acquisition module is used for acquiring historical purchasing data of products;
the first construction module is used for constructing an objective function and a constraint condition based on the historical purchasing data;
the second construction module is used for constructing a purchasing transportation model according to the objective function and the constraint condition;
the second acquisition module is used for acquiring basic data and finished product demand data of a supplier and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data;
and the calculation module is used for inputting the product demand data, the supply data and the finished automobile transportation data into a purchasing transportation model and outputting data for purchasing products from each supplier.
In order to solve the above problem, the present application also provides a computer device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a product procurement analysis method as described above.
To solve the above problem, the present application also provides a non-transitory computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor, implement the product procurement analysis method as described above.
Compared with the prior art, the product purchase analysis method, the product purchase analysis device, the computer equipment and the storage medium provided by the embodiment of the application have at least the following beneficial effects:
acquiring historical purchasing data of a product, and constructing a target function and constraint conditions based on the historical purchasing data; constructing a purchasing transportation model according to the objective function and the constraint condition; acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data; inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier; the method and the system for purchasing the products from the multiple suppliers calculate the purchase amount of purchasing from each supplier under the condition that the transportation cost is kept small, and can balance the purchase price on the basis of reducing the transportation cost.
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In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for describing the embodiments of the present application, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a schematic flow chart illustrating a product procurement analysis method according to an embodiment of the application;
FIG. 3 is a block diagram of a product procurement analysis device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. One skilled in the art will explicitly or implicitly appreciate that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
The present application provides a product procurement analysis method, which can be applied to the system architecture 100 shown in fig. 1, and the system architecture 100 can include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the product purchase analysis method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the product purchase analysis apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The application provides a product purchase analysis method. Referring to fig. 2, fig. 2 is a schematic flow chart of a product purchase analysis method according to an embodiment of the present application.
In this embodiment, the product purchase analysis method includes:
s1, acquiring historical purchasing data of the product;
in the application, historical purchasing data can be obtained from a database, or historical purchasing data of products can be obtained from other butt-jointed systems; the historical purchasing data comprises the capacity data of the whole vehicle transported to the factory by each supplier, the weight data of products supplied to the factory by each supplier, the unit price of the whole vehicle transported to the factory by each supplier and the like.
When historical purchasing data are acquired from a database, a calling request is required to be sent to the database, and the calling request carries a signature checking token; and receiving the label checking result returned by the database, and calling the historical purchasing data in the database when the label checking result is passed.
The database is encrypted, and the label checking is needed when the historical purchasing data in the database is extracted, so that the safety of the data is ensured.
S2, constructing an objective function and constraint conditions based on the historical purchasing data;
specifically, the objective function is constructed according to the capacity data of the whole factory-transported vehicles transported by each supplier, the weight data of products supplied to the factory by each supplier, and the unit price of the whole factory-transported vehicles transported by each supplier in the historical purchasing data; and constructing supplier constraints, demand constraints, supply constraints and contract constraints according to the historical purchasing data.
Further, the constructing an objective function based on the historical procurement data includes:
constructing the objective function according to the capacity data of the whole factory-transported vehicles from each supplier, the weight data of the products supplied to the factory from each supplier and the unit price of the whole factory-transported vehicles from each supplier in the historical purchasing data, wherein the expression of the objective function is as follows:
where T represents shipping cost, m represents the number of suppliers, n represents the number of plants, xijRepresents the weight of the product supplied by supplier i to plant j, cijRepresents the capacity, p, of the supplier i to transport the entire truck product to plant jijIndicating the unit price of the finished vehicle product transported by supplier i to factory j.
In this embodiment, a factory purchases PET raw materials from suppliers, a plurality of existing suppliers provide PET raw materials for a plurality of factories, PET transaction belongs to industrial product transaction, and unlike conventional goods, if one factory gives all purchase demands to one supplier for providing, not only is the risk of short-term supply increased, but also the price of PET materials is easily controlled by the supplier for a long time, and the profit of the factory is affected. In this context, therefore, building a model of procurement shipping to meet production requirements and balance PET prices, a model that minimizes shipping costs, is of great strategic and economic importance to the plant. The objective function is even the smallest total freight.
The target function is constructed by utilizing the historical purchasing data, so that the total freight is minimum, and a purchasing transportation model is convenient to obtain for use subsequently.
Further, the constraints include supplier constraints, demand constraints, supply constraints, and contract constraints, and the building constraints based on the historical procurement data includes:
determining the supplier constraint according to the following formula:
xij-Mzij≤0
zij-Mxij≤0
wherein, z isijIs represented by the following formulaij>0, then equal to 1, if xijWhen the value is less than or equal to 0, the value is equal to 0, and M represents the maximum number;
determining the demand constraint according to the following formula:
wherein v represents any of 0.1 to 0.9;
determining the feed constraint according to the following formula:
wherein s isiIndicates the weight of the product available from supplier i, djRepresents the demand of plant j;
determining the contractual constraints according to the following formula:
if there is no contract between supplier i and plant j, xijX is 0, otherwiseij>0。
Specifically, in the supplier constraint condition, in order to prevent the PET processing from being monopolized by suppliers of suppliers, the purchasing demand is generally distributed to a plurality of suppliers according to a certain proportion, so the supplier constraint is obtained by limiting the number of suppliers to be more than or equal to 2;
in the requirement constraint condition, limiting the quantity v provided by each supplier to be at least larger than the total requirement, and avoiding that the products purchased by each supplier are too low or too few, wherein the value of v in the application is 0.1;
in the supply constraint, it is guaranteed that the sum of the weights of the products purchased by the various plants from each supplier cannot exceed the weight of the PET that it can supply, and that the sum of the weights of the PET supplied by the various suppliers to the same plant is greater than the demand of the plant.
The contractual constraint that the supplier and the factory have to contract to supply.
The supplier constraint conditions, the demand constraint conditions, the supply constraint conditions and the contract constraint conditions are constructed and obtained by utilizing historical purchasing data, so that the objective function meets the constraint conditions when the total freight is minimized, and the final purchasing transportation model outputs available data.
S3, constructing a purchasing transportation model according to the objective function and the constraint condition;
specifically, the procurement transportation model is constructed according to the linearized objective function and the constraint conditions by performing linearization processing on the objective function.
Further, the constructing the procurement transportation model according to the objective function and the constraint condition comprises:
according to the objective function and the constraint conditions, constructing a purchasing transportation model by adopting the following formula:
wherein, cij(yij-1)-xij≤0，cijyij-xij≥0，yijIndicating the number of vehicles transported by supplier i to plant j.
Specifically, the purchasing transportation model is constructed by linearizing the objective function and then combining the objective function with the constraint condition, namely according to the objective function
To linearize the objective function.
And constructing a purchase transportation model according to the objective function and the constraint condition to facilitate subsequent use, so that the total transportation cost can be kept low and the purchase price of the product can be balanced when the supplier is subsequently purchased.
S4, acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data;
specifically, through the basic data that receives the supplier and send to and the finished product demand data that obtains according to customer data, will finished product data turns into product demand data according to product demand model to follow-up purchase analysis that carries on, the basic data is including supply data and whole car transportation data, supply data includes data such as the weight of the supplier's that can supply PET, whole car transportation data is data such as current supplier's capacity and current supplier to the whole car PET's of each mill transportation price to each mill transportation.
Further, before the acquiring the finished product requirement data, the method further includes:
constructing a product demand model according to product demand data in the historical purchasing data, product data required by production of each finished product, existing product data and product safety library data, wherein the expression of the product demand model is as follows:
wherein k represents the kth product, f represents the number of products, dkIndicating the required number of finished products k, stockkIndicating the existing stock quantity, pet, of finished product kkRepresents the amount of product required to produce a finished product k, before _ pet represents the inventory of existing product, safe _ stock _ pet represents the safe inventory of product, demiRepresenting the total amount of product purchase at plant i;
the converting the finished product demand data into product demand data comprises:
and converting the finished product demand data into product demand data by using the product demand model.
Specifically, the finished product demand data is converted into product demand data according to the product demand model obtained through construction; the safety stock in the expression of the product demand model is obtained by pre-measuring or self-setting according to historical purchasing data by methods such as statistics, machine learning and the like.
And the product demand data, i.e. the required number d of products kkEqual to the quantity a of finished products k ordered by the existing orderkDemand forecast b for finished product kkAnd the safe stock c of finished products kkThe sum of (1). And bkAnd ckThe value of (b) is obtained by a prediction amount obtained by a method such as machine learning or by self-setting.
The PET purchasing total amount of each factory can be quickly obtained through the product demand model, and the processing efficiency is improved.
Further, before the acquiring the finished product requirement data, the method further includes:
and predicting the demand of the finished product in the future preset time by utilizing historical finished product sales data according to a demand prediction model to obtain the demand prediction quantity in the finished product demand data, wherein the demand prediction model is obtained by training based on an ANN-ARMA model.
In particular, for the demand forecast bkValue calculation using historical product sales data from a demand prediction model trained from the ANN-ARMA model, but bkHas a value of onlyTo preset the predicted value within the time.
The historical finished product sales data is obtained from a database or other systems in the docking.
The ANN-ARMA model is an integrated model of an Artificial Neural Network (ANN) and an autoregressive moving average (ARMA) model, the ANN has the characteristics of self-adaptability, self-learning function, nonlinear mapping capability, fault tolerance, robustness and the like, the ANN can learn and train by inputting and outputting data, and nonlinear mapping is established; the autoregressive moving average model is an important method for researching time series and is formed by mixing an autoregressive model (AR model for short) and a moving average model (MA model for short) on the basis. The two are integrated, and the demand prediction quantity has better prediction effect
By utilizing the demand forecasting model pair, the demand forecasting quantity can be obtained quickly and accurately, and the total purchasing number of the product can be calculated conveniently in the follow-up process.
And S5, inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier.
Further, the inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and the outputting the data of purchasing products from each supplier includes:
and inputting the product demand data, the supply data and the finished automobile transportation data into a purchasing transportation model, solving by adopting a linear solver, and outputting data of purchasing products to each supplier, namely, the weight of purchasing PET products from each supplier by a factory, so that the product purchasing under the conditions of multiple factories and multiple suppliers can be well solved, and the transportation cost can be effectively reduced.
By adopting the linear solver to solve, the data of products purchased by each supplier can be rapidly output, so that the processing efficiency is improved.
Acquiring historical purchasing data of a product, and constructing a target function and constraint conditions based on the historical purchasing data; constructing a purchasing transportation model according to the objective function and the constraint condition; acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data; inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier; the method and the system for purchasing the products from the multiple suppliers calculate the purchase amount of purchasing from each supplier under the condition that the transportation cost is kept small, and can balance the purchase price on the basis of reducing the transportation cost.
The present embodiment further provides a product procurement analysis device, as shown in fig. 3, which is a functional block diagram of the product procurement analysis device according to the present application.
The product procurement analysis device 100 can be installed in an electronic device. Depending on the functionality implemented, the product procurement analysis device 100 can include a first acquisition module 101, a first build module 102, a second build module 103, a second acquisition module 104, and a calculation module 105. A module, which may also be referred to as a unit in this application, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the first acquisition module 101 is used for acquiring historical purchasing data of products;
a first constructing module 102, configured to construct an objective function and a constraint condition based on the historical procurement data;
further, the first build model 102 includes a target build submodule;
the target construction submodule is used for constructing the target function according to the capacity data of the whole factory-transported vehicles transported by each supplier, the weight data of products supplied to the factory by each supplier and the unit price of the whole factory-transported vehicles transported by each supplier in the historical purchasing data, wherein the target function expression is as follows:
where T represents shipping cost, m represents the number of suppliers, n represents the number of plants, xijRepresents the weight of the product supplied by supplier i to plant j, cijRepresents the capacity, p, of the supplier i to transport the entire truck product to plant jijIndicating the unit price of the finished vehicle product transported by supplier i to factory j.
The target construction submodule constructs and obtains a target function by utilizing the historical purchasing data, so that the total freight is minimum, and a purchasing transportation model is conveniently obtained subsequently for use.
Further, the constraint conditions include supplier constraint conditions, demand constraint conditions, supply constraint conditions and contract constraint conditions, and the first construction model 102 includes a supplier constraint construction submodule, a demand constraint construction submodule, a supply constraint construction submodule and a contract constraint construction submodule;
the supplier constraint construction sub-module is used for determining the supplier constraint condition according to the following formula:
xij-Mzij≤0
zij-Mxij≤0
wherein, z isijIs represented by the following formulaij>0, then equal to 1, if xijWhen the value is less than or equal to 0, the value is equal to 0, and M represents the maximum number;
the requirement constraint construction submodule is used for determining the requirement constraint condition according to the following formula:
wherein v represents any of 0.1 to 0.9;
the supply constraint construction submodule is used for determining the supply constraint condition according to the following formula:
wherein s isiIndicates the weight of the product available from supplier i, djRepresents the demand of plant j;
the contract constraint construction submodule is used for determining the contract constraint condition according to the following formula:
if there is no contract between supplier i and plant j, xijX is 0, otherwiseij>0。
Through the cooperation of the supplier constraint construction submodule, the demand constraint construction submodule, the supply constraint construction submodule and the contract constraint construction submodule, the supplier constraint condition, the demand constraint condition, the supply constraint condition and the contract constraint condition are constructed and obtained by utilizing historical purchasing data, so that the objective function meets the constraint conditions when the total freight is minimized, and the final purchasing transportation model outputs available data. (ii) a
A second construction module 103, configured to construct a procurement transportation model according to the objective function and the constraint condition;
further, the second building module 103 comprises a model building submodule;
the model construction submodule is used for constructing a purchasing transportation model by adopting the following formula according to the objective function and the constraint condition:
wherein, cij(yij-1)-xij≤0，cijyij-xij≥0，yijIndicating the number of vehicles transported by supplier i to plant j.
And constructing a purchasing transportation model through the model construction sub-module according to the objective function and the constraint condition to facilitate subsequent use, so that the total freight can be kept low and the purchasing price of the product can be balanced when purchasing is subsequently carried out on a supplier.
The second obtaining module 104 is configured to obtain basic data and finished product demand data of a supplier, and convert the finished product demand data into product demand data, where the basic data of the supplier includes supply data and vehicle transportation data;
further, the product purchasing analysis device 100 further includes a product demand model building module, and the second obtaining module 104 includes a conversion sub-module;
the product demand model building module is used for building a product demand model according to product demand data in the historical purchasing data, product data required by production of all finished products, existing product data and product safety library data, wherein the expression of the product demand model is as follows:
wherein k represents the kth product, f represents the number of products, dkIndicating the required number of finished products k, stockkIndicating the existing stock quantity, pet, of finished product kkRepresents the amount of product required to produce a finished product k, before _ pet represents the inventory of existing product, safe _ stock _ pet represents the safe inventory of product, demiRepresenting the total amount of product purchase at plant i;
and the conversion submodule is used for converting the finished product demand data into product demand data by using the product demand model.
According to the cooperation of the product demand model building module and the conversion submodule, the PET purchasing total amount of each factory can be quickly obtained through the product demand model, and the processing efficiency is improved.
Further, the product purchase analysis apparatus 100 further includes a prediction module;
the forecasting module is used for forecasting the demand of the finished product in the future preset time according to a demand forecasting model and by utilizing historical finished product sales data to obtain the demand forecasting quantity in the finished product demand data, wherein the demand forecasting model is obtained based on ANN-ARMA model training.
The demand forecasting quantity can be quickly and accurately obtained by utilizing the demand forecasting model pair through the forecasting module, and the total purchasing number of the product can be conveniently calculated subsequently.
And the calculation module 105 is used for inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model and outputting data of purchasing products from each supplier.
Further, the calculation module 105 includes a linear solving submodule;
and the linear solving submodule is used for inputting the product demand data, the supply data and the finished automobile transportation data into a purchasing transportation model, adopting a linear solver to solve and outputting the data of purchasing products from each supplier.
The linear solver is adopted by the linear solving submodule to solve, so that data of products purchased by each supplier can be rapidly output, and the processing efficiency is improved.
By adopting the device, the product purchase analysis device 100 acquires historical purchase data of a product through the cooperative use of the first acquisition module 101, the first construction module 102, the second construction module 103, the second acquisition module 104 and the calculation module 105, and constructs an objective function and a constraint condition based on the historical purchase data; constructing a purchasing transportation model according to the objective function and the constraint condition; acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data; inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier; the method and the system for purchasing the products from the multiple suppliers calculate the purchase amount of purchasing from each supplier under the condition that the transportation cost is kept small, and can balance the purchase price on the basis of reducing the transportation cost.
The embodiment of the application also provides computer equipment. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed on the computer device 4 and various application software, such as computer readable instructions of a product purchase analysis method. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the product procurement analysis method.
The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.
The present embodiment implements the steps of the product purchase analysis method as described in the above embodiments when the processor executes the computer readable instructions stored in the memory, obtains historical purchase data of the product, and constructs an objective function and constraint conditions based on the historical purchase data; constructing a purchasing transportation model according to the objective function and the constraint condition; acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data; inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier; the method and the system for purchasing the products from the multiple suppliers calculate the purchase amount of purchasing from each supplier under the condition that the transportation cost is kept small, and can balance the purchase price on the basis of reducing the transportation cost.
Embodiments of the present application also provide a computer-readable storage medium storing computer-readable instructions, which are executable by at least one processor to cause the at least one processor to perform the steps of the product procurement analysis method as described above, obtain historical procurement data of a product, and construct an objective function and a constraint condition based on the historical procurement data; constructing a purchasing transportation model according to the objective function and the constraint condition; acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data; inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier; the method and the system for purchasing the products from the multiple suppliers calculate the purchase amount of purchasing from each supplier under the condition that the transportation cost is kept small, and can balance the purchase price on the basis of reducing the transportation cost.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
The product purchase analysis apparatus, the computer device, and the computer-readable storage medium according to the embodiments of the present application have the same technical effects as the product purchase analysis method according to the embodiments, and are not expanded herein.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A method for product procurement analysis, the method comprising:
acquiring historical purchasing data of a product;
constructing an objective function and constraint conditions based on the historical purchasing data;
constructing a purchasing transportation model according to the objective function and the constraint condition;
acquiring basic data and finished product demand data of a supplier, and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data;
and inputting the product demand data, the supply data and the vehicle transportation data into a purchasing transportation model, and outputting data of purchasing products from each supplier.
2. The product procurement analysis method of claim 1 characterized by, the building an objective function based on the historical procurement data comprises:
constructing the objective function according to the capacity data of the whole factory-transported vehicles from each supplier, the weight data of the products supplied to the factory from each supplier and the unit price of the whole factory-transported vehicles from each supplier in the historical purchasing data, wherein the expression of the objective function is as follows:
where T represents shipping cost, m represents the number of suppliers, n represents the number of plants, xijRepresents the weight of the product supplied by supplier i to plant j, cijRepresents the capacity, p, of the supplier i to transport the entire truck product to plant jijIndicating the unit price of the finished vehicle product transported by supplier i to factory j.
3. The product procurement analysis method of claim 2 characterized by, the constraints comprise supplier constraints, demand constraints, supply constraints, and contract constraints, and the building constraints based on the historical procurement data comprises:
determining the supplier constraint according to the following formula:
xij-Mzij≤0
zij-Mxij≤0
wherein, z isijIs represented by the following formulaij>0, then equal to 1, if xijWhen the value is less than or equal to 0, the value is equal to 0, and M represents the maximum number;
determining the demand constraint according to the following formula:
wherein v represents any of 0.1 to 0.9;
determining the feed constraint according to the following formula:
wherein s isiIndicates the weight of the product available from supplier i, djRepresents the demand of plant j;
determining the contractual constraints according to the following formula:
if there is no contract between supplier i and plant j, xijX is 0, otherwiseij>0。
4. The product procurement analysis method of claim 3 characterized by, the constructing a procurement transportation model according to the objective function and constraint conditions comprises:
according to the objective function and the constraint conditions, constructing a purchasing transportation model by adopting the following formula:
wherein, cij(yij-1)-xij≤0，cijyij-xij≥0，yijIndicating the number of vehicles transported by supplier i to plant j.
5. The product procurement analysis method of claim 1, wherein the inputting the product demand data, the supply data, and the bulk shipment data into a procurement shipping model, the outputting data for procurement of products to each of the suppliers comprises:
and inputting the product demand data, the supply data and the finished automobile transportation data into a purchasing transportation model, solving by adopting a linear solver, and outputting data of products purchased to each supplier.
6. The product procurement analysis method of claim 1 characterized by, prior to the acquiring finished product demand data, further comprising:
constructing a product demand model according to product demand data in the historical purchasing data, product data required by production of each finished product, existing product data and product safety library data, wherein the expression of the product demand model is as follows:
wherein k represents the kth product, f represents the number of products, dkIndicating the required number of finished products k, stockkIndicating the existing stock quantity, pet, of finished product kkRepresents the amount of product required to produce a finished product k, before _ pet represents the inventory of existing product, safe _ stock _ pet represents the safe inventory of product, demiRepresenting the total amount of product purchase at plant i;
the converting the finished product demand data into product demand data comprises:
and converting the finished product demand data into product demand data by using the product demand model.
7. The product procurement analysis method of claim 1 characterized by, prior to the acquiring finished product demand data, further comprising:
and predicting the demand of the finished product in the future preset time by utilizing historical finished product sales data according to a demand prediction model to obtain the demand prediction quantity in the finished product demand data, wherein the demand prediction model is obtained by training based on an ANN-ARMA model.
8. A product procurement analysis device, characterized by, the device comprises:
the first acquisition module is used for acquiring historical purchasing data of products;
the first construction module is used for constructing an objective function and a constraint condition based on the historical purchasing data;
the second construction module is used for constructing a purchasing transportation model according to the objective function and the constraint condition;
the second acquisition module is used for acquiring basic data and finished product demand data of a supplier and converting the finished product demand data into product demand data, wherein the basic data of the supplier comprises supply data and vehicle transportation data;
and the calculation module is used for inputting the product demand data, the supply data and the finished automobile transportation data into a purchasing transportation model and outputting data for purchasing products from each supplier.
9. A computer device, characterized in that the computer device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer readable instructions which, when executed by the processor, implement the product procurement analysis method of any of claims 1 to 7.
10. A computer-readable storage medium having computer-readable instructions stored thereon which, when executed by a processor, implement a product procurement analysis method according to any of claims 1 to 7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090063251A1 (en) * | 2007-09-05 | 2009-03-05 | Oracle International Corporation | System And Method For Simultaneous Price Optimization And Asset Allocation To Maximize Manufacturing Profits |
| CN105069540A (en) * | 2015-09-02 | 2015-11-18 | 华中科技大学 | Method for designing fuel procurement decision support system for thermal power enterprises |
| CN108108994A (en) * | 2017-11-10 | 2018-06-01 | 浙江中控软件技术有限公司 | For the plan optimization method of chemical enterprise supply chain |
| CN110852667A (en) * | 2019-09-18 | 2020-02-28 | 浙江工商大学 | Two-stage scheduling method for multi-period multi-product evanescent product supply chain network design |
| CN111401619A (en) * | 2020-03-10 | 2020-07-10 | 杭州网易再顾科技有限公司 | Purchase order processing method and device, electronic equipment and storage medium |
| CN111428927A (en) * | 2020-03-23 | 2020-07-17 | 中国人民解放军军事科学院军事科学信息研究中心 | Weapon equipment purchasing provider selection method and device based on delimiting algorithm |
| CN112381325A (en) * | 2020-11-27 | 2021-02-19 | 云南电网有限责任公司电力科学研究院 | Hydrogenation station planning method |
| CN113128761A (en) * | 2021-04-19 | 2021-07-16 | 天津大学 | Elastic supply chain network optimization method based on genetic algorithm |
-
2021
- 2021-12-22 CN CN202111582207.0A patent/CN114240509A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090063251A1 (en) * | 2007-09-05 | 2009-03-05 | Oracle International Corporation | System And Method For Simultaneous Price Optimization And Asset Allocation To Maximize Manufacturing Profits |
| CN105069540A (en) * | 2015-09-02 | 2015-11-18 | 华中科技大学 | Method for designing fuel procurement decision support system for thermal power enterprises |
| CN108108994A (en) * | 2017-11-10 | 2018-06-01 | 浙江中控软件技术有限公司 | For the plan optimization method of chemical enterprise supply chain |
| CN110852667A (en) * | 2019-09-18 | 2020-02-28 | 浙江工商大学 | Two-stage scheduling method for multi-period multi-product evanescent product supply chain network design |
| CN111401619A (en) * | 2020-03-10 | 2020-07-10 | 杭州网易再顾科技有限公司 | Purchase order processing method and device, electronic equipment and storage medium |
| CN111428927A (en) * | 2020-03-23 | 2020-07-17 | 中国人民解放军军事科学院军事科学信息研究中心 | Weapon equipment purchasing provider selection method and device based on delimiting algorithm |
| CN112381325A (en) * | 2020-11-27 | 2021-02-19 | 云南电网有限责任公司电力科学研究院 | Hydrogenation station planning method |
| CN113128761A (en) * | 2021-04-19 | 2021-07-16 | 天津大学 | Elastic supply chain network optimization method based on genetic algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 李姗姗;: "物流服务供应链订单分配优化及其遗传算法", 运筹与管理, no. 05, 25 October 2014 (2014-10-25) * |
| 韩加峰;卢虎生;: "钢铁企业三类含铁物料的协同采购研究", 物流科技, no. 12, 10 December 2010 (2010-12-10) * |
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