CN113610455A - System and method for adjusting and dialing engineering machinery accessories - Google Patents
System and method for adjusting and dialing engineering machinery accessories Download PDFInfo
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Abstract
The invention discloses a system and a method for allocating engineering machinery accessories. The system comprises: the data preprocessing module is configured to acquire inventory data of each warehouse and integrate the inventory data to convert the inventory data into a mathematical matrix; the accessory allocation module is in communication connection with the data preprocessing module and is configured to determine allocation schemes among all warehouses according to the mathematical matrix and the constructed mathematical model; the data storage module is in communication connection with the data preprocessing module and the accessory allocating module and is configured to store data information of the data preprocessing module and the accessory allocating module; and the accessory dialing billboard module is in communication connection with the data storage module and is configured to display data information of the data preprocessing module and the accessory dialing module. The invention saves the total cost of engineering mechanical part allocation and reduces the stagnant stock rate; and also improves the efficiency of maintaining warehouse inventory.
Description
Technical Field
The invention relates to the technical field of engineering machinery, in particular to a system and a method for allocating engineering machinery accessories.
Background
The accessory demand of the engineering machinery industry mainly depends on the maintenance and repair of the corresponding host, and the host can greatly influence the construction period of a client and bring extra economic loss when being out of schedule, so that the accessory demand has the characteristics of time urgency, multiple varieties, small single material demand and the like, most materials cannot form stable logistics, the stock network is dull and serious, and the accessories are not circulated. In the prior art, mutual allocation of outside warehouses is not carried out on the scheduling of engineering machinery accessories, and when monthly stock preparation is carried out, all warehouse requirements are delivered out of a warehouse through a main warehouse and an allocation sheet is generated. Therefore, a large amount of materials are excessive and sluggish in one part of the warehouse outside, and the other part of the warehouse is short of goods, so that the purchasing cost and the transportation cost are high.
Disclosure of Invention
The invention aims to provide a system and a method for allocating engineering machinery parts, which are used for solving the problems of high purchase cost and transportation cost related to the allocation of the engineering machinery parts in the prior art.
In order to achieve the above object, a first aspect of the present invention provides a system for allocating a work machine accessory, the system comprising:
the data preprocessing module is configured to acquire inventory data of each warehouse and integrate the inventory data to convert the inventory data into a mathematical matrix;
the accessory allocation module is in communication connection with the data preprocessing module and is configured to determine allocation schemes among all warehouses according to the mathematical matrix and the constructed mathematical model;
the data storage module is in communication connection with the data preprocessing module and the accessory allocating module and is configured to store data information of the data preprocessing module and the accessory allocating module;
and the accessory dialing billboard module is in communication connection with the data storage module and is configured to display data information of the data preprocessing module and the accessory dialing module.
In an embodiment of the present invention, the data preprocessing module includes:
a data acquisition sub-module configured to determine inventory data from the safety inventory, the machine learning forecast inventory, and the existing inventory of each warehouse;
the data cleaning submodule is configured to screen and clean the inventory data to obtain scheduling data of the transfer warehouse;
a matrixing sub-module configured to convert the scheduling data into a mathematical matrix.
In an embodiment of the invention, the data cleansing submodule is configured to:
determining allocation warehouses and allocation accessories corresponding to the allocation warehouses according to the inventory data of each warehouse;
and screening the transportation distance between the allocation warehouses, the weight of the allocation accessories and the purchase price to obtain an allocation data table.
In an embodiment of the invention, the accessory dialing module comprises:
a mathematical modeling sub-module configured to construct a mathematical model for determining a dial-up scheme;
and the transfer calculation submodule is configured to determine a transfer scheme according to the mathematical matrix and the mathematical model.
In an embodiment of the invention, the mathematical model satisfies the following formula:
argminf＝∑i,j,ks·(xijkzk)dij+∑j，kpkx1jk；
wherein, i represents the ith warehouse, j represents the jth warehouse, and i is 1 represents the total warehouse; k represents a fitting; z is a radical ofijkRepresents the weight of the accessory k; x is the number ofijkRepresents the number of parts k allocated from warehouse i to warehouse j (a non-negative integer); dijRepresents the distance from warehouse i to warehouse j; s represents the transportation cost per unit weight and distance; qikRepresenting the redundant inventory of parts k in warehouse i; mjkIndicating the stock of parts k in warehouse j; p is a radical ofkIndicating the purchase cost of part k.
In an embodiment of the invention, the dial calculation submodule is configured to:
inputting the mathematical matrix to a mathematical model to obtain a result matrix;
converting the result matrix into a corresponding transfer schedule;
the transfer schedule comprises a transfer warehouse, accessory models and transfer quantity.
In an embodiment of the present invention, a data storage module includes:
a source data storage submodule configured to store inventory data for each warehouse;
a result data storage submodule configured to store the allocation data of each warehouse;
a model optimization submodule configured to store parameter data of the mathematical model.
In an embodiment of the present invention, the inventory calling billboard module comprises:
an inventory status billboard sub-module configured to display data information of the data pre-processing sub-module;
and the dialing status board submodule is configured to display the data information of the accessory dialing module.
The invention provides a method for allocating engineering machinery accessories, which comprises the following steps:
determining inventory data according to the safety inventory, the machine learning forecast inventory and the existing inventory of each warehouse;
screening and cleaning the inventory data to obtain dispatching data of a dispatching warehouse;
converting the scheduling data into a mathematical matrix;
and determining a transfer scheme among all warehouses according to the mathematical matrix and the constructed mathematical model.
In the embodiment of the invention, determining the allocation scheme among the warehouses according to the mathematical matrix and the constructed mathematical model comprises the following steps:
constructing a mathematical model for determining a transfer scheme;
inputting the mathematical matrix to a mathematical model to obtain a result matrix;
converting the result matrix into a corresponding transfer schedule;
the transfer schedule comprises a transfer warehouse, accessory models and transfer quantity.
According to the technical scheme, the data preprocessing module and the accessory allocation module are combined with the machine learning prediction value to calculate the accessory inventory, so that the accuracy is improved, the mathematical model is introduced to carry out global optimization, the stock shortage is filled through the redundant inventory, the accessory purchasing cost is reduced, the total cost is saved, the automatic method is used for balancing the stock states of different warehouses and accessories, the warehouse distribution stability is improved, and the stagnant stock rate is reduced; and through data storage module and accessory allocation billboard module, can in time trail accessory stock and allocation status information, improved the maintenance efficiency of warehouse stock.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of a system for allocating an engineering machine tool accessory according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a system for adjusting and dialing a work machine accessory according to another embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method for adjusting an engineering machine tool accessory according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a method for allocating a work machine component according to another embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
It should be noted that, if directional indications (such as up, down, left, right, front, and back … …) are involved in the embodiment of the present invention, the directional indications are only used to explain the relative positional relationship between the components, the movement situation, and the like in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indications are changed accordingly.
In addition, if there is a description of "first", "second", etc. in an embodiment of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
According to the embodiment of the invention, a machine learning prediction and mixed integer optimization engineering machinery part allocation mathematical model is combined, and a part allocation optimization scheme among all warehouses is obtained according to the redundant inventory, the stock shortage, the delivery distance of all warehouse parts and the multidimensional data such as the quality, the purchase price and the sale price of all kinds of parts, so that the inventory of all warehouses is kept, the inventory gap of the parts is made up, and the stay of the parts is reduced.
Fig. 1 is a schematic structural diagram of a system for allocating a work machine accessory according to an embodiment of the present invention. As shown in fig. 1, the present invention provides a system for allocating an accessory of a construction machine, which may include:
the data preprocessing module 1 is configured to acquire inventory data of each warehouse and integrate the inventory data to convert the inventory data into a mathematical matrix;
the accessory allocation module 2 is in communication connection with the data preprocessing module and is configured to determine allocation schemes among all warehouses according to the mathematical matrix and the constructed mathematical model;
the data storage module 3 is in communication connection with the data preprocessing module and the accessory allocating module and is configured to store data information of the data preprocessing module and the accessory allocating module;
and the accessory dialing signboard module 4 is in communication connection with the data storage module and is configured to display data information of the data preprocessing module and the accessory dialing module.
In the embodiment of the invention, the system for allocating the engineering machinery comprises a data preprocessing module 1, an accessory allocating module 2, a data storage module 3 and an accessory allocating billboard module 4. The data preprocessing module 1 is in communication connection with the accessory allocating module 2, the data storage module 3 is in communication connection with the data preprocessing module 1 and the accessory allocating module 2 respectively, and the accessory allocating billboard module 4 is in communication connection with the data storage module 3. The embodiment of the invention can be combined with the machine learning model to predict the inventory data, can obtain more accurate data compared with the traditional inventory management, and the machine learning model can realize high-precision prediction of the optimal inventory by utilizing the inventory data of the product and the sales condition and the distribution condition of a certain node. In one example, inventory data for each part of each warehouse of an embodiment of the present invention may be obtained from safety stock, machine learning forecasting stock, and on-hand stock. For example, the stock status of the warehouse is calculated in units of months, and stock data of each part, safety stock + machine learning prediction stock-inventory on hand. Among them, the safety stock (also called insurance stock) is a buffer stock prepared to prevent uncertain factors of future material supply or demand (such as a large sudden order, unexpected interruption or sudden delay of delivery, etc.), and its size depends on uncertainty of supply and demand, customer service level (or order satisfaction rate), and backorder cost and stock holding cost. If the customer service level is higher, the safe stock is increased, which results in lower stock out cost and higher stock holding cost; conversely, with lower customer service levels, the safe inventory is reduced and results in higher backorder costs and lower inventory holding costs. The existing inventory refers to the available inventory of the material actually stored in the warehouse by the enterprise, for example, the existing inventory in a certain period is equal to the existing inventory in the previous time period plus the scheduled receiving amount in the same period, plus the scheduled order receiving amount in the same period and minus the gross demand. And after the stock data of each warehouse is obtained through the calculation formula, judging whether the stock data of each warehouse is larger than zero. And when the stock data of the warehouse is less than zero, judging that the stock data of the current warehouse is stock shortage. After determining the inventory data of each warehouse, the data preprocessing module 1 may also integrate and convert the inventory data into a mathematical matrix, so as to input a mathematical model to determine a transfer scheme. The warehouse judged as the stock shortage is a warehouse needing to transfer accessories from the outside, the warehouse judged as the redundant stock can transfer the accessories to the stock shortage, the warehouses and the accessories participating in transfer can be determined according to the stock data of each accessory in each warehouse, the distance between each warehouse and each warehouse, the weight of the accessories and other data are matched and screened to obtain a transfer data table, and the transfer data table is converted into a mathematical matrix form. The mathematical matrix of the embodiment of the invention can be formed by matrixing the allocation data of the allocation data table by respectively representing the accessories and the warehouse by rows and columns. For example, the first row represents a-part, the second row represents B-part … the first column represents warehouse # 1, the second column represents warehouse # 2 …, and so on. In this way, inventory data about the parts and warehouse is presented in a matrixed form for input to the mathematical model.
In the embodiment of the invention, the mathematical model is an operation and research optimization mathematical model, the accessory allocation plan is subjected to mathematical modeling and converted into a mixed integer programming problem, and the accessory allocation mathematical model is constructed by using the allocation cost minimization as an optimization target. And combining the mathematical matrix and the mathematical model obtained by the data preprocessing module 1 to obtain an accessory allocation scheme among all warehouses. The mixed integer programming algorithm integrates solving algorithms such as an accurate algorithm (a secant plane method and a branch and bound method), an approximate algorithm, a heuristic algorithm and the like. And inputting the warehouse and accessory data into the mathematical model in a mathematical matrix form for operation to obtain an optimization result. The result obtained through the mathematical model is also a matrix mode, so that the matrix result needs to be converted, and the result solved by the mixed integer programming algorithm is converted into an accessory allocation schedule containing information data such as an allocation warehouse, accessory models, allocation quantity and the like. The embodiment of the invention combines the machine learning prediction value to calculate the inventory of the parts, improves the accuracy, introduces the mathematical model to carry out global optimization, fills up the stock shortage through redundant stock, reduces the purchase cost of the parts, saves the total cost, balances the stock states of different warehouses and parts by using an automatic method, improves the stability of warehouse distribution and reduces the stagnant stock rate.
In the embodiment of the present invention, the data storage module 3 is communicatively connected to the data preprocessing module 1 and the accessory allocating module 2, and may store data of the data preprocessing module 1 and the accessory allocating module 2, such as source data, result data, and model optimization data. The source data is data such as safety stock, machine learning prestoring stock, actual stock, distribution distance between each warehouse, weight of accessories, purchase price and the like of each warehouse; the result data is the accessory data of each warehouse participating in allocation; the model optimization data is data such as model paths, model index data, model reference data and the like. The accessory calling signboard module 4 is also in communication connection with the data storage module 3, and can view data stored in the data storage module 3, for example, visual processing is performed on the accessory inventory data, and the inventory condition is displayed by taking a warehouse as a unit; and displaying the completion progress, allocation cost and other execution conditions of the accessory allocation scheme. According to the embodiment of the invention, the data storage module 3 and the accessory allocation billboard module 4 can track the inventory and allocation state information of the accessories in time, so that the maintenance efficiency of the warehouse inventory is improved.
Fig. 2 is a schematic structural diagram of a system for allocating a work machine accessory according to another embodiment of the present invention. As shown in fig. 2, the data preprocessing module 1 may include:
a data acquisition sub-module 11 configured to determine inventory data from the safety inventory, the machine learning forecast inventory and the on-hand inventory of each warehouse;
the data cleaning submodule 12 is configured to screen and clean the inventory data to obtain scheduling data of a transfer warehouse;
a matrixing sub-module 13 configured to convert the scheduling data into a mathematical matrix.
In an embodiment of the invention, inventory data for each part of each warehouse may be obtained from safety stock, machine learning forecasting stock, and on-hand stock. For example, the stock status of the warehouse is calculated in units of months, and stock data of each part, safety stock + machine learning prediction stock-inventory on hand. Among them, the safety stock (also called insurance stock) is a buffer stock prepared to prevent uncertain factors of future material supply or demand (such as a large sudden order, unexpected interruption or sudden delay of delivery, etc.), and its size depends on uncertainty of supply and demand, customer service level (or order satisfaction rate), and backorder cost and stock holding cost. If the customer service level is higher, the safe stock is increased, which results in lower stock out cost and higher stock holding cost; conversely, with lower customer service levels, the safe inventory is reduced and results in higher backorder costs and lower inventory holding costs. The existing inventory refers to the available inventory of the material actually stored in the warehouse by the enterprise, for example, the existing inventory in a certain period is equal to the existing inventory in the previous time period plus the scheduled receiving amount in the same period, plus the scheduled order receiving amount in the same period and minus the gross demand. And after the stock data of each warehouse is obtained through the calculation formula, judging whether the stock data of each warehouse is larger than zero. And when the stock data of the warehouse is less than zero, judging that the stock data of the current warehouse is stock shortage.
In an embodiment of the present invention, the planned inventory data may deviate from the actual inventory data obtained, and therefore, the data cleansing sub-module 12 may also cleanse and filter the inventory data. For example, warehouse # 1 has part a out of stock, but part a is not needed for this month and can therefore be screened and cleaned, selecting the inventory data for part B needed for this month.
In one example, the data cleansing submodule 12 may be configured to:
determining allocation warehouses and allocation accessories corresponding to the allocation warehouses according to the inventory data of each warehouse;
and screening the transportation distance between the allocation warehouses, the weight of the allocation accessories and the purchase price to obtain an allocation data table.
Specifically, the warehouse judged as the stock in short supply is a warehouse needing to transfer accessories from the outside, the warehouse judged as the redundant stock can transfer the accessories to the stock in short supply, the warehouses and the accessories participating in transfer can be determined according to the stock data of each accessory in each warehouse, the distance between each warehouse and each warehouse, the weight of the accessories and other data are matched and screened to obtain a transfer data table, and the transfer data table is converted into a mathematical matrix form. All warehouse and accessory data which can participate in allocation can be determined according to stock shortage and redundant stock, data such as the transportation distance between allocation warehouses and the weight of allocated accessories are screened out, and finally the matrixing submodule 3 matriculates allocation data of an allocation data table by representing the accessories and the warehouses respectively in rows and columns. For example, the first row represents a-part, the second row represents B-part … the first column represents warehouse # 1, the second column represents warehouse # 2 …, and so on. In this way, inventory data about the parts and warehouse is presented in a matrixed form for input to the mathematical model. In this way, inventory data about the parts and warehouse is presented in a matrixed form for input to the mathematical model.
As shown in fig. 2, in an embodiment of the present invention, the accessory dialing module 2 may include:
a mathematical modeling sub-module 21 configured to construct a mathematical model for determining a dialing scheme;
and a dial calculation sub-module 22 configured to determine a dial plan based on the mathematical matrix and the mathematical model.
In the embodiment of the invention, the mathematical model is an operation and research optimization mathematical model, the accessory allocation plan is subjected to mathematical modeling and converted into a mixed integer programming problem, and the accessory allocation mathematical model is constructed by using the allocation cost minimization as an optimization target. And combining the mathematical matrix and the mathematical model obtained by the data preprocessing module 1 to obtain an accessory allocation scheme among all warehouses. The mixed integer programming algorithm integrates solving algorithms such as an accurate algorithm (a secant plane method and a branch and bound method), an approximate algorithm, a heuristic algorithm and the like. And inputting the warehouse and accessory data into the mathematical model in a mathematical matrix form for operation to obtain an optimization result. The result obtained through the mathematical model is also a matrix mode, so that the matrix result needs to be converted, and the result solved by the mixed integer programming algorithm is converted into an accessory allocation schedule containing information data such as an allocation warehouse, accessory models, allocation quantity and the like. The embodiment of the invention combines the machine learning prediction value to calculate the inventory of the parts, improves the accuracy, introduces the mathematical model to carry out global optimization, fills up the stock shortage through redundant stock, reduces the purchase cost of the parts, saves the total cost, balances the stock states of different warehouses and parts by using an automatic method, improves the stability of warehouse distribution and reduces the stagnant stock rate.
In an embodiment of the invention, the mathematical model may satisfy the following formula:
argminf＝∑i,j,ks.(xijkzk)dij+∑j，kpkx1jk；
wherein, i represents the ith warehouse, j represents the jth warehouse, and i is 1 represents the total warehouse; k represents a fitting; z is a radical ofijkRepresents the weight of the accessory k; x is the number ofijkRepresents the number of parts k allocated from warehouse i to warehouse j (a non-negative integer); dijRepresents the distance from warehouse i to warehouse j; s represents the transportation cost per unit weight and distance; qikRepresenting the redundant inventory of parts k in warehouse i; mjkIndicating the stock of parts k in warehouse j; p is a radical ofkIndicating the purchase cost of part k.
It should be noted that the mathematical model according to the embodiment of the present invention is not limited to the above formula, and may be other mathematical models capable of performing operation optimization.
In an embodiment of the present invention, the dial calculation submodule 22 may be configured to:
inputting the mathematical matrix to a mathematical model to obtain a result matrix;
converting the result matrix into a corresponding transfer schedule;
the transfer schedule comprises a transfer warehouse, accessory models and transfer quantity.
Specifically, warehouse and accessory data are input into a mathematical model in a mathematical matrix form for operation so as to obtain an optimization result. The result obtained through the mathematical model is also a matrix mode, so that the matrix result needs to be converted, and the result solved by the mixed integer programming algorithm is converted into an accessory allocation schedule containing information data such as an allocation warehouse, accessory models, allocation quantity and the like.
As shown in fig. 2, in an embodiment of the present invention, the data storage module 3 may include:
a source data storage submodule 31 configured to store inventory data of each warehouse;
a result data storage submodule 32 configured to store the allocation data of each warehouse;
a model optimization submodule 33 configured to store parameter data of the mathematical model.
In the embodiment of the present invention, the data storage module 3 is communicatively connected to the data preprocessing module 1 and the accessory allocating module 2, and may store data of the data preprocessing module 1 and the accessory allocating module 2, such as source data, result data, and model optimization data. The source data is data such as safety stock, machine learning prestoring stock, actual stock, distribution distance between each warehouse, weight of accessories, purchase price and the like of each warehouse; the result data is the accessory data of each warehouse participating in allocation; the model optimization data is data such as model paths, model index data, model reference data and the like.
As shown in fig. 2, in an embodiment of the present invention, the inventory calling board module 4 may include:
an inventory status signboard sub-module 41 configured to display data information of the data preprocessing sub-module;
and a dial status signboard sub-module 42 configured to display data information of the accessory dial module.
In the embodiment of the invention, the accessory dial-up billboard module 4 is also in communication connection with the data storage module 3, and can view the data stored in the data storage module 3, for example, the accessory inventory data is visualized, and the inventory condition is displayed by taking a warehouse as a unit; and displaying the completion progress, allocation cost and other execution conditions of the accessory allocation scheme. According to the embodiment of the invention, the data storage module 3 and the accessory allocation billboard module 4 can track the inventory and allocation state information of the accessories in time, so that the maintenance efficiency of the warehouse inventory is improved.
Fig. 3 is a flowchart illustrating a method for allocating an engineering machine component according to an embodiment of the present invention. As shown in fig. 3, the present invention provides a method for adjusting an accessory of a construction machine, which may include:
step S31, determining inventory data according to the safety inventory, the machine learning forecast inventory and the existing inventory of each warehouse;
s32, screening and cleaning the inventory data to obtain dispatching data of a dispatching warehouse;
step S33, converting the scheduling data into a mathematical matrix;
and step S34, determining a transfer scheme among all warehouses according to the mathematical matrix and the constructed mathematical model.
The method for allocating the engineering machinery parts is applied to the system for allocating the engineering machinery parts, and the system comprises a data preprocessing module, an accessory allocating module, a data storage module and an accessory allocating billboard module. The data preprocessing module is in communication connection with the accessory allocating module, the data storage module is in communication connection with the data preprocessing module and the accessory allocating module respectively, and the accessory allocating billboard module is in communication connection with the data storage module. The embodiment of the invention can be combined with the machine learning model to predict the inventory data, can obtain more accurate data compared with the traditional inventory management, and the machine learning model can realize high-precision prediction of the optimal inventory by utilizing the inventory data of the product and the sales condition and the distribution condition of a certain node. In one example, inventory data for each part of each warehouse of an embodiment of the present invention may be obtained from safety stock, machine learning forecasting stock, and on-hand stock. For example, the stock status of the warehouse is calculated in units of months, and stock data of each part, safety stock + machine learning prediction stock-inventory on hand. Among them, the safety stock (also called insurance stock) is a buffer stock prepared to prevent uncertain factors of future material supply or demand (such as a large sudden order, unexpected interruption or sudden delay of delivery, etc.), and its size depends on uncertainty of supply and demand, customer service level (or order satisfaction rate), and backorder cost and stock holding cost. If the customer service level is higher, the safe stock is increased, which results in lower stock out cost and higher stock holding cost; conversely, with lower customer service levels, the safe inventory is reduced and results in higher backorder costs and lower inventory holding costs. The existing inventory refers to the available inventory of the material actually stored in the warehouse by the enterprise, for example, the existing inventory in a certain period is equal to the existing inventory in the previous time period plus the scheduled receiving amount in the same period, plus the scheduled order receiving amount in the same period and minus the gross demand. And after the stock data of each warehouse is obtained through the calculation formula, judging whether the stock data of each warehouse is larger than zero. And when the stock data of the warehouse is less than zero, judging that the stock data of the current warehouse is stock shortage.
In embodiments of the present invention, the planned inventory data may deviate from the actual inventory data acquired, and therefore, the inventory data may be cleaned and filtered. For example, warehouse # 1 has part a out of stock, but part a is not needed for this month and can therefore be screened and cleaned, selecting the inventory data for part B needed for this month. In one example, screening and cleansing inventory data to obtain dispatch data for a dispatch warehouse may include: determining allocation warehouses and allocation accessories corresponding to the allocation warehouses according to the inventory data of each warehouse; and screening the transportation distance between the allocation warehouses, the weight of the allocation accessories and the purchase price to obtain an allocation data table.
After the data cleaning and screening are carried out to determine the inventory data of each warehouse, the inventory data can be integrated and converted into a mathematical matrix, so that a mathematical model can be input to determine a transfer scheme. The warehouse judged as the stock shortage is a warehouse needing to transfer accessories from the outside, the warehouse judged as the redundant stock can transfer the accessories to the stock shortage, the warehouses and the accessories participating in transfer can be determined according to the stock data of each accessory in each warehouse, the distance between each warehouse and each warehouse, the weight of the accessories and other data are matched and screened to obtain a transfer data table, and the transfer data table is converted into a mathematical matrix form. The mathematical matrix of the embodiment of the invention can be formed by matrixing the allocation data of the allocation data table by respectively representing the accessories and the warehouse by rows and columns. For example, the first row represents a-part, the second row represents B-part … the first column represents warehouse # 1, the second column represents warehouse # 2 …, and so on. In this way, inventory data about the parts and warehouse is presented in a matrixed form for input to the mathematical model.
In the embodiment of the invention, the mathematical model is an operation and research optimization mathematical model, the accessory allocation plan is subjected to mathematical modeling and converted into a mixed integer programming problem, and the accessory allocation mathematical model is constructed by using the allocation cost minimization as an optimization target. And combining the mathematical matrix and the mathematical model to obtain the accessory allocation scheme among all the warehouses. The mixed integer programming algorithm integrates solving algorithms such as an accurate algorithm (a secant plane method and a branch and bound method), an approximate algorithm, a heuristic algorithm and the like. And inputting the warehouse and accessory data into the mathematical model in a mathematical matrix form for operation to obtain an optimization result. The result obtained through the mathematical model is also a matrix mode, so that the matrix result needs to be converted, and the result solved by the mixed integer programming algorithm is converted into an accessory allocation schedule containing information data such as an allocation warehouse, accessory models, allocation quantity and the like. The embodiment of the invention combines the machine learning prediction value to calculate the inventory of the parts, improves the accuracy, introduces the mathematical model to carry out global optimization, fills up the stock shortage through redundant stock, reduces the purchase cost of the parts, saves the total cost, balances the stock states of different warehouses and parts by using an automatic method, improves the stability of warehouse distribution and reduces the stagnant stock rate.
Fig. 4 is a flowchart illustrating a method for allocating a work machine component according to another embodiment of the present invention. As shown in fig. 4, the step S34 of determining the allocation scheme between the warehouses according to the mathematical matrix and the constructed mathematical model includes:
step S41, constructing a mathematical model for determining a transfer scheme;
step S42, inputting the mathematical matrix into the mathematical model to obtain a result matrix;
step S43, converting the result matrix into a corresponding transfer schedule;
the transfer schedule comprises a transfer warehouse, accessory models and transfer quantity.
In an embodiment of the invention, the mathematical model may satisfy the following formula:
argminf＝Ci,j,ks·(xijkzk)dij+∑j，kpkx1jk；
wherein, i represents the ith warehouse, j represents the jth warehouse, and i is 1 represents the total warehouse; k represents a fitting; z is a radical ofijkRepresents the weight of the accessory k; x is the number ofijkRepresents the number of parts k allocated from warehouse i to warehouse j (a non-negative integer); dijRepresents the distance from warehouse i to warehouse j; s represents the transportation cost per unit weight and distance; qikRepresenting the redundant inventory of parts k in warehouse i; mjkIndicating the stock of parts k in warehouse j; p is a radical ofkIndicating the purchase cost of part k.
It should be noted that the mathematical model according to the embodiment of the present invention is not limited to the above formula, and may be other mathematical models capable of performing operation optimization.
In the embodiment of the invention, warehouse and accessory data are input into a mathematical model in a form of a mathematical matrix to be operated so as to obtain an optimization result. The result obtained through the mathematical model is also a matrix mode, so that the matrix result needs to be converted, and the result solved by the mixed integer programming algorithm is converted into an accessory allocation schedule containing information data such as an allocation warehouse, accessory models, allocation quantity and the like. In summary, the embodiment of the invention calculates the inventory of the parts by combining the machine learning prediction value, improves the accuracy, introduces the mathematical model to perform global optimization, fills up the stock shortage through redundant inventory, reduces the purchase cost of the parts, saves the total cost, balances the stock states of different warehouses and parts by using an automatic method, improves the stability of warehouse distribution and reduces the stagnant stock rate.
In an embodiment of the present invention, the data storage module is communicatively connected to the data preprocessing module and the accessory allocating module, and may store data of the data preprocessing module and the accessory allocating module, such as source data, result data, and model optimization data. The source data is data such as safety stock, machine learning prestoring stock, actual stock, distribution distance between each warehouse, weight of accessories, purchase price and the like of each warehouse; the result data is the accessory data of each warehouse participating in allocation; the model optimization data is data such as model paths, model index data, model reference data and the like.
In the embodiment of the invention, the accessory dial-up billboard module is also in communication connection with the data storage module, and can view the data stored by the data storage module, for example, the accessory inventory data is visualized, and the inventory condition is displayed by taking a warehouse as a unit; and displaying the completion progress, allocation cost and other execution conditions of the accessory allocation scheme. According to the embodiment of the invention, through the data storage module and the accessory allocation billboard module, the inventory and allocation state information of the accessories can be tracked in time, and the maintenance efficiency of the warehouse inventory is improved.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (10)
1. A system for deploying an engineering machine accessory, the system comprising:
a data preprocessing module configured to acquire inventory data of each warehouse and integrate the inventory data to convert into a mathematical matrix;
the accessory allocation module is in communication connection with the data preprocessing module and is configured to determine allocation schemes among all warehouses according to the mathematical matrix and the constructed mathematical model;
a data storage module, communicatively connected to the data preprocessing module and the accessory allocation module, configured to store data information of the data preprocessing module and the accessory allocation module;
and the accessory dialing signboard module is in communication connection with the data storage module and is configured to display data information of the data preprocessing module and the accessory dialing module.
2. The system of claim 1, wherein the data pre-processing module comprises:
a data acquisition sub-module configured to determine inventory data from the safety inventory, the machine learning forecast inventory, and the existing inventory of each warehouse;
the data cleaning submodule is configured to screen and clean the inventory data to obtain scheduling data of a transfer warehouse;
a matrixing sub-module configured to convert the scheduling data into a mathematical matrix.
3. The system of claim 2, wherein the data cleansing sub-module is configured to:
determining allocation warehouses and allocation accessories corresponding to the allocation warehouses according to the inventory data of each warehouse;
and screening the transportation distance between the allocation warehouses, the weight of the allocation accessories and the purchase price to obtain an allocation data table.
4. The system of claim 1, wherein the accessory dialing module comprises:
a mathematical modeling sub-module configured to construct a mathematical model for determining a dial-up scheme;
and the allocation calculation submodule is configured to determine an allocation scheme according to the mathematical matrix and the mathematical model.
5. The system of claim 4, wherein the mathematical model satisfies the following formula:
argminf＝∑i,j,ks.(xijkZk)dij+∑j，kpkx1jk；
wherein, i represents the ith warehouse, j represents the jth warehouse, and i is 1 represents the total warehouse; k represents a fitting; z is a radical ofijkRepresents the weight of the accessory k; x is the number ofijkRepresents the number of parts k allocated from warehouse i to warehouse j (a non-negative integer); dijRepresents the distance from warehouse i to warehouse j; s represents the transportation cost per unit weight and distance; qikRepresenting the redundant inventory of parts k in warehouse i; mjkIndicating the stock of parts k in warehouse j; p is a radical ofkIndicating the purchase cost of part k.
6. The system of claim 5, wherein the dial calculation sub-module is configured to:
inputting the mathematical matrix to the mathematical model to derive a result matrix;
converting the result matrix into a corresponding transfer schedule;
the transfer schedule comprises a transfer warehouse, accessory models and transfer quantity.
7. The system of claim 1, wherein the data storage module comprises:
a source data storage submodule configured to store inventory data for each warehouse;
a result data storage submodule configured to store the allocation data of each warehouse;
a model optimization submodule configured to store parameter data of the mathematical model.
8. The system of claim 1, wherein the inventory dialboard module comprises:
an inventory status billboard sub-module configured to display data information of the data pre-processing sub-module;
and the dial-up status signboard sub-module is configured to display the data information of the accessory dial-up module.
9. A method for adjusting an engineering machine tool accessory, the method comprising:
determining inventory data according to the safety inventory, the machine learning forecast inventory and the existing inventory of each warehouse;
screening and cleaning the inventory data to obtain dispatching data of a dispatching warehouse;
converting the scheduling data into a mathematical matrix;
and determining a transfer scheme among all warehouses according to the mathematical matrix and the constructed mathematical model.
10. The method of claim 9, wherein determining a transfer plan between warehouses based on the mathematical matrix and the constructed mathematical model comprises:
constructing a mathematical model for determining a transfer scheme;
inputting the mathematical matrix to the mathematical model to derive a result matrix;
converting the result matrix into a corresponding transfer schedule;
the transfer schedule comprises a transfer warehouse, accessory models and transfer quantity.
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