CN111612259A - Warehouse allocation amount determining method and device, electronic equipment and readable storage medium - Google Patents
Warehouse allocation amount determining method and device, electronic equipment and readable storage medium Download PDFInfo
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- CN111612259A CN111612259A CN202010458104.2A CN202010458104A CN111612259A CN 111612259 A CN111612259 A CN 111612259A CN 202010458104 A CN202010458104 A CN 202010458104A CN 111612259 A CN111612259 A CN 111612259A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The application provides a warehouse transfer amount determination method, a warehouse transfer amount determination device, an electronic device and a readable storage medium, and the method comprises the following steps: determining a source warehouse and a target warehouse from a plurality of warehouses to be selected; calculating a difference N between the inventory of the source warehouse and the highest predicted sales; respectively calculating the N +1 allocation quantities from 0 to N according to allocation cost and an integer planning model to obtain final income prediction values of the N +1 target warehouses and corresponding prediction sales quantities of a plurality of time periods; determining a final income prediction value with the maximum income and the corresponding prediction sales volume of a plurality of time periods from the N +1 final income prediction values; and determining at least one time period for allocating the source warehouse to the target warehouse and the allocation amount corresponding to the at least one time period according to the predicted sales volume of the plurality of time periods and the inventory of the target warehouse. By the mode, the allocation amount between the source warehouse and the target warehouse can be accurately predicted on the premise of the maximum profit, so that the effect of better cleaning the inventory is achieved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a warehouse transfer amount determining method and device, electronic equipment and a readable storage medium.
Background
In the prior art, when the allocation amount of goods is predicted, the sales amount of the time period corresponding to the current year is usually predicted according to the sales amount of the same time period in the previous year, and the sales amount of the corresponding time period is compared with the inventory, so that whether the goods are allocated or not and the allocation amount are determined.
However, due to the influence of climate change and market change, the prediction of the allocation amount is usually not accurate enough.
Disclosure of Invention
An embodiment of the present application provides a method and an apparatus for determining a warehouse allocation amount, an electronic device, and a readable storage medium, so as to solve the problem in the prior art that the allocation amount prediction is inaccurate.
In a first aspect, an embodiment of the present application provides a method for determining a transfer amount of a warehouse, which is used for determining a transfer amount of goods from a source warehouse to a destination warehouse, and the method includes: determining a source warehouse and a target warehouse from a plurality of warehouses to be selected, wherein the source warehouse is a warehouse with an inventory exceeding the highest predicted sales amount of the source warehouse, and the target warehouse is a warehouse with an inventory smaller than the lowest predicted sales amount of the target warehouse; calculating a difference value N between the inventory of the source warehouse and the highest predicted sales amount of the source warehouse, wherein N is an adjustable upper limit; respectively calculating N +1 allocation quantities of the target warehouse from 0 to N according to allocation cost and an integer planning model to obtain final income prediction values of the N +1 target warehouses and predicted sales quantities of a plurality of time periods corresponding to each final income prediction value; determining a final income prediction value with the maximum income and prediction sales volumes of a plurality of time periods corresponding to the final income prediction value with the maximum income from the N +1 final income prediction values; and determining at least one time period for allocating the source warehouse to the target warehouse and the allocation amount corresponding to the at least one time period according to the predicted sales amount of the time periods corresponding to the final profit predicted value with the maximum profit and the inventory of the target warehouse.
In the above embodiment, the source warehouse, the destination warehouse, and the adjustable dialing upper limit N of the source warehouse are determined, and then, for the N +1 types of adjustable dialing conditions from the dialing amount of 0 to the dialing amount of N, the final profit prediction value of the destination warehouse and the predicted sales amount of the plurality of time periods corresponding to the final profit prediction value are calculated respectively, so as to obtain the final profit prediction values of the N +1 destination warehouses and the predicted sales amount of the plurality of time periods corresponding to each final profit prediction value. And selecting the maximum final income predicted value and the corresponding predicted sales volume of a plurality of time periods from the N +1 final income predicted values, and then determining the time period for allocating the source warehouse to the target warehouse and the corresponding allocation volume by combining the inventory of the target warehouse and the predicted sales volume of the plurality of time periods. By the mode, the allocation amount between the source warehouse and the target warehouse can be accurately predicted on the premise of the maximum profit, so that the effect of better cleaning the inventory is achieved.
In one possible design, determining a source warehouse from a plurality of candidate warehouses includes: for each candidate warehouse in a plurality of candidate warehouses, determining the highest forecast sublist amount of each time period in the plurality of time periods; calculating the sum of the highest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the highest forecasted sub-sales is the highest forecasted sales of the warehouse to be selected; and comparing the highest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the highest predicted sales volume smaller than the inventory as the source warehouse.
In the above embodiment, the highest predicted sales volume of each of the multiple candidate warehouses may be predicted first, and the highest predicted sales volume of each of the multiple candidate warehouses is compared with its own inventory, and if the highest predicted sales volume of each of the candidate warehouses is still smaller than its own inventory, it indicates that the inventory of the candidate warehouse is too large, and even if a part of the inventory is allocated, the prediction of the maximum profit value is not affected, so that the candidate warehouse may be used as the source warehouse.
In one possible design, the selecting the candidate warehouse with the highest predicted sales volume smaller than the own inventory as the source warehouse includes: if the highest predicted sales volume is smaller than a plurality of warehouses to be selected of the warehouse per se, calculating an adjustable upper limit N of each warehouse to be selected in the warehouses to be selected; and taking the warehouse to be selected with the maximum adjustable upper limit N as the source warehouse.
In the foregoing embodiment, if there are multiple candidate warehouses whose highest predicted sales volume is smaller than their own inventory, the adjustable upper limit N of each candidate warehouse may be calculated for the multiple candidate warehouses, and then the candidate warehouse with the maximum adjustable upper limit N is selected as the source warehouse. The large adjustable upper limit means that the selectivity is more, so that the larger prediction benefit is more favorably selected.
In one possible design, determining a destination warehouse from a plurality of candidate warehouses includes: for each candidate warehouse in a plurality of candidate warehouses, determining the lowest forecast sublist amount of each time period in the plurality of time periods; calculating the sum of the lowest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the lowest forecasted sub-sales is the lowest forecasted sales of the warehouse to be selected; and comparing the lowest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the lowest predicted sales volume larger than the inventory as the target warehouse.
In the above embodiment, the lowest predicted sales volume of each warehouse to be selected is predicted for a plurality of warehouses to be selected, and then the lowest predicted sales volume is compared with the inventory of the warehouse, and if none of the predicted lowest predicted sales volumes of the warehouses can be met, the warehouse to be selected has a high demand for goods, so that the warehouse to be selected can be used as the target warehouse.
In one possible design, the using the candidate warehouse with the lowest predicted sales volume larger than the own inventory as the destination warehouse includes: if the minimum predicted sales amount is larger than a plurality of warehouses to be selected of the warehouses, calculating a difference value between the minimum predicted sales amount of each warehouse to be selected in the warehouses to be selected and the warehouse to be selected, wherein the difference value is a to-be-supplemented amount; and taking the warehouse to be selected with the minimum amount to be supplied as the target warehouse.
In the above embodiment, if there are a plurality of candidate warehouses whose lowest predicted sales volume is greater than their own inventory, the amount to be replenished of each candidate warehouse may be calculated for the plurality of candidate warehouses, and then the candidate warehouse with the smallest amount to be replenished serves as the destination warehouse. The target warehouse with the minimum amount to be supplied is selected as the target warehouse, so that the target warehouse can meet the requirement of the lowest predicted sales amount as soon as possible, and other allocation amounts can be traversed on the basis of meeting the requirement of the lowest predicted sales amount, so that the maximum benefit which can be achieved by the target warehouse is calculated.
In one possible design, the step of calculating the N +1 allocation amounts of the target warehouse from 0 to N according to the allocation cost and the integer programming model to obtain the final income prediction values of the N +1 target warehouses includes: calculating the sum of the transfer amount and the stock of the target warehouse for each transfer amount in the N +1 transfer amounts, wherein the sum is the latest stock of the target warehouse; calculating a target warehouse with the latest cargo stock quantity by using the integer programming model to obtain a maximum profit predicted value of the target warehouse; and removing the allocation cost corresponding to the allocation amount from the maximum profit predicted value of the target warehouse to obtain a final profit predicted value corresponding to the target warehouse.
In the above embodiment, each of the N +1 allocation amounts corresponds to the latest inventory of one destination warehouse, the maximum profit prediction value is calculated for the destination warehouse of the latest inventory by using the integer programming model, and then the allocation cost corresponding to the allocation amount is deducted on the basis of the maximum profit prediction value, so that the final profit prediction value corresponding to the destination warehouse under the condition of the allocation amount can be obtained. And calculating the income based on the final income prediction value with the allocation cost deducted, and considering the influence caused by the allocation cost, so that the predicted income value is more accurate.
In a second aspect, an embodiment of the present application provides a warehouse allocation amount determining apparatus, configured to determine an allocation amount of goods from a source warehouse to a destination warehouse, where the apparatus includes: the warehouse determining module is used for determining a source warehouse and a target warehouse from a plurality of warehouses to be selected, wherein the source warehouse is a warehouse with an inventory exceeding the highest predicted sales amount of the source warehouse, and the target warehouse is a warehouse with an inventory smaller than the lowest predicted sales amount of the target warehouse; the allocation upper limit determining module is used for calculating a difference value N between the inventory of the source warehouse and the highest predicted sales volume of the source warehouse, wherein N is an adjustable allocation upper limit; the profit calculation module is used for calculating N +1 allocation quantities of the target warehouse from 0 to N according to allocation cost and an integer planning model respectively to obtain final profit predicted values of the N +1 target warehouses and predicted sales quantities of a plurality of time periods corresponding to each final profit predicted value; the maximum profit determining module is used for determining a final profit predicted value with the maximum profit from the N +1 final profit predicted values and the predicted sales volume of a plurality of time periods corresponding to the final profit predicted value with the maximum profit; and the allocation amount determining module is used for determining at least one time period for allocating the source warehouse to the target warehouse and the allocation amount corresponding to the at least one time period according to the predicted sales amount of the time periods corresponding to the final profit predicted value with the maximum profit and the inventory of the target warehouse.
In one possible design, the warehouse determination module is specifically configured to determine, for each warehouse to be selected in a plurality of warehouses to be selected, a highest forecasted sub-sales amount for each time period in the plurality of time periods; calculating the sum of the highest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the highest forecasted sub-sales is the highest forecasted sales of the warehouse to be selected; and comparing the highest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the highest predicted sales volume smaller than the inventory as the source warehouse.
In one possible design, the warehouse determining module is specifically configured to calculate an adjustable upper limit N of each warehouse to be selected among the multiple warehouses to be selected when there are multiple warehouses to be selected whose highest predicted sales volume is smaller than their own inventory; and taking the warehouse to be selected with the maximum adjustable upper limit N as the source warehouse.
In one possible design, the warehouse determination module is specifically configured to determine, for each warehouse to be selected in a plurality of warehouses to be selected, a lowest forecasted sub-sales amount for each time period in the plurality of time periods; calculating the sum of the lowest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the lowest forecasted sub-sales is the lowest forecasted sales of the warehouse to be selected; and comparing the lowest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the lowest predicted sales volume larger than the inventory as the target warehouse.
In one possible design, the warehouse determining module is specifically configured to calculate a difference between the lowest predicted sales amount of each warehouse to be selected among the multiple warehouses to be selected and the inventory of the warehouse to be selected when the lowest predicted sales amount is greater than the inventory of the warehouse to be selected, where the difference is an amount to be compensated; and taking the warehouse to be selected with the minimum amount to be supplied as the target warehouse.
In one possible design, the profit calculation module is specifically configured to calculate, for each of the N +1 allocation amounts, a sum of the allocation amount and the inventory of the destination warehouse, the sum being a latest inventory of the destination warehouse; calculating a target warehouse with the latest cargo stock quantity by using the integer programming model to obtain a maximum profit predicted value of the target warehouse; and removing the allocation cost corresponding to the allocation amount from the maximum profit predicted value of the target warehouse to obtain a final profit predicted value corresponding to the target warehouse.
In a third aspect, an embodiment of the present application provides an electronic device, including the method in the first aspect or any optional implementation manner of the first aspect.
In a fourth aspect, the present application provides a readable storage medium having stored thereon an executable program which, when executed by a processor, performs the method of the first aspect or any of the optional implementations of the first aspect.
In a fifth aspect, the present application provides an executable program product which, when run on a computer, causes the computer to perform the method of the first aspect or any possible implementation manner of the first aspect.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart illustrating a method for determining a warehouse transfer amount according to an embodiment of the present application;
FIG. 2 is a flow chart showing a part of the steps of step S110 in FIG. 1;
FIG. 3 is a flow chart showing a part of the steps of step S110 in FIG. 1;
FIG. 4 is a flowchart illustrating a specific step of step S130 in FIG. 1;
fig. 5 is a schematic structural block diagram of a warehouse transfer amount determination apparatus provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the comparison embodiment, when predicting the allocation amount of the goods, the sales amount of the time period corresponding to the current year is usually predicted according to the sales amount of the same time period in the previous year, and then the predicted sales amount is compared with the actual inventory, so as to determine whether the goods are allocated and the allocation amount. However, sales data over the same time period may have been too long in the past year and local markets or climates have changed, resulting in often inaccurate predictions of call volume.
In the method for determining the transfer amount of the warehouse provided by the embodiment of the application, the source warehouse, the destination warehouse and the upper limit N of the transfer amount of the source warehouse are determined, and then the N +1 types of transfer conditions from 0 transfer amount to N transfer amount are calculated respectively to obtain the final income prediction values of the N +1 destination warehouses and the predicted sales amount of a plurality of time periods corresponding to each final income prediction value. And selecting the maximum final income predicted value and the corresponding predicted sales volume of a plurality of time periods from the N +1 final income predicted values, and then determining the time period for allocating the source warehouse to the target warehouse and the corresponding allocation volume by combining the inventory of the target warehouse and the predicted sales volume of the plurality of time periods. By the mode, the allocation amount between the source warehouse and the target warehouse can be accurately predicted on the premise of the maximum profit, so that the effect of better cleaning the inventory is achieved.
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 shows a method for determining a warehouse allocation amount according to an embodiment of the present application, where the method may be executed by an electronic device, and the electronic device may be a server or a terminal device, and the method specifically includes the following steps S110 to S150:
step S110, determining a source warehouse and a destination warehouse from a plurality of warehouses to be selected, where the source warehouse is a warehouse whose inventory exceeds the highest predicted sales amount of the source warehouse, and the destination warehouse is a warehouse whose inventory is less than the lowest predicted sales amount of the destination warehouse.
The source warehouse is a warehouse for transferring goods, and the target warehouse is a warehouse for transferring goods to the corresponding warehouse. The method of determining the source and destination warehouses is described in detail below.
And step S120, calculating a difference value N between the inventory of the source warehouse and the highest predicted sales volume of the source warehouse, wherein N is an adjustable upper limit.
The inventory of the source warehouse is larger than the highest predicted sales volume of the source warehouse, namely, even if the source warehouse is sold according to the highest predicted sales volume, the inventory of the source warehouse still has a margin, and the margin is the allocation volume which can be allocated. The difference N between the inventory of the source warehouse and the highest forecasted sales may be calculated, which is the maximum allotment, i.e., the upper allotment limit.
It can be understood that the adjustable upper limit N is a quantifier for counting goods, and the unit of N may be one, or a group or a cluster. For example, if the goods to be transferred are not clothes, if there is a difference of 30 clothes between the stock and the highest predicted sales amount, N may be taken as a unit of "one", that is, N is 30; if there are 4000 garments in the difference between the inventory and the highest predicted sales, N may be taken as a unit of "group", where one group corresponds to 100 garments, i.e., N is 40 groups. The specific unit of count for N should not be construed as a limitation on the present application.
And S130, respectively calculating N +1 allocation quantities of the target warehouse from 0 to N according to allocation cost and an integer planning model, and obtaining final income prediction values of the N +1 target warehouses and prediction sales quantities of a plurality of time periods corresponding to each final income prediction value.
Referring to fig. 4, the step S130 specifically includes the following steps S131 to S133:
step S131, calculating the sum of the transfer amount and the stock of the target warehouse for each transfer amount in the N +1 transfer amounts, wherein the sum is the latest stock of the target warehouse.
For convenience of explanation, it is not assumed that the goods to be dialed are clothes, the upper dialing limit is 30 pieces, and the plurality of time periods are three months (i.e., 7 months, 8 months, and 9 months) of the third quarter, and the following explanation is made: the transfer amount of the target warehouse can be 0 at least and 30 at most, so that the transfer amount of the target warehouse has 31 transfer amounts of 0 to 30.
For each of the 31 transfer amounts, the specific transfer amount may be added to the inventory of the destination warehouse, which is the latest inventory of the destination warehouse. Can be expressed by the formula: m + Xi (i is 0,1,2,3.. 30), where M is the latest inventory, a is the inventory of the destination warehouse, and Xi is 31 allocation amounts.
For convenience of description, if the transfer amount is 20, the latest stock of the target warehouse is a + 20.
And step S132, calculating the target warehouse with the latest cargo quantity by using the integer programming model to obtain the maximum profit prediction value of the target warehouse.
Continuing with the above example, the latest stock quantity a +20 of the target warehouse is input into the integer programming model, and the integer programming model may output the maximum profit prediction value B corresponding to the latest stock quantity and the predicted sales volume of each of the time slots (i.e. 7 months, 8 months, and 9 months) corresponding to the maximum profit prediction value: 7 months c1 pieces, 8 months c2 pieces and 9 months c3 pieces.
Optionally, the specific steps of the integer programming model for calculation may be as follows:
i warehouse of i1As a source warehouse, i2For the purpose warehouse, I is all warehouses;
j is the clothes size; j is all sizes;
t is a time period; t is the total time period;
Qij: at warehouse i, the initial inventory of the cargo with the size of j (input quantity of the integer planning model);
qijpt: at warehouse i, at time t, the goods with size j predict sales (output quantity of the integer programming model) at price p;
pit: at warehouse i, price at time t; p is all price sets (input volume of integer programming model);
s: to adjust the cost;
xijpt: price selection decision, xijpt1 denotes that at time t in region i, the size selects the price pth price (decision variable) for j goods;
zijptthe size of j goods in the region i in the time period t corresponds to the quantity of reserved goods (decision variable) at the p-th price;
yi1j: the number of transfers (decision variables) of the item of size j from the source warehouse to the destination warehouse; y isi2j0 (decision variable).
Objective function (maximum revenue prediction for the destination warehouse):
wherein the content of the first and second substances,
the corresponding revenue prediction value for the inventory of the destination warehouse,
and predicting the yield corresponding to the transfer amount of the transfer destination warehouse.
And S133, removing the allocation cost corresponding to the allocation amount from the maximum profit predicted value of the target warehouse to obtain a final profit predicted value corresponding to the target warehouse.
If the allocation cost of each piece of clothes is not set to be a fixed value S, the final predicted benefit value is (B-20 × S), wherein the allocation cost is the sum of the logistics cost and the packaging cost.
According to the method, the allocation amount is 0 to 30, and the 31 allocation amounts are calculated to obtain 31 final income predicted values.
Each transfer amount in the N +1 transfer amounts respectively corresponds to the latest goods stock of a target warehouse, the maximum profit prediction value of the target warehouse of the latest goods stock is calculated by using an integer programming model, and then the transfer cost corresponding to the transfer amount is deducted on the basis of the maximum profit prediction value, so that the final profit prediction value corresponding to the target warehouse under the condition of the transfer amount can be obtained. And calculating the income based on the final income prediction value with the allocation cost deducted, and considering the influence caused by the allocation cost, so that the predicted income value is more accurate.
And step S140, determining a final profit predicted value with the maximum profit and predicted sales volume of a plurality of time periods corresponding to the final profit predicted value with the maximum profit from the N +1 final profit predicted values.
Continuing with the above example, after 31 final profit prediction values are obtained, the 31 final profit prediction values may be compared, and the final profit prediction value with the largest profit is selected from the 31 final profit prediction values, and the prediction sales volumes of a plurality of time periods corresponding to the final profit prediction values are obtained at the same time.
And if the final income prediction value corresponding to the transfer amount of 20 pieces is not the final income prediction value with the maximum income, the prediction sales of the target warehouse in a plurality of corresponding time periods are respectively 7-c 1 pieces in month, 8-c 2 pieces in month and 9-c 3 pieces in month.
And S150, determining at least one time period for allocating the source warehouse to the target warehouse and the allocation amount corresponding to the at least one time period according to the predicted sales amount of the time periods corresponding to the final profit predicted value with the maximum profit and the inventory of the target warehouse.
Continuing with the above example, through the above steps, it has been known that the predicted sales for the plurality of time segments are: 7, 8, c1 pieces, 8, c2 pieces and 9, c3 pieces, wherein the stock of the target warehouse is A pieces, and the following modes can be adopted when the allocation amount is determined:
the size relationship between the inventory a of the destination warehouse and the predicted sales volume c1 for 7 months is compared.
If a is less than c1, it means that in 7 months, the stock of the destination warehouse cannot meet the demand of the predicted sales volume, and therefore, the allocation amount c1-a of 7 months is calculated. Meanwhile, since the stock a of the destination warehouse has been consumed in 7 months, it means that the predicted sales amounts in 8 months and 9 months are both from the allocation amount. Therefore, the call amount in 8 months is equal to the predicted sales amount, which is c 2; the call volume at 9 months is equal to the predicted sales volume and is c 3.
If a is greater than or equal to c1, this indicates that the inventory of the warehouse at 7 months of age can meet the demand for the forecasted sales volume, and no call is needed for 7 months.
In the case where a is greater than or equal to c1, the calculation of the dialing amount for month 8 continues:
the destination warehouse for month 8 has a remaining inventory of a-c1, and the size relationship between the remaining inventory a-c1 and the forecasted sales for month 8, c2, is compared.
If a-c1 is less than c2, this means that the remaining inventory in the destination warehouse cannot meet the demand for the forecasted sales volume at month 8, and therefore, the allocation volume c2- (a-c1) for month 8 is calculated. Meanwhile, since 8 months have consumed the remaining inventory a-c1 of the destination warehouse, it means that the predicted sales volume for 9 months comes from the volume of transfers. Therefore, the call amount in 9 months is equal to the predicted sales amount, and is c 3.
If A-c1 is greater than or equal to c2, this indicates that the inventory remaining in the destination warehouse at month 8 may meet the demand for the forecasted sales volume, and no call is needed for month 8.
In the case where A-c1 is greater than or equal to c2, the calculation of the dialing amount for month 9 continues:
the remaining inventory of the destination warehouse for month 9 is a-c1-c2, and the size relationship between the remaining inventory a-c1-c2 and the forecasted sales volume for month 9, c3, is compared.
If a-c1-c2 is smaller than c3, it means that the remaining stock in the destination warehouse cannot meet the demand of the predicted sales volume in 9 months, and therefore, the allocation volume c3- (a-c1-c2) in 9 months is calculated.
If a-c1-c2 is greater than or equal to c3, this indicates that the remaining inventory of the destination warehouse in month 9 can meet the demand for the forecasted sales volume, and no allocation is needed in month 9.
And selecting the maximum final income predicted value and the corresponding predicted sales volume of a plurality of time periods from the N +1 final income predicted values, and then determining the time period for allocating the source warehouse to the target warehouse and the corresponding allocation volume by combining the inventory of the target warehouse and the predicted sales volume of the plurality of time periods. By the mode, the allocation amount between the source warehouse and the target warehouse can be accurately predicted on the premise of the maximum profit, so that the effect of better cleaning the inventory is achieved.
Optionally, referring to fig. 2, determining a source warehouse from a plurality of warehouses to be selected specifically includes the following steps S210 to S230:
step S210, for each warehouse to be selected in a plurality of warehouses to be selected, determining a highest predicted sub-sales amount of each time period in the plurality of time periods.
Optionally, for each time segment in the multiple time segments, the lowest selling price of the goods may be obtained, and the highest predicted sub-sales volume for each time segment may be obtained according to the corresponding functional relationship between the selling price and the sales volume.
At each time period, the goods may have a plurality of pending sales prices for selection by the user, and the pending sales prices may be obtained based on historical data, such as historical prices for goods of the same type and model.
Step S220, calculating a sum of the highest forecasted sub-sales of each warehouse to be selected in multiple time periods, where the sum of the highest forecasted sub-sales is the highest forecasted sales of the warehouse to be selected.
Step S230, comparing the highest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and using the warehouse to be selected, of which the highest predicted sales volume is smaller than the inventory of the warehouse to be selected, as the source warehouse.
The method can predict the highest predicted sales volume of each warehouse to be selected in the plurality of warehouses, compare the highest predicted sales volume of each warehouse to be selected with the inventory of the warehouse, and if the highest predicted sales volume of each warehouse to be selected is still smaller than the inventory of the warehouse to be selected, the inventory of the warehouse to be selected is too much, and even if a part of the inventory is dispatched, the prediction of the maximum profit value cannot be influenced, so that the warehouse to be selected can be used as a source warehouse.
For the source warehouse, the highest predicted sales amount can be used as an input amount and input into the integer programming model, and the maximum profit predicted value of the source warehouse and the predicted sales amount of each time segment in the multiple time segments corresponding to the maximum profit predicted value are obtained through the integer programming model.
Continuing with the above distance: the maximum profit prediction value of the source warehouse is as follows:
optionally, taking the candidate warehouse with the highest predicted sales volume smaller than the own inventory as the source warehouse includes: if the highest predicted sales volume is smaller than a plurality of warehouses to be selected of the warehouse per se, calculating an adjustable upper limit N of each warehouse to be selected in the warehouses to be selected; and taking the warehouse to be selected with the maximum adjustable upper limit N as the source warehouse.
If the maximum predicted sales volume is smaller than a plurality of to-be-selected warehouses of the self inventory, the adjustable upper limit N of each to-be-selected warehouse can be calculated for the plurality of to-be-selected warehouses, and then the to-be-selected warehouse with the maximum adjustable upper limit N is selected as the source warehouse. The large adjustable upper limit means that the selectivity is more, so that the larger prediction benefit is more favorably selected.
Optionally, referring to fig. 3, the determining a destination warehouse from a plurality of warehouses to be selected specifically includes the following steps S310 to S330:
step S310, for each warehouse to be selected in the multiple warehouses to be selected, determining the lowest predicted sub-sales amount of each time segment in the multiple time segments.
Optionally, for each time segment in the multiple time segments, the highest selling price of the goods may be obtained, and the lowest predicted sub-sales volume for each time segment may be obtained according to the corresponding functional relationship between the selling price and the sales volume.
Step S320, calculating a sum of the lowest forecasted sub-sales of each warehouse to be selected in multiple time periods, where the sum of the lowest forecasted sub-sales is the lowest forecasted sales of the warehouse to be selected.
Step S330, comparing the minimum predicted sales amount of each warehouse to be selected with the inventory of the warehouse to be selected, and using the warehouse to be selected, in which the minimum predicted sales amount is greater than the inventory of the warehouse to be selected, as the target warehouse.
And predicting the lowest predicted sales volume of each warehouse to be selected for a plurality of warehouses to be selected, then comparing the lowest predicted sales volume with the inventory of the warehouse to be selected, and if the predicted lowest predicted sales volumes of the warehouses cannot be met, indicating that the warehouse to be selected has high demand for goods, so that the warehouse to be selected can be used as a target warehouse.
Optionally, taking the candidate warehouse with the lowest predicted sales volume greater than the own inventory as the destination warehouse includes: if the minimum predicted sales amount is larger than a plurality of warehouses to be selected of the warehouses, calculating a difference value between the minimum predicted sales amount of each warehouse to be selected in the warehouses to be selected and the warehouse to be selected, wherein the difference value is a to-be-supplemented amount; and taking the warehouse to be selected with the minimum amount to be supplied as the target warehouse.
If there are a plurality of candidate warehouses with the lowest predicted sales volume larger than the inventory of the candidate warehouse, the supply volume of each candidate warehouse can be calculated for the plurality of candidate warehouses, and then the candidate warehouse with the minimum supply volume is used as the target warehouse. The target warehouse with the minimum amount to be supplied is selected as the target warehouse, so that the target warehouse can meet the requirement of the lowest predicted sales amount as soon as possible, and other allocation amounts can be traversed on the basis of meeting the requirement of the lowest predicted sales amount, so that the maximum benefit which can be achieved by the target warehouse is calculated.
Referring to fig. 5, fig. 5 illustrates a warehouse allocation amount determining apparatus provided in an embodiment of the present application, where the apparatus 500 includes:
a warehouse determination module 510, configured to determine a source warehouse and a destination warehouse from a plurality of candidate warehouses, where the source warehouse is a warehouse with an inventory exceeding the highest predicted sales amount of the source warehouse, and the destination warehouse is a warehouse with an inventory smaller than the lowest predicted sales amount of the destination warehouse.
And a dial upper limit determining module 520, configured to calculate a difference N between the inventory of the source warehouse and the highest predicted sales amount of the source warehouse, where N is an adjustable dial upper limit.
And a profit calculation module 530, configured to calculate, according to the allocation cost and the integer planning model, N +1 allocation quantities of the destination warehouse from 0 to N, respectively, to obtain final profit predicted values of the N +1 destination warehouses and predicted sales quantities of a plurality of time periods corresponding to each of the final profit predicted values.
And a maximum benefit determining module 540, configured to determine, from the N +1 final benefit predicted values, a final benefit predicted value with the maximum benefit and predicted sales volumes of a plurality of time periods corresponding to the final benefit predicted value with the maximum benefit.
And an allocation amount determining module 550, configured to determine at least one time period for allocating the source warehouse to the destination warehouse and an allocation amount corresponding to the at least one time period according to the predicted sales amount of the multiple time periods corresponding to the final profit prediction value with the largest profit and the inventory of the destination warehouse.
A warehouse determining module 510, configured to determine, for each warehouse to be selected in a plurality of warehouses to be selected, a highest forecasted sub-sales amount for each time period in the plurality of time periods; calculating the sum of the highest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the highest forecasted sub-sales is the highest forecasted sales of the warehouse to be selected; and comparing the highest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the highest predicted sales volume smaller than the inventory as the source warehouse.
The warehouse determining module 510 is specifically configured to calculate an adjustable upper limit N of each warehouse to be selected among the multiple warehouses to be selected when there are multiple warehouses to be selected whose highest predicted sales are smaller than their own inventory; and taking the warehouse to be selected with the maximum adjustable upper limit N as the source warehouse.
A warehouse determining module 510, configured to determine, for each warehouse to be selected in a plurality of warehouses to be selected, a lowest forecasted sublist amount for each time period in the plurality of time periods; calculating the sum of the lowest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the lowest forecasted sub-sales is the lowest forecasted sales of the warehouse to be selected; and comparing the lowest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the lowest predicted sales volume larger than the inventory as the target warehouse.
The warehouse determining module 510 is specifically configured to, when there are multiple warehouses to be selected whose lowest predicted sales are greater than their own inventory, calculate a difference between the lowest predicted sales of each warehouse to be selected in the multiple warehouses to be selected and their own inventory, where the difference is an amount to be supplemented; and taking the warehouse to be selected with the minimum amount to be supplied as the target warehouse.
A profit calculation module 530, specifically configured to calculate, for each of the N +1 allocation amounts, a sum of the allocation amount and the inventory of the destination warehouse, where the sum is a latest inventory of the destination warehouse; calculating a target warehouse with the latest cargo stock quantity by using the integer programming model to obtain a maximum profit predicted value of the target warehouse; and removing the allocation cost corresponding to the allocation amount from the maximum profit predicted value of the target warehouse to obtain a final profit predicted value corresponding to the target warehouse.
The warehouse allocation amount determining apparatus shown in fig. 5 corresponds to the warehouse allocation amount determining method shown in fig. 1, and details thereof are not repeated here.
Fig. 6 is a block diagram of a structure of an electronic device 600 in an embodiment of the present application, as shown in fig. 6. Electronic device 600 may include a processor 610, a communication interface 620, a memory 630, and at least one communication bus 640. Wherein communication bus 640 is used to enable direct, coupled communication of these components. The communication interface 620 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The processor 610 may be an integrated circuit chip having signal processing capabilities. The Processor 610 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 610 may be any conventional processor or the like.
The Memory 630 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 630 stores computer readable instructions, and when the computer readable instructions are executed by the processor 610, the electronic device 600 may perform the steps involved in the method embodiments of fig. 1 to 4.
The electronic device 600 may further include a memory controller, an input-output unit, an audio unit, and a display unit.
The memory 630, the memory controller, the processor 610, the peripheral interface, the input/output unit, the audio unit, and the display unit are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components may be electrically coupled to each other via one or more communication buses 640. The processor 610 is configured to execute executable modules stored in the memory 630, such as software functional modules or computer programs included in the apparatus 300.
The input and output unit is used for providing input data for a user to realize the interaction of the user and the server (or the local terminal). The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
The audio unit provides an audio interface to the user, which may include one or more microphones, one or more speakers, and audio circuitry.
The input and output unit is used for providing input data for a user to realize the interaction between the user and the processing terminal. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
It will be appreciated that the configuration shown in FIG. 6 is merely illustrative and that the electronic device 600 may include more or fewer components than shown in FIG. 6 or have a different configuration than shown in FIG. 6. The components shown in fig. 6 may be implemented in hardware, software, or a combination thereof.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of the method embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A warehouse allocation amount determination method for determining an allocation amount of goods from a source warehouse to a destination warehouse, the method comprising:
determining a source warehouse and a target warehouse from a plurality of warehouses to be selected, wherein the source warehouse is a warehouse with an inventory exceeding the highest predicted sales amount of the source warehouse, and the target warehouse is a warehouse with an inventory smaller than the lowest predicted sales amount of the target warehouse;
calculating a difference value N between the inventory of the source warehouse and the highest predicted sales amount of the source warehouse, wherein N is an adjustable upper limit;
respectively calculating N +1 allocation quantities of the target warehouse from 0 to N according to allocation cost and an integer planning model to obtain final income prediction values of the N +1 target warehouses and predicted sales quantities of a plurality of time periods corresponding to each final income prediction value;
determining a final income prediction value with the maximum income and prediction sales volumes of a plurality of time periods corresponding to the final income prediction value with the maximum income from the N +1 final income prediction values;
and determining at least one time period for allocating the source warehouse to the target warehouse and the allocation amount corresponding to the at least one time period according to the predicted sales amount of the time periods corresponding to the final profit predicted value with the maximum profit and the inventory of the target warehouse.
2. The method of claim 1, wherein determining a source warehouse from a plurality of candidate warehouses comprises:
for each candidate warehouse in a plurality of candidate warehouses, determining the highest forecast sublist amount of each time period in the plurality of time periods;
calculating the sum of the highest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the highest forecasted sub-sales is the highest forecasted sales of the warehouse to be selected;
and comparing the highest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the highest predicted sales volume smaller than the inventory as the source warehouse.
3. The method according to claim 2, wherein the step of using the candidate warehouse with the highest predicted sales volume smaller than the own inventory as the source warehouse comprises the steps of:
if the highest predicted sales volume is smaller than a plurality of warehouses to be selected of the warehouse per se, calculating an adjustable upper limit N of each warehouse to be selected in the warehouses to be selected;
and taking the warehouse to be selected with the maximum adjustable upper limit N as the source warehouse.
4. The method of claim 1, wherein determining a destination warehouse from a plurality of candidate warehouses comprises:
for each candidate warehouse in a plurality of candidate warehouses, determining the lowest forecast sublist amount of each time period in the plurality of time periods;
calculating the sum of the lowest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the lowest forecasted sub-sales is the lowest forecasted sales of the warehouse to be selected;
and comparing the lowest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the lowest predicted sales volume larger than the inventory as the target warehouse.
5. The method according to claim 4, wherein the step of using the candidate warehouse with the lowest predicted sales volume larger than the own inventory as the destination warehouse comprises the steps of:
if the minimum predicted sales amount is larger than a plurality of warehouses to be selected of the warehouses, calculating a difference value between the minimum predicted sales amount of each warehouse to be selected in the warehouses to be selected and the warehouse to be selected, wherein the difference value is a to-be-supplemented amount;
and taking the warehouse to be selected with the minimum amount to be supplied as the target warehouse.
6. The method according to claim 1, wherein the N +1 allocation amounts of the allocation amount of the destination warehouse from 0 to N are respectively calculated according to allocation cost and an integer planning model to obtain final profit predicted values of the N +1 destination warehouses, and the method comprises:
calculating the sum of the transfer amount and the stock of the target warehouse for each transfer amount in the N +1 transfer amounts, wherein the sum is the latest stock of the target warehouse;
calculating a target warehouse with the latest cargo stock quantity by using the integer programming model to obtain a maximum profit predicted value of the target warehouse;
and removing the allocation cost corresponding to the allocation amount from the maximum profit predicted value of the target warehouse to obtain a final profit predicted value corresponding to the target warehouse.
7. A warehouse allocation amount determination apparatus for determining an allocation amount of goods from a source warehouse to a destination warehouse, the apparatus comprising:
the warehouse determining module is used for determining a source warehouse and a target warehouse from a plurality of warehouses to be selected, wherein the source warehouse is a warehouse with an inventory exceeding the highest predicted sales amount of the source warehouse, and the target warehouse is a warehouse with an inventory smaller than the lowest predicted sales amount of the target warehouse;
the allocation upper limit determining module is used for calculating a difference value N between the inventory of the source warehouse and the highest predicted sales volume of the source warehouse, wherein N is an adjustable allocation upper limit;
the profit calculation module is used for calculating N +1 allocation quantities of the target warehouse from 0 to N according to allocation cost and an integer planning model respectively to obtain final profit predicted values of the N +1 target warehouses and predicted sales quantities of a plurality of time periods corresponding to each final profit predicted value;
the maximum profit determining module is used for determining a final profit predicted value with the maximum profit from the N +1 final profit predicted values and the predicted sales volume of a plurality of time periods corresponding to the final profit predicted value with the maximum profit;
and the allocation amount determining module is used for determining at least one time period for allocating the source warehouse to the target warehouse and the allocation amount corresponding to the at least one time period according to the predicted sales amount of the time periods corresponding to the final profit predicted value with the maximum profit and the inventory of the target warehouse.
8. The apparatus according to claim 7, wherein the warehouse determination module is specifically configured to determine, for each warehouse of a plurality of warehouses to be selected, a highest forecasted sub-sales amount for each time segment of the plurality of time segments; calculating the sum of the highest forecasted sub-sales of each warehouse to be selected in a plurality of time periods, wherein the sum of the highest forecasted sub-sales is the highest forecasted sales of the warehouse to be selected; and comparing the highest predicted sales volume of each warehouse to be selected with the inventory of the warehouse to be selected, and taking the warehouse to be selected with the highest predicted sales volume smaller than the inventory as the source warehouse.
9. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating over the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the method of any one of claims 1-6 when executed.
10. A readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the method of any one of claims 1-6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114219376A (en) * | 2022-02-22 | 2022-03-22 | 哈尔滨工业大学（威海） | Household appliance large-scale customized service resource scheduling method based on knowledge graph |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003312848A (en) * | 2002-04-23 | 2003-11-06 | Toshiba Corp | Product distribution system, product supply source device, product distribution method, and supply target warehouse terminal |
| 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 |
| CN108351999A (en) * | 2015-11-16 | 2018-07-31 | 甲骨文国际公司 | System and method for providing inventory allocation approach by all kinds of means for retailer |
| CN110046755A (en) * | 2019-04-03 | 2019-07-23 | 中国联合网络通信集团有限公司 | Allot the determination method and apparatus in warehouse |
| CN110689157A (en) * | 2018-07-04 | 2020-01-14 | 北京京东尚科信息技术有限公司 | Method and device for determining call relation |
-
2020
- 2020-05-26 CN CN202010458104.2A patent/CN111612259B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003312848A (en) * | 2002-04-23 | 2003-11-06 | Toshiba Corp | Product distribution system, product supply source device, product distribution method, and supply target warehouse terminal |
| 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 |
| CN108351999A (en) * | 2015-11-16 | 2018-07-31 | 甲骨文国际公司 | System and method for providing inventory allocation approach by all kinds of means for retailer |
| CN110689157A (en) * | 2018-07-04 | 2020-01-14 | 北京京东尚科信息技术有限公司 | Method and device for determining call relation |
| CN110046755A (en) * | 2019-04-03 | 2019-07-23 | 中国联合网络通信集团有限公司 | Allot the determination method and apparatus in warehouse |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114219376A (en) * | 2022-02-22 | 2022-03-22 | 哈尔滨工业大学（威海） | Household appliance large-scale customized service resource scheduling method based on knowledge graph |
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