CN112215530B - Bin selection method and device - Google Patents
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Abstract
The invention discloses a bin selection method and device, and relates to the technical field of logistics. Wherein the method comprises the following steps: processing the cargo order history data of the user based on the trained first deep learning model to obtain cargo order prediction data of the user in a preset period; inputting the goods order prediction data into a pre-constructed warehouse selection model to select an optimal storage warehouse for the user from all available warehouses; wherein, the objective function of the bin selection model is as follows: the overall cost required by the user to store and distribute goods based on the warehouse selected is minimized. By the method, the optimal warehouse selection scheme can be automatically planned for the user, the rationality of warehouse selection is improved, the cost of goods storage and delivery is reduced, and the goods delivery timeliness is improved. Furthermore, personalized and scientific logistics service can be provided for users (such as merchants) by utilizing the self-warehouse resources.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to a bin selection method and device.
Background
With the development of the logistics industry, logistics companies are gradually opened to the outside, and various business demands are also accompanied. Because the logistics system of the logistics company is strong and the warehouse is nationwide, merchants hope to store and distribute goods for the logistics company by means of the strong logistics system of the logistics company so as to save cost and improve distribution timeliness.
Currently, none of the nationwide companies provides integrated storage and distribution services for numerous merchants. Therefore, after the logistics company is gradually opened to the outside, powerful logistics service is provided for the merchant, and it is very meaningful to meet various demands of the merchant.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art: in providing integrated storage and distribution services, it is a first need to address the need to select which warehouse or warehouses to provide goods storage and distribution services to merchants due to the multitude of warehouses of the logistics company. If the warehouse is unreasonable to select, the goods storage and distribution cost of merchants can be increased undoubtedly, and the goods distribution timeliness is reduced.
Disclosure of Invention
In view of the above, the invention provides a bin selection method and device, which can automatically plan an optimal bin selection scheme for a user, improve the rationality of warehouse selection, reduce the cost of goods storage and delivery, and improve the goods delivery timeliness.
To achieve the above object, according to a first aspect of the present invention, there is provided a bin selection method.
The bin selection method comprises the following steps: processing the cargo order history data of the user based on the trained first deep learning model to obtain cargo order prediction data of the user in a preset period; inputting the goods order prediction data into a pre-constructed warehouse selection model to select an optimal storage warehouse for the user from all available warehouses; wherein, the objective function of the bin selection model is as follows: the overall cost required by the user to store and distribute goods based on the warehouse selected is minimized.
Optionally, the bin selection model satisfies at least one of the following constraints: the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input.
Optionally, the first deep learning model includes: LSTM model.
Optionally, the method further comprises: after the step of inputting the cargo order forecast data into a pre-built warehouse selection model to select an optimal storage warehouse for the user from all available warehouses is performed, information of the optimal storage warehouse selected for the user is returned to the user.
Optionally, the method further comprises: after the step of inputting the goods order prediction data into a pre-constructed warehouse selection model to select an optimal storage warehouse for the user from all available warehouses is executed, the goods order prediction data of the user in each optimal storage warehouse is obtained, and the goods order prediction data of the optimal storage warehouse is processed based on a trained second deep learning model to obtain inventory prediction data of the user in the optimal storage warehouse.
Optionally, the second deep learning model is a linear chain member random field model, and the step of processing the cargo order forecast data of the optimal storage warehouse based on the trained second deep learning model is performed by adopting a viterbi algorithm.
To achieve the above object, according to a second aspect of the present invention, there is provided a cartridge selection device.
The bin selection device of the invention comprises: the determining module is used for processing the goods order history data of the user based on the trained first deep learning model so as to obtain goods order prediction data of the user in a preset period; the selection module is used for inputting the goods order prediction data into a pre-constructed warehouse selection model so as to select an optimal storage warehouse for the user from all available warehouses; wherein, the objective function of the bin selection model is as follows: the overall cost required by the user to store and distribute goods based on the warehouse selected is minimized.
To achieve the above object, according to a third aspect of the present invention, there is provided an electronic apparatus.
The electronic device of the present invention includes: one or more processors; and a storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the bin selection method of the present invention.
To achieve the above object, according to a fourth aspect of the present invention, a computer-readable medium is provided.
The computer readable medium of the present invention has stored thereon a computer program which, when executed by a processor, implements the bin selection method of the present invention.
One embodiment of the above invention has the following advantages or benefits: the optimal bin selection scheme can be automatically planned for the user, the rationality of warehouse selection is improved, the cost of goods storage and distribution is reduced, and the goods distribution timeliness is improved by pre-constructing the bin selection model and setting the objective function of the bin selection model to be 'the total cost required by the user for goods storage and distribution based on the selected warehouse', processing the goods order historical data of the user based on the trained first deep learning model so as to obtain the goods order prediction data of the user in a preset period, inputting the goods order prediction data of the user in the preset period into the pre-constructed bin selection model for processing, and the like. Furthermore, personalized and scientific logistics service can be provided for users (such as merchants) by utilizing the self-warehouse resources.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main flow of a bin selection method according to a first embodiment of the invention;
FIG. 2 is a schematic diagram of the main flow of a bin selection method according to a second embodiment of the invention;
FIG. 3 is a schematic diagram of the main flow of a bin selection method according to a third embodiment of the invention;
Fig. 4 is a schematic view of the main modules of a cartridge selection device according to a fourth embodiment of the invention;
fig. 5 is a schematic view of the main modules of a cartridge selection device according to a fifth embodiment of the invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
Fig. 7 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It is noted that embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Fig. 1 is a schematic diagram of the main flow of a bin selection method according to the first embodiment of the invention. As shown in fig. 1, the bin selection method in the embodiment of the invention includes:
Step S101, processing the cargo order history data of the user based on the trained first deep learning model to obtain cargo order prediction data of the user in a preset period.
In an alternative embodiment, the cargo order history data of the user can be obtained in real time, and the cargo order history data is processed in real time based on the trained first deep learning model, so that cargo order prediction data of the user in a preset period can be obtained. Illustratively, the preset period of time may be one month, half year, one year, or the like. Wherein the first deep learning model comprises: a time recurrent neural network model, an LSTM (long short term memory network) model, or other deep learning model that can be used for timing prediction.
In another alternative embodiment, the cargo order history data of each user may be obtained in advance, the cargo order history data is processed based on the trained first deep learning model, and then the cargo order prediction data of each user obtained by processing is stored in a database. When the bin selection method provided by the embodiment of the invention is executed, the cargo order prediction data of the user in the preset period can be obtained by directly inquiring the database.
In addition, in the implementation, in addition to the data for predicting the order of the goods, data required for calculation such as available warehouse collection, unit price for storing the goods, unit price for delivering and transporting the goods, distance for transporting the goods, and volume of the goods are determined. The determined data of the forecast order of the goods and the data required by other operations of the user in the preset time period are input into the warehouse selecting model.
And step S102, inputting the goods order prediction data into a pre-constructed warehouse selection model to select an optimal storage warehouse for the user from all available warehouses. Wherein, the objective function of the bin selection model is as follows: the overall cost required by the user to store and distribute goods based on the warehouse selected is minimized.
Further, the bin selection model satisfies at least one of the following constraints: 1. the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; 2. the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input. By setting the constraint conditions, the requirements of users on order distribution timeliness and the number of selected storage warehouses can be met, and the flexibility of the warehouse selection model is improved.
In addition, in the concrete implementation, in order to improve the solving efficiency of the bin selection model, a branch-and-bound algorithm can be adopted for solving. The branch-and-bound method is a very widely used algorithm whose basic idea is to search all feasible solution spaces for constrained optimization problems. The algorithm, when executed in detail, continuously partitions the total feasible solution space into smaller and smaller subsets (called branches) and computes a lower or upper bound (called bounds) for the values of the solutions within each subset. After each branch, no further branches are made for those subsets where the limit exceeds the known feasible solution value. Thus, many subsets of solutions (i.e., many nodes on the search tree) may be disregarded, thereby reducing the scope of the search. This process continues until a viable solution is found whose value is not greater than the limits of any subset.
The bin selection model is described in detail below in connection with one specific example. In this particular example, the objective function of the bin selection model may be expressed as:
Wherein the first item Representing the cost required by the user to store goods based on the selected warehouse; second item/>Representing the cost required by the user for goods delivery based on the selected warehouse; x ij is a decision variable, the value of which is 1 or 0, x ij is 1, which indicates that warehouse i can cover the area where warehouse j is located (i.e., the order in the area where warehouse j is located in the distribution range of warehouse i), and x ij is 0, which indicates that warehouse i cannot cover the area where warehouse j is located; f ij is a decision variable, the value of the decision variable is 1 or 0, f ij is 1, the selection of the warehouse i as the storage warehouse, and f ij is 0, the non-selection of the warehouse i as the storage warehouse; q jm is a known variable, which takes a value of 1 or 0, q jm is 1 indicating that there is an order to be delivered to the region where warehouse j is located, and q jm is 0 indicating that there is no order to be delivered to the region where warehouse j is located; p mn is a known variable representing the quantity of the item n contained in order m; c in is a known variable representing the individual inventory cost of the good n in warehouse i; v n is a known variable representing the volume of cargo n; d ij is a known variable representing the distance from warehouse i to warehouse j; r+f is a known variable representing the set of all available warehouses; w is a known variable representing the unit price of delivery and transportation of goods per cubic meter per kilometer; m is a known variable representing a collection of orders over a predetermined period of time (e.g., half a year), and N is a known variable representing a collection of goods in an order.
Further, in this particular example, the constraints of the bin selection model may be expressed as:
Wherein constraints (1) and (2) represent constraint relationships between decision variables f i and x ij, constraint (3) represents that if repository i is selected as the storage repository, repository i can cover the region in which it is located (i.e., orders in the region in which repository i is located can be dispatched by repository i); constraint (4) indicates that the number of first type warehouses (such as RDC warehouses) is less than a preset threshold RN; the constraint (5) indicates that the number of second type warehouses, such as FDC warehouses, is smaller than a preset threshold FN; constraint (6) indicates that the total number of available warehouses (such as FDC warehouses) is less than a preset threshold DN; constraint (7) indicates that all storage warehouses selected for the user can cover all available warehouses; the constraint condition (8) indicates that the ratio of the Order quantity of the goods capable of meeting the requirement of the delivery time effect to the total Order quantity Order of the user is more than or equal to a preset threshold value Per; constraint (9) indicates that if an order from a region where one warehouse is located can be delivered by multiple warehouses, then the warehouse closest to the region where that warehouse is located is selected for delivery.
In the above constraint, the preset thresholds RN, FN, DN, and Per may be selectively set by the user. In the concrete implementation, the user can input the number of warehouses used by the user and the proportion of the order quantity which is expected to meet the aging requirement according to the requirement of the user. For example, the user may set the values of DN and Per, and the user may set the values of RN, FN and Per.
In the embodiment of the invention, the bin selection model is constructed in advance, and the objective function of the bin selection model is set as follows: the method has the advantages that the total cost required by the user for storing and distributing goods based on the selected warehouse is minimized, then the goods order prediction data of the user in the preset period is input into a pre-constructed warehouse selection model for processing and the like, the optimal warehouse selection scheme can be automatically planned for the user, the rationality of warehouse selection is improved, the goods storage and distribution cost is reduced, and the goods distribution timeliness is improved. Furthermore, personalized and scientific logistics service can be provided for users (such as merchants) by utilizing the self-warehouse resources.
Fig. 2 is a schematic diagram of the main flow of a bin selection method according to the second embodiment of the invention. As shown in fig. 2, the bin selection method in the embodiment of the invention includes:
step S201, acquiring the goods order history data of the user.
Wherein the user's cargo order history data may include: the user orders various items for delivery from the regional warehouses over a period of time (e.g., one day, one week, one month, etc.).
Step S202, processing the cargo order history data based on the trained LSTM model to obtain cargo order prediction data of a user in a preset period.
In an embodiment of the present invention, the first deep learning model is an LSTM (long short term memory network) model. The LSTM model is an RNN model that performs well in sequence model prediction, mainly including forget gates, input gates, and output gates.
Further, before step S202, the method according to the embodiment of the present invention further includes: and constructing a training data set based on the cargo order history data of the user, and training the LSTM model according to the training data set to obtain a trained LSTM model.
Next, when processing based on the trained LSTM model, we can predict the next-period (e.g., next day) of the cargo order amount using the cargo order amount in one period (e.g., one day), and then predict the next-period (e.g., next day) of the cargo order amount based on the predicted cargo order amount, so that the cargo order amount in a predetermined period (e.g., one month) in the future can be predicted. In the embodiment of the invention, the prediction accuracy of the goods order can be improved by adopting the LSTM model to predict the goods order.
And step 203, inputting the goods order prediction data into a pre-constructed warehouse selection model so as to defend the user from all available warehouses to select an optimal storage warehouse.
Further, the bin selection model satisfies the following constraint conditions: 1. the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; 2. the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input. By setting the constraint conditions, the requirements of users on order distribution timeliness and the number of selected storage warehouses can be met, and the flexibility of the warehouse selection model is improved.
In addition, in the concrete implementation, in order to improve the solving efficiency of the bin selection model, a branch-and-bound algorithm can be adopted for solving. The branch-and-bound method is a very widely used algorithm whose basic idea is to search all feasible solution spaces for constrained optimization problems. The algorithm, when executed in detail, continuously partitions the total feasible solution space into smaller and smaller subsets (called branches) and computes a lower or upper bound (called bounds) for the values of the solutions within each subset. After each branch, no further branches are made for those subsets where the limit exceeds the known feasible solution value. Thus, many subsets of solutions (i.e., many nodes on the search tree) may be disregarded, thereby reducing the scope of the search. This process continues until a viable solution is found whose value is not greater than the limits of any subset.
And step S204, returning the information of the optimal storage warehouse selected for the user to the user.
Illustratively, after the optimal storage warehouse of the user is obtained through steps S201 to S203, information of the optimal storage warehouse of the user may be sent to the front end, so as to perform visual display through a front end (such as a client) page, so that the user can learn the optimal bin selection scheme in time. In addition, in the implementation, the goods order prediction data obtained in the steps S201 to S202 may be further sent to the user, so that the user can know the sales prediction situation of the goods in time.
According to the embodiment of the invention, the cargo order historical data of the user is processed based on the trained LSTM model so as to obtain the cargo order prediction data of the user in the preset period, so that the prediction accuracy of the cargo order can be improved, and the follow-up bin selection effect can be improved; the optimal bin selection scheme can be automatically planned for the user by pre-constructing the bin selection model, setting the objective function of the bin selection model as 'the total cost required by the user for storing and delivering the goods based on the selected warehouse is minimized', inputting the goods order prediction data of the user in the preset period into the pre-constructed bin selection model for processing and the like, improving the rationality of warehouse selection, reducing the cost of storing and delivering the goods, and improving the goods delivery timeliness.
Fig. 3 is a schematic diagram of the main flow of the bin selection method according to the third embodiment of the invention. As shown in fig. 3, the bin selection method in the embodiment of the invention includes:
step S301, acquiring the goods order history data of the user.
Wherein the user's cargo order history data may include: the user orders various items for delivery from the regional warehouses over a period of time (e.g., one day, one week, one month, etc.).
Step S302, processing the goods order history data based on the trained first deep learning model to obtain goods order prediction data of the user in a preset period.
Illustratively, the first deep learning model includes: LSTM (long short term memory network) model. The LSTM model is an RNN model that performs well in sequence model prediction, mainly including forget gates, input gates, and output gates.
Further, before step S302, the method according to the embodiment of the present invention further includes: and constructing a training data set based on the cargo order history data of the user, and training the LSTM model according to the training data set to obtain a trained LSTM model.
When processing based on the trained LSTM model, we can learn and predict the next cycle (e.g., next day) of the cargo order volume with the cargo order volume in one cycle (e.g., one day), then learn and predict the next cycle (e.g., next day) of the cargo order volume based on the predicted cargo order volume, and then predict the cargo order volume in some preset period (e.g., one month) in the future. In the embodiment of the invention, the prediction accuracy of the goods order can be improved by adopting the trained LSTM model to predict the goods order.
And step S303, inputting the goods order prediction data into a pre-constructed warehouse selection module so as to select an optimal storage warehouse for the user from all available warehouses.
Further, the bin selection model satisfies at least one of the following constraints: 1. the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; 2. the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input. By setting the constraint conditions, the requirements of users on order distribution timeliness and the number of selected storage warehouses can be met, and the flexibility of the warehouse selection model is improved.
In addition, in the concrete implementation, in order to improve the solving efficiency of the bin selection model, a branch-and-bound algorithm can be adopted for solving. The branch-and-bound method is a very widely used algorithm whose basic idea is to search all feasible solution spaces for constrained optimization problems. The algorithm, when executed in detail, continuously partitions the total feasible solution space into smaller and smaller subsets (called branches) and computes a lower or upper bound (called bounds) for the values of the solutions within each subset. After each branch, no further branches are made for those subsets where the limit exceeds the known feasible solution value. Thus, many subsets of solutions (i.e., many nodes on the search tree) may be disregarded, thereby reducing the scope of the search. This process continues until a viable solution is found whose value is not greater than the limits of any subset.
And step S304, acquiring the goods order prediction data of the user in each optimal storage warehouse.
For example, the cargo order history data for each optimal storage warehouse may be processed based on the trained first deep learning model to obtain cargo order forecast data for that optimal storage warehouse.
And step S305, processing the goods order prediction data of the optimal storage warehouse based on the trained second deep learning module to obtain inventory prediction data of the user in the optimal storage warehouse.
Illustratively, the second deep learning model is a linear chain member random field model. Further, a Viterbi (Viterbi) algorithm may be employed to solve the stock forecast data for the user in the optimal storage warehouse as the stock forecast data for the optimal storage warehouse is processed based on the trained linear chain conditional random field model.
A conditional random field is a conditional probability distribution model that gives another set of output sequences given a set of input sequences. The linear chain member random field model is a conditional random field model that requires the input sequence to have the same structure as the output sequence. In the embodiment of the invention, the input sequence of the linear chain conditional random field model is a sales sequence constructed based on the goods order prediction data: x= (x 1,x2,…xn), the output sequence is the stock sequence: y= (y 1,y2,…yn), the predictive problem of the linear chain random field model in the embodiment of the invention is: solving the problem of inventory sequence y with the maximum conditional probability.
In particular, the linear chain conditional random field model can be expressed as the inner product of vector w and vector F (y, x):
Wherein ,w＝(w1,w2,…wK),F(y,x)＝(f1(y,x),f2(y,x),…fK(y,x))T,wk(k＝1,2,…K) is the weight corresponding to the feature function, and f k (y, x) (k=1, 2, … K) is the feature function of the inventory and sales variables.
Further, the problem of predicting a conditional random field can be converted into a problem of maximizing the output sequence of non-normalized probabilities, which can be expressed as:
Thereby obtaining the product,
Wherein the method comprises the steps of ,Fi(yi-1,yi,x)＝(f1(yi-1,yi,x,i),f2(yi-1,yi,x,i),…fK(yi-1,yi,x,i))T,w＝(w1,w2,…wK).
Further, solving inventory forecast data of the user in the optimal storage warehouse based on the Viterbi algorithm mainly comprises the following steps:
1. initializing non-normalized probabilities of respective inventory labels:
δ1(l)＝w·F1(y0＝start,y1＝l,x)l＝1,2,…m (14)
2. for i=2, 3, … n, the maximum value δ i (l) of the non-normalized probabilities for each inventory annotation is recursively calculated and the sequence of probability maxima ψ i (l) is recorded:
3. Ending when i=n is calculated, the end point of the maximum probability sequence is:
4. Output maximum probability inventory sequence y *:
And step S306, returning the optimal storage warehouse selected for the user and the inventory prediction data of each optimal storage warehouse to the user.
The method includes the steps that after the optimal storage warehouse of the user and the inventory prediction data of the optimal storage warehouse are obtained through the steps, information of the optimal storage warehouse of the user and the inventory prediction data of each optimal storage warehouse can be sent to the front end, visual display is conducted through a front end (such as a client) page, and the user can know the optimal warehouse selection scheme and the inventory prediction condition of the optimal warehouse in time conveniently.
In the embodiment of the invention, the bin selection model is constructed in advance, and the objective function of the bin selection model is set as follows: the method has the advantages that the total cost required by the user for storing and distributing goods based on the selected warehouse is minimized, then the goods order prediction data of the user in the preset period is input into a pre-constructed warehouse selection model for processing and the like, the optimal warehouse selection scheme can be automatically planned for the user, the rationality of warehouse selection is improved, the goods storage and distribution cost is reduced, and the goods distribution timeliness is improved; further, the goods sales quantity condition is predicted through the trained first deep learning model, the optimal storage warehouse of the user is determined through the bin selection model, and the goods inventory condition in the optimal storage warehouse is predicted through the trained second deep learning model, so that the user can conveniently and reasonably layout goods.
Fig. 4 is a schematic view of the main modules of a cartridge selection device according to a fourth embodiment of the invention. As shown in fig. 4, the bin selection device 400 according to the embodiment of the present invention includes: a determining module 401 and a selecting module 402.
The determining module 401 is configured to process the cargo order history data of the user based on the trained first deep learning model, so as to obtain cargo order prediction data of the user within a preset period.
In an alternative embodiment, the determining module 401 may acquire the cargo order history data of the user in real time, and process the cargo order history data in real time based on the trained first deep learning model, so as to obtain cargo order prediction data of the user in a preset period. Illustratively, the preset period of time may be one month, half year, one year, or the like.
In another alternative embodiment, the cargo order history data of each user may be obtained in advance, the cargo order history data is processed based on the trained first deep learning model, and then the cargo order prediction data of each user obtained by processing is stored in a database. When the bin selection method of the embodiment of the invention is executed, the determination module 401 can directly query the database to obtain the predicted data of the cargo order of the user in the preset period.
In addition, in the implementation, in addition to the data for predicting the order of the goods, data required for calculation such as available warehouse collection, unit price for storing the goods, unit price for delivering and transporting the goods, distance for transporting the goods, and volume of the goods are determined. The determined data of the forecast order of the goods and the data required by other operations of the user in the preset time period are input into the warehouse selecting model.
A selection module 402, configured to input the cargo order forecast data into a pre-constructed warehouse selection model, so as to select an optimal storage warehouse for the user from all available warehouses. Wherein, the objective function of the bin selection model is as follows: the overall cost required by the user to store and distribute goods based on the warehouse selected is minimized.
Further, the bin selection model satisfies at least one of the following constraints: 1. the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; 2. the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input. By setting the constraint conditions, the requirements of users on order distribution timeliness and the number of selected storage warehouses can be met, and the flexibility of the warehouse selection model is improved.
In addition, in the specific implementation, in order to improve the solving efficiency of the bin selection model, the selecting module 402 may adopt a branch-and-bound algorithm to solve. The branch-and-bound method is a very widely used algorithm whose basic idea is to search all feasible solution spaces for constrained optimization problems. The algorithm, when executed in detail, continuously partitions the total feasible solution space into smaller and smaller subsets (called branches) and computes a lower or upper bound (called bounds) for the values of the solutions within each subset. After each branch, no further branches are made for those subsets where the limit exceeds the known feasible solution value. Thus, many subsets of solutions (i.e., many nodes on the search tree) may be disregarded, thereby reducing the scope of the search. This process continues until a viable solution is found whose value is not greater than the limits of any subset.
In the device provided by the embodiment of the invention, the optimal bin selection scheme can be automatically planned for the user by pre-constructing the bin selection model, setting the objective function of the bin selection model as 'the total cost required by the user for storing and distributing goods based on the selected warehouse is minimum', determining the goods order prediction data of the user in the preset period through the determining module, inputting the goods order prediction data into the pre-constructed bin selection model through the selecting module for processing, improving the rationality of warehouse selection, reducing the goods storage and distribution cost and improving the goods distribution timeliness. Furthermore, personalized and scientific logistics service can be provided for users (such as merchants) by utilizing the self-warehouse resources.
Fig. 5 is a schematic view of the main modules of a cartridge selection device according to a fifth embodiment of the invention. As shown in fig. 5, the bin selection device 500 of the embodiment of the present invention includes: a determining module 501, a selecting module 502, an inventory predicting module 503 and a sending module 504.
The determining module 501 is configured to obtain cargo order history data of a user, and process the cargo order history data based on a trained first deep learning model to obtain cargo order prediction data of the user within a preset period.
Wherein the user's cargo order history data may include: the user's order amount of various goods distributed by the various regional warehouses over a period of time (e.g., one day, one week, one month, etc.); the goods order forecast data of the user within the preset time period can comprise: the user orders various items for distribution by the regional warehouses over a future period of time.
Illustratively, the first deep learning model includes: LSTM (long short term memory network) model. The LSTM model is an RNN model that performs well in sequence model prediction, mainly including forget gates, input gates, and output gates.
When processing based on the trained LSTM model, we can learn and predict the next cycle (e.g., next day) of the cargo order volume with the cargo order volume in one cycle (e.g., one day), then learn and predict the next cycle (e.g., next day) of the cargo order volume based on the predicted cargo order volume, and then predict the cargo order volume in some preset period (e.g., one month) in the future. In the embodiment of the invention, the cargo order is predicted by adopting the trained LSTM model, so that the prediction accuracy can be improved.
A selection module 502, configured to input the cargo order prediction data into a pre-constructed warehouse selection model, so as to select an optimal storage warehouse for the user from all available warehouses. Wherein, the objective function of the bin selection model is as follows: the overall cost required by the user to store and distribute goods based on the warehouse selected is minimized.
Further, the bin selection model satisfies at least one of the following constraints: 1. the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; 2. the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input. By setting the constraint conditions, the requirements of users on order distribution timeliness and the number of selected storage warehouses can be met, and the flexibility of the warehouse selection model is improved. In addition, in the concrete implementation, in order to improve the solving efficiency of the bin selection model, a branch-and-bound algorithm can be adopted for solving.
And the inventory prediction module 503 is configured to obtain the inventory prediction data of the user in each optimal storage warehouse, and process the inventory prediction data of the optimal storage warehouse based on the trained second deep learning model, so as to obtain the inventory prediction data of the user in the optimal storage warehouse.
Illustratively, the second deep learning model is a linear chain member random field model. Further, a Viterbi (Viterbi) algorithm may be employed to solve the stock forecast data for the user in the optimal storage warehouse as the stock forecast data for the optimal storage warehouse is processed based on the trained linear chain conditional random field model.
And the sending module 504 is configured to return the optimal storage warehouse selected for the user and inventory prediction data of each optimal storage warehouse to the user.
The information of the optimal storage warehouse of the user and the inventory prediction data of each optimal storage warehouse can be sent to the front end after the optimal storage warehouse of the user and the inventory prediction data of the optimal storage warehouse are obtained, so that visual display can be performed through a front end (such as a client) page, and the user can know the optimal warehouse scheme and the inventory prediction condition of the optimal warehouse in time conveniently.
In the device provided by the embodiment of the invention, the optimal bin selection scheme can be automatically planned for the user by pre-constructing the bin selection model, setting the objective function of the bin selection model as 'minimizing the total cost required by the user for storing and distributing goods based on the selected warehouse', determining the goods order prediction data of the user in the preset period through the determining module, inputting the goods order prediction data into the pre-constructed bin selection model through the selecting module for processing, improving the rationality of warehouse selection, reducing the goods storage and distribution cost and improving the goods distribution timeliness; further, the goods sales quantity condition is predicted through the trained first deep learning model, the optimal storage warehouse of the user is determined through the bin selection model, and the goods inventory condition in the optimal storage warehouse is predicted through the trained second deep learning model, so that the user can conveniently and reasonably layout goods.
Fig. 6 shows an exemplary system architecture 600 in which the method of selecting bins or the device of selecting bins of embodiments of the invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 is used as a medium to provide communication links between the terminal devices 601, 602, 603 and the server 605. The network 604 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 605 via the network 604 using the terminal devices 601, 602, 603 to receive or send messages, etc. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 601, 602, 603.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server providing support for a cargo storage and delivery service client or cargo storage and delivery service website that a user browses using the terminal devices 601, 602, 603. The background management server can analyze and process the received data such as the bin selection request and the like, and feed back the processing result (such as the selected optimal storage bin) to the terminal equipment.
It should be noted that, the bin selection method provided in the embodiment of the present invention is generally executed by the server 605, and accordingly, the bin selection device is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, there is illustrated a schematic diagram of a computer system 700 suitable for use in implementing an electronic device of an embodiment of the present invention. The computer system shown in fig. 7 is only an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the system 700 are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 701.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes a determination module and a selection module. Wherein the names of the modules do not constitute a limitation of the module itself in some cases, for example, the determination module may also be described as "module for determining the forecast data of the order of goods".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer-readable medium carries one or more programs which, when executed by one of the devices, cause the device to perform the following: processing the cargo order history data of the user based on the trained first deep learning model to obtain cargo order prediction data of the user in a preset period; inputting the goods order prediction data into a pre-constructed warehouse selection model to select an optimal storage warehouse for the user from all available warehouses; wherein, the objective function of the bin selection model is as follows: the overall cost required by the user to store and distribute goods based on the warehouse selected is minimized.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (7)
1. A method of selecting bins, the method comprising:
Processing the cargo order history data of the user based on the trained first deep learning model to obtain cargo order prediction data of the user in a preset period;
Inputting the goods order prediction data into a pre-constructed warehouse selection model to select an optimal storage warehouse for the user from all available warehouses; wherein, the objective function of the bin selection model is as follows: minimizing the total cost required by the user for goods storage and distribution based on the selected warehouse; the bin selection model meets at least one of the following constraints: the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input; the bin selection model is solved by adopting a branch-and-bound algorithm;
acquiring the goods order forecast data of the user in each optimal storage warehouse, and processing the goods order forecast data of the optimal storage warehouse based on the trained second deep learning model to acquire the inventory forecast data of the user in the optimal storage warehouse; the second deep learning model is a linear chain member random field model, and the prediction problem of the linear chain conditional random field model is as follows: solving the problem of stock sequence with maximum conditional probability.
2. The method of claim 1, wherein the first deep learning model comprises: LSTM model.
3. The method according to any one of claims 1 to 2, further comprising:
After the step of inputting the cargo order forecast data into a pre-built warehouse selection model to select an optimal storage warehouse for the user from all available warehouses is performed, information of the optimal storage warehouse selected for the user is returned to the user.
4. The method of claim 1, wherein the step of processing the cargo order forecast data for the optimal storage warehouse based on the trained second deep learning model is performed using a viterbi algorithm.
5. A cartridge selection device, the device comprising:
The determining module is used for processing the goods order history data of the user based on the trained first deep learning model so as to obtain goods order prediction data of the user in a preset period;
the selection module is used for inputting the goods order prediction data into a pre-constructed warehouse selection model so as to select an optimal storage warehouse for the user from all available warehouses; wherein, the objective function of the bin selection model is as follows: minimizing the total cost required by the user for goods storage and distribution based on the selected warehouse;
The bin selection model meets at least one of the following constraints: the ratio of the order quantity of the goods which can meet the requirement of the delivery time effect to the total order quantity is larger than or equal to a first threshold value; the total number of the selected storage warehouses is smaller than or equal to a second threshold value; wherein the first threshold and/or the second threshold are preset according to user input; the bin selection model is solved by adopting a branch-and-bound algorithm;
the inventory prediction module is used for acquiring the goods order prediction data of the user in each optimal storage warehouse, and processing the goods order prediction data of the optimal storage warehouse based on the trained second deep learning model so as to acquire inventory prediction data of the user in the optimal storage warehouse; the second deep learning model is a linear chain member random field model, and the prediction problem of the linear chain conditional random field model is as follows: solving the problem of stock sequence with maximum conditional probability.
6. An electronic device, comprising:
one or more processors;
Storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1 to 4.
7. A computer readable medium on which a computer program is stored, characterized in that the program, when executed by a processor, implements the method according to any one of claims 1 to 4.
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| CN113077218A (en) * | 2021-04-16 | 2021-07-06 | 北京京东振世信息技术有限公司 | Storage network planning method and device, readable storage medium and electronic equipment |
| CN115907598A (en) * | 2022-11-07 | 2023-04-04 | 杭州巨灵兽智能科技有限公司 | Supply chain transportation network planning method, device, equipment and storage medium |
| CN115760305B (en) * | 2022-11-30 | 2023-09-01 | 中国外运股份有限公司 | Delivery method and system for intelligent multiple bins of electric business |
| CN115965140A (en) * | 2022-12-27 | 2023-04-14 | 北京航天智造科技发展有限公司 | Inventory optimal planning method, system, equipment and storage medium |
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