Order Information Processing Method, Device, Computer Equipment and Medium

Abstract

The present disclosure provides an order information processing method, including: obtaining order information, where the order information includes information on a specified address and information on at least one specified item. Obtaining warehouse information, where the warehouse information includes inventory information of multiple warehouses and distribution information of multiple warehouses. Then, using a pre-constructed optimization model to process the above order information and warehouse information, to determine at least one warehouse from the above multiple warehouses as a distribution warehouse according to the output result of the optimization model, so that the sum of a first value and a second value is less than or equal to a predetermined value. Wherein, the first value is used to characterize the distribution duration spent on delivering at least one specified item from the above distribution warehouse to the specified address, and the second value is used to characterize the distribution cost spent on delivering at least one specified item from the above distribution warehouse to the specified address. The present disclosure also provides an order information processing device, a computer device, and a medium.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of computer technology, and more particularly, to an order information processing method, apparatus, computer device, and medium. Background Art

[0002] With the rapid development of Internet technology, e-commerce has risen rapidly. Various e-commerce platforms provide a variety of online commodity trading channels, which greatly facilitates people's work and life.

[0003] E-commerce fulfillment refers to the whole process from the generation of an order to the user receiving the ordered item. Generally, a merchant will set up several distribution centers within or around the served area. Multiple warehouses may be set up under each distribution center to store items to be sold. Fulfillment decision-making refers to, for each order, determining one or more warehouses from multiple candidate warehouses as the actual fulfillment warehouses, also known as distribution warehouses, and delivering the specified items in the order from the determined distribution warehouses to the specified delivery address of the order. The locations and inventory levels of different warehouses may vary, and moreover, the warehousing and distribution costs of different warehouses also differ. Therefore, the result of fulfillment decision-making will directly affect the delivery duration and delivery cost, thus affecting the user's shopping experience and the merchant's fulfillment cost. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an order information processing method, apparatus, computer device, and medium to further optimize the e-commerce fulfillment decision-making scheme and determine a distribution warehouse that better meets the actual needs for an order.

[0005] One aspect of embodiments of the present invention provides an order information processing method, including: obtaining order information, where the order information includes information of a specified address and information of at least one specified item. Obtaining warehouse information, where the warehouse information includes inventory information of multiple warehouses and distribution information of multiple warehouses. Then, using a pre-constructed optimization model to process the above order information and warehouse information, and determining at least one warehouse from the above multiple warehouses as the distribution warehouse according to the output result of the optimization model, so that the sum of a first value and a second value is less than or equal to a predetermined value. Wherein, the first value is used to represent the delivery duration for delivering at least one specified item from the above distribution warehouse to the specified address, and the second value is used to represent the delivery cost for delivering at least one specified item from the above distribution warehouse to the specified address.

[0006] According to an embodiment of the present invention, the information of each designated item among the above at least one designated item includes: the identification information of each designated item and the required quantity of each designated item. The inventory information of each of the above multiple warehouses includes: the identification information of the items stored in each warehouse and the storage quantity of each item in each warehouse. And, the distribution information of each of the above multiple warehouses includes: the expected delivery duration for multiple addresses of each warehouse and the expected delivery cost of each warehouse.

[0007] According to an embodiment of the present invention, the optimization model includes a first sub-model. The above-mentioned processing of the order information and warehouse information by using the pre-constructed optimization model includes: using the first sub-model to perform the following operations: when there is at least one first-category item among the above at least one designated item, for each first-category item, according to the identification information and required quantity of the first-category item, determine from the above multiple warehouses the first candidate warehouses that store the first-category item and the storage quantity is greater than or equal to the required quantity, and determine from the first candidate warehouses the first candidate warehouse with the shortest expected delivery duration for the designated address as the pending warehouse for the first-category item. Take the pending warehouses of each of the above at least one first-category item as the output result of the first sub-model.

[0008] According to an embodiment of the present invention, the above-mentioned determining at least one warehouse as the distribution warehouse from the multiple warehouses according to the output result of the optimization model includes: when the pending warehouses of each of the above at least one first-category item are the same warehouse, determine the pending warehouses of each of the above at least one first-category item as the distribution warehouses of each of the above at least one first-category item.

[0009] According to an embodiment of the present invention, the optimization model further includes a second sub-model, the second sub-model is an integer programming model, the objective function of the integer programming model represents the sum of a first value and a second value, and the integer programming model includes at least one constraint condition. The above-mentioned processing of the order information and warehouse information by using the pre-constructed optimization model further includes: when the pending warehouses of each of the above at least one first-category item are not the same warehouse, based on the above at least one constraint condition, the above order information and the above warehouse information, determine at least one warehouse allocation method, where each warehouse allocation method includes: the distribution relationship between each of the above at least one first-category item and at least one of the above multiple warehouses. Based on the above at least one warehouse allocation method, determine the value ranges of the first value and the second value. Then, based on the value ranges of the first value and the second value, calculate the value of the objective function. Next, take the warehouse allocation method that makes the value of the objective function the smallest as the output result of the second sub-model.

[0010] According to an embodiment of the present invention, determining at least one warehouse as a distribution warehouse from multiple warehouses according to the output result of the optimization model further includes: determining whether the running duration of the second sub-model before obtaining the output result is greater than a predetermined duration. If so, determining the to-be-determined warehouse of each of the at least one first-category item as the distribution warehouse of each of the at least one first-category item. If not, determining the distribution warehouse of each of the at least one first-category item according to the output result of the second sub-model.

[0011] According to an embodiment of the present invention, each of the above warehouse allocation methods includes: the distribution relationship between the at least one first-category item and M warehouses, where M is an integer greater than or equal to 1. Determining the value ranges of the first value and the second value based on at least one warehouse allocation method includes: for each warehouse allocation method, determining the first value for each warehouse allocation method according to the expected delivery duration of each of the M warehouses for a specified address. And determining the second value for each warehouse allocation method according to the expected delivery cost of each of the M warehouses.

[0012] According to an embodiment of the present invention, the at least one constraint condition is used to limit at least one of the following: the number of distribution warehouses of each first-category item; the storage quantity of the first-category item in the distribution warehouse of each first-category item; and the number of items of the first-category item distributed by each warehouse.

[0013] According to an embodiment of the present invention, the optimization model further includes a third sub-model. Processing the order information and warehouse information by using the pre-constructed optimization model further includes: using the third sub-model to perform the following operations: when there is at least one second-category item among the at least one specified item, for each second-category item, determining, according to the identification information and demand quantity of the second-category item, a second candidate warehouse that stores the second-category item and has a storage quantity greater than or equal to the demand quantity from the multiple warehouses, and determining, from the second candidate warehouses, the second candidate warehouse with the shortest expected delivery duration for a specified address as the distribution warehouse of the second-category item. Taking the distribution warehouses of each of the at least one second-category item as the output result of the third sub-model.

[0014] According to an embodiment of the present invention, determining at least one warehouse as a distribution warehouse from multiple warehouses according to the output result of the optimization model further includes: determining the distribution warehouses of each of the at least one second-category item according to the output result of the third sub-model.

[0015] Another aspect of the embodiments of the present invention provides an order information processing device, including: a first acquisition module, a second acquisition module, and a model processing module. The first acquisition module is configured to acquire order information, which includes: information of a specified address and information of at least one specified item. The second acquisition module is configured to acquire warehouse information, which includes: inventory information of multiple warehouses and distribution information of multiple warehouses. Then, the model processing module is configured to process the above-mentioned order information and warehouse information by using a pre-constructed optimization model, so as to determine at least one warehouse from the above-mentioned multiple warehouses as a distribution warehouse according to the output result of the optimization model, so that the sum of a first value and a second value is less than or equal to a predetermined value. Wherein, the first value is used to represent the distribution duration spent on delivering at least one specified item from the above-mentioned distribution warehouse to the specified address, and the second value is used to represent the distribution cost spent on delivering at least one specified item from the above-mentioned distribution warehouse to the specified address.

[0016] Another aspect of the embodiments of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned method is implemented.

[0017] Another aspect of the embodiments of the present invention provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above-mentioned method when executed.

[0018] Another aspect of the embodiments of the present invention provides a computer program, which includes computer-executable instructions, and the instructions are used to implement the above-mentioned method when executed.

[0019] According to the embodiments of the present invention, a pre-constructed optimization model is used to make fulfillment decisions for orders. Using order information and warehouse information as input features of the optimization model, a distribution warehouse for at least one specified item indicated by the order information is determined according to the output result of the optimization model. The fulfillment decision result of the optimization model can comprehensively weigh the distribution duration and the distribution cost, so that when all the specified items indicated by the order information are delivered from the determined one or more distribution warehouses to the specified indication, the sum of the first value representing the distribution duration and the second value representing the distribution cost is optimized as much as possible to be less than or equal to a predetermined value. Thereby improving the user experience and fulfillment cost and meeting the needs of both users and merchants. Description of the Drawings

[0020] Through the following description of the embodiments of the present invention with reference to the drawings, the above-mentioned and other objects, features, and advantages of the embodiments of the present invention will become clearer. In the drawings:

[0021] Figure 1Schematically shows an exemplary system architecture of an application order information processing method and apparatus according to an embodiment of the present invention;

[0022] Figure 2 Schematically shows a flowchart of an order information processing method according to an embodiment of the present invention;

[0023] Figure 3 Schematically shows an example flowchart of an order information processing method according to another embodiment of the present invention;

[0024] Figure 4 Schematically shows an example flowchart of an order information processing method according to another embodiment of the present invention;

[0025] Figure 5 Schematically shows a block diagram of an order information processing apparatus according to an embodiment of the present invention; and

[0026] Figure 6 Schematically shows a block diagram of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0027] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the embodiments of the present invention. In the following detailed description, for the sake of explanation, many specific details are set forth in order to provide a comprehensive understanding of the embodiments of the present invention. However, it is obvious that one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the embodiments of the present invention.

[0028] The terms used herein are merely for describing specific embodiments and are not intended to limit the embodiments of the present invention. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0029] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0030] In the case of using expressions such as "at least one of A, B, and C, etc.", generally, it should be interpreted according to the meaning that those skilled in the art usually understand this expression (for example, "a system having at least one of A, B, and C" should include but not be limited to a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", generally, it should be interpreted according to the meaning that those skilled in the art usually understand this expression (for example, "a system having at least one of A, B, or C" should include but not be limited to a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0031] Embodiments of the present invention provide an order information processing method, apparatus, computer device, and medium. Among them, the order information processing method may include a first acquisition process, a second acquisition process, and a model processing process. In the first acquisition process, order information is acquired, and the order information includes: information of a specified address and information of at least one specified item. In the second acquisition process, warehouse information is acquired, and the warehouse information includes: inventory information of multiple warehouses and distribution information of multiple warehouses. Then, in the model processing process, the above-mentioned order information and warehouse information are processed using a pre-constructed optimization model to determine at least one warehouse from the above-mentioned multiple warehouses as the distribution warehouse according to the output result of the optimization model, so that the sum of a first value and a second value is less than or equal to a predetermined value. Wherein, the first value is used to represent the distribution duration spent on distributing at least one specified item from the above-mentioned distribution warehouse to the specified address, and the second value is used to represent the distribution cost spent on distributing at least one specified item from the above-mentioned distribution warehouse to the specified address.

[0032] With the rapid development of Internet technology, e-commerce has risen rapidly. Various e-commerce platforms provide a variety of online commodity trading channels, greatly facilitating people's work and life. E-commerce fulfillment refers to the entire process from the generation of an order to the user receiving the ordered item. Merchants generally set up several distribution centers within or around the areas they serve. Multiple warehouses may be set up under each distribution center to store items to be sold. Fulfillment decision-making refers to, for each order, determining one or more warehouses from multiple candidate warehouses as the actual fulfillment warehouses, also known as distribution warehouses, and delivering the specified items in the order from the determined distribution warehouses to the specified delivery address of the order. On the one hand, since the locations and inventory levels of different warehouses may vary, the selection of the distribution warehouse will directly affect the user's receipt time, that is, affect the delivery duration. On the other hand, for the same order, the selection of the distribution warehouse will also affect the delivery cost. For example, multiple items shipped from the same warehouse can be combined into one package, thus reducing logistics costs. In addition, there are differences in the warehousing and distribution costs of different warehouses. Therefore, the result of fulfillment decision-making will directly affect the delivery duration and delivery cost, thus affecting the user's shopping experience and the merchant's fulfillment cost.

[0033] Existing fulfillment decision-making schemes are mainly based on preset rules. The priorities of warehouses are preset according to the specified delivery address of the user or the categories of items purchased. When making a fulfillment decision for the items in an order, warehouses with higher priorities are considered first. When an order contains multiple items, warehouses that can meet all the items in the order are given priority in fulfillment decision-making. If no warehouse that meets the above conditions is found, based on the preset order-splitting algorithm, the order is split into several sub-orders, each sub-order containing a part of the original order's goods, and then fulfillment decisions are made for each sub-order separately.

[0034] The above schemes cannot comprehensively balance the user experience and fulfillment cost. The priorities of each warehouse are preset and do not fully reflect the length of the delivery duration. Even if a warehouse with a high priority is selected, it is not necessarily the warehouse with the shortest delivery duration, and the delivery cost is not taken into account. For example, under the current fulfillment decision-making method, for an order containing multiple items, when there is exactly one warehouse that can provide all the items in the order, that warehouse will be selected as the distribution warehouse for all the items in the order, without considering whether the delivery duration of this warehouse for the specified delivery address of the order is too long. Once the delivery duration of this warehouse is too long, it will lead to a very poor user experience for this order. And when no warehouse that can provide all the items in the order is found, the existing order-splitting algorithm cannot ensure the balance and optimization of the delivery duration and delivery cost.

[0035] According to an embodiment of the present invention, there is provided a method and apparatus for processing order information to make fulfillment decisions and determine at least one warehouse as a distribution warehouse from multiple warehouses. The order information processing method according to the embodiment of the present invention can comprehensively balance the distribution duration and distribution cost during the fulfillment decision-making process, not only improving the user's shopping experience but also minimizing the merchant's fulfillment cost as much as possible.

[0036] Figure 1 Schematically shown is an exemplary system architecture 100 to which the order information processing method and apparatus according to the embodiment of the present invention can be applied. It should be noted that Figure 1 The illustration is only an example of the system architecture to which the embodiment of the present invention can be applied to help those skilled in the art understand the technical content of the embodiment of the present invention, but it does not mean that the embodiment of the present invention cannot be used in other devices, systems, environments or scenarios.

[0037] As Figure 1 shown, the system architecture 100 according to the embodiment of the present invention may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0038] The terminal devices 101, 102, 103 communicate with the server 105 through the network 104 to receive or send messages, etc. Various client applications with various functions can be installed on the terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0039] The terminal devices 101, 102, 103 can be various electronic devices, including but not limited to in-vehicle navigation, smartphones, tablets, laptop portable computers, and desktop computers, etc.

[0040] The server 105 can be a server that provides various services, such as a background management server that supports various client applications in the terminal devices 101, 102, 103. The background management server can receive the request messages sent by the terminal devices 101, 102, 103, analyze and process the received request messages for response, and feedback the response results for the request messages (such as web pages, information, or data obtained or processed and generated according to the request messages) to the terminal devices 101, 102, 103, and the terminal devices 101, 102, 103 output these response results to the user.

[0041] It should be noted that the order information processing method according to the embodiments of the present invention can be implemented in the terminal devices 101, 102, and 103. Correspondingly, the order information processing device according to the embodiments of the present invention can be disposed in the terminal devices 101, 102, and 103. Alternatively, the order information processing method according to the embodiments of the present invention can also be implemented in the server 105. Correspondingly, the order information processing device according to the embodiments of the present invention can be disposed in the server 105. Alternatively, the order information processing method according to the embodiments of the present invention can also be implemented in other computer devices capable of communicating with the terminal devices 101, 102, 103 and / or the server 105. Correspondingly, the order information processing device according to the embodiments of the present invention can be disposed in other computer devices capable of communicating with the terminal devices 101, 102, 103 and / or the server 105.

[0042] It should be understood that Figure 1 the number and type of the terminal devices, networks, and servers in

[0043] According to an embodiment of the present invention, an order information processing method is provided. The following provides an exemplary description of this method through diagrams. It should be noted that the serial numbers of the respective operations in the following method are only used for representing the operations for description purposes, and should not be regarded as indicating the execution order of the respective operations. Unless explicitly stated, this method does not need to be executed exactly in the order shown.

[0044] Figure 2 The flowchart of the order information processing method according to an embodiment of the present invention is schematically shown.

[0045] As Figure 2 shown, this method may include operation S210 to operation S230.

[0046] In operation S210, order information is obtained.

[0047] Among them, the order information includes: information of a specified address and information of at least one specified item.

[0048] In operation S220, warehouse information is obtained.

[0049] Among them, the warehouse information includes: inventory information of multiple warehouses and distribution information of multiple warehouses.

[0050] Then, in operation S230, the above order information and warehouse information are processed using a pre-constructed optimization model to determine at least one warehouse from the above multiple warehouses as the distribution warehouse according to the output result of the optimization model.

[0051] According to an embodiment of the present disclosure, for example, the delivery duration for delivering at least one specified item indicated by the above order information from the above delivery warehouse to the specified address is characterized by a first numerical value. The delivery duration may be the time length from the order generation to the full delivery of the specified items indicated by the order information to the specified address, or the delivery duration may be the time length from the start of delivery to the full delivery of the specified items indicated by the order information to the specified address. The delivery cost for delivering at least one specified item indicated by the above order information from the above delivery warehouse to the specified address is characterized by a second numerical value. The delivery cost represents the cost that the merchant needs to spend during the delivery process. For example, it may include one or more of the transportation costs, package packing costs, and labor costs during the delivery process for all the specified items indicated by the order information. The above delivery warehouse determined from multiple warehouses according to the output result of the optimization model can make the sum of the first numerical value and the second numerical value less than or equal to a predetermined value.

[0052] Those skilled in the art can understand that the order information processing method according to the embodiment of the present invention uses a pre-constructed optimization model to make fulfillment decisions for orders. Using order information and warehouse information as input features of the optimization model, the delivery warehouse for at least one specified item indicated by the order information is determined according to the output result of the optimization model. The fulfillment decision result of the optimization model can comprehensively balance the delivery duration and the delivery cost, so that when all the specified items indicated by the order information are delivered from the determined one or more delivery warehouses to the specified address, the sum of the first numerical value representing the delivery duration and the second numerical value representing the delivery cost is optimized to be less than or equal to a predetermined value as much as possible. Thereby improving the user experience and fulfillment cost and meeting the needs of both users and merchants.

[0053] According to an embodiment of the present invention, the information of each specified item in the above at least one specified item may include: the identification information of each specified item and the required quantity of each specified item. The inventory information of each warehouse in the above multiple warehouses may include: the identification information of the items stored in each warehouse and the storage quantity of each item in each warehouse. And, the delivery information of each warehouse in the above multiple warehouses may include: the expected delivery duration of each warehouse for multiple addresses and the expected delivery cost of each warehouse.

[0054] For example, the specified item or the identification information of the item may be the SKU (Stock Keeping Unit) number. The expected delivery duration of a warehouse for an address can characterize the expected duration required to deliver the item from the warehouse to the address. The expected delivery cost of a warehouse can characterize the expected cost required for the warehouse to deliver a package, where a package can be obtained by packing one or more items, and each item can include one or more pieces. Exemplarily, the expected delivery duration of each warehouse for any address can be obtained by statistically analyzing the historical delivery duration data of the warehouse for the address, or predicted based on the historical delivery duration data of the warehouse for other addresses near the address; the expected delivery cost of each warehouse can also be obtained by statistically analyzing the historical delivery cost data. According to another embodiment of the present disclosure, the warehouse information may further include the identification information of each of the multiple warehouses.

[0055] Figure 3 Schematically shows an example flowchart of an order information processing method according to another embodiment of the present invention, which is used to illustrate an example implementation process of the above operation S230 for processing the above order information and warehouse information by using a pre-constructed optimization model, and determining at least one warehouse from the above multiple warehouses as the delivery warehouse according to the output result of the optimization model.

[0056] As Figure 3 shown, after starting to execute, the method may include operations S231 to S235.

[0057] In operation S231, it is determined whether there is at least one first-category item among at least one specified item indicated by the order information. If so, operation S232 is executed. If not, the process returns to the start state.

[0058] Exemplarily, the first-category item can be set as needed. For example, the first-category item may refer to all items that need to be packed, which can be called "non-original package items", that is, items that are not directly delivered according to the original packaging. If there is a first-category item among at least one specified item indicated by the order information, the possibility that part or all of the at least one specified item is combined and packed needs to be considered during the performance decision-making process.

[0059] In operation S232, for each first-category item, according to the identification information and the required quantity of the first-category item, a first candidate warehouse that stores the first-category item and has a storage quantity greater than or equal to the required quantity is determined from the above multiple warehouses.

[0060] Taking any first-category item A as an example, an exemplary description is given below. The order information includes the identification information "A" of the first-category item A and the required quantity M of the first-category item A. For the first-category item A, one or more first candidate warehouses for the first-category item A are determined from multiple warehouses. Each of the determined first candidate warehouses stores the first-category item A, and the storage quantity of the first-category item A stored in each first candidate warehouse is greater than or equal to the required quantity M of the first-category item A indicated in the above order information. The process of determining the first candidate warehouses may be, for example, to perform a matching search in the inventory information of multiple warehouses according to the identification information "A", and determine the warehouses whose inventory information contains the identification information "A". Then, perform a matching search in the inventory information containing the identification information "A" according to the required quantity M, and determine the warehouses whose storage quantity corresponding to the identification information "A" is greater than or equal to M as the first candidate warehouses for the first-category item A.

[0061] In operation S233, a first candidate warehouse with the shortest expected delivery duration for a specified address is determined from the above first candidate warehouses as the pending warehouse for the first-category item.

[0062] Exemplarily, in this operation S233, the expected delivery durations of the respective first candidate warehouses for the specified address indicated in the order information are determined according to the respective delivery information of the first candidate warehouses determined above. The first candidate warehouse with the shortest expected delivery duration for the specified address is used as the pending warehouse for the first-category item. For example, the order information indicates the specified address L and the first-category item A. By operating S232, the first candidate warehouses for the first-category item A are determined to include warehouses D1, D2, and D3. According to the respective delivery information of warehouses D1, D2, and D3, it is known that the expected delivery duration of warehouse D1 for the specified address L is t1, the expected delivery duration of warehouse D2 for the specified address L is t2, and the expected delivery duration of warehouse D3 for the specified address L is t3. If t2 < t1 < t3, then warehouse D2 is determined as the pending warehouse for the first-category item A. Similarly, when the order information also indicates other first-category items, the pending warehouses for each first-category item can be determined according to the above logic.

[0063] According to an embodiment of the present invention, the optimization model may include a first sub-model. The above operations S231 to S233 can all be executed using the first sub-model, and the respective pending warehouses of the above at least one first-category item are used as the output results of the first sub-model.

[0064] In operation S234, it is determined whether the respective pending warehouses of the above at least one first-category item are the same warehouse. If so, operation S235 is executed.

[0065] In operation S235, determine that the to-be-determined warehouse for each of the at least one first-category item is the distribution warehouse for each of the at least one first-category item.

[0066] Exemplarily, when the output result of the first sub-model indicates that the to-be-determined warehouses for at least one first-category item in the order information are the same warehouse, it means that the same warehouse can provide at least one first-category item at the same time. If the same warehouse is used as the distribution warehouse for at least one first-category item, the first-category items can be packed into the same package, reducing the distribution cost. And since the expected distribution duration of each first-category item for this same warehouse is relatively short, using this same warehouse as the distribution warehouse for at least one first-category item will not result in an increase in the distribution duration. Therefore, in this operation S234, the distribution warehouse for at least one first-category item indicated by the order information can be determined according to the output result of the first sub-model, and the to-be-determined warehouse is used as the distribution warehouse.

[0067] According to an embodiment of the present invention, the optimization model may further include a second sub-model, and the second sub-model is an integer programming model. The objective function of this integer programming model can represent the sum of a first value and a second value, that is, represent the total of the distribution duration and the distribution cost. This integer programming model includes at least one constraint condition. As Figure 3 shown, the above operation S230 using the pre-constructed optimization model to process the above order information and warehouse information to determine at least one warehouse as the distribution warehouse from the above multiple warehouses according to the output result of the optimization model may further include operations S236 to S239.

[0068] When it is determined in the above operation S234 that the to-be-determined warehouses for at least one first-category item are not the same warehouse, perform operation S236: Based on the at least one constraint condition, the above order information, and the above warehouse information, determine at least one warehouse allocation method. Wherein, each warehouse allocation method may include: the distribution relationship between at least one first-category item and at least one warehouse among the above multiple warehouses. For example, the order information indicates first-category items A1, A2, and A3. Each determined warehouse allocation method may include: the distribution relationship between first-category item A1 and one warehouse among the multiple warehouses, the distribution relationship between first-category item A2 and one warehouse among the multiple warehouses, and the distribution relationship between first-category item A3 and one warehouse among the multiple warehouses.

[0069] In operation S237, calculate the value of the objective function based on the at least one warehouse allocation method. Take the warehouse allocation method that makes the value of the objective function the smallest as the output result of the second sub-model.

[0070] Exemplarily, embodiments of the present invention can determine the value ranges of a first value and a second value based on at least one of the above warehouse allocation methods. Then, based on the value ranges of the first value and the second value, calculate the value of the objective function. Next, use the warehouse allocation method that minimizes the value of the objective function as the output result of the second sub-model.

[0071] For example, each of the above warehouse allocation methods may include: the distribution relationship between at least one of the above first-category items and M warehouses, where M is an integer greater than or equal to 1. The determination of the value ranges of the first value and the second value based on at least one warehouse allocation method includes: for each warehouse allocation method, determine the first value for each warehouse allocation method according to the expected delivery duration of each of the M warehouses for a specified address. And, determine the second value for each warehouse allocation method according to the expected delivery cost of each of the M warehouses.

[0072] The following uses a specific example to exemplarily illustrate the process of processing order data and warehouse data using the integer programming model.

[0073] For example, an integer programming model can be set as shown in formulas (1) to (7).

[0074]

[0075] s.t.X ij ≤s ij

[0076] ∑ j∈J X ij =1

[0077] ∑ i∈I X ij ≥Y j

[0078] X ij ≤Y j

[0079] X ij ∈{0, 1}

[0080] Y j ∈{0, 1}

[0081] Among them, formula (1) indicates that the objective of the integer programming model is to minimize the objective function. Formulas (2) to (7) show the constraint conditions of the integer programming model. Exemplarily, i represents the SKU number of an item, and I is the set of SKU numbers of all specified items in the order information. j represents the number of a warehouse, and J is the set of multiple warehouses. The value of s ij is used to characterize whether warehouse j can meet all the demands of specified item i indicated by the order information. When s ij = 1, it means it can meet the demands; when s ij = 0, it means it cannot meet the demands. t j represents the expected delivery duration of warehouse j. n i represents the demand quantity of specified item i indicated by the order information. n represents the total quantity of all specified items indicated by the order information. w is a weight, and this weight can, for example, characterize the expected delivery cost for a warehouse to deliver a package once. The same or different weights can be set for different warehouses. The value of X ij is used to characterize whether specified item i is delivered by warehouse j, that is, whether there is a delivery relationship between specified item i and warehouse j. When X ij = 1, it means specified item i is delivered by warehouse j; when X ij = 0, it means specified item i is not delivered by warehouse j. The value of Y j is used to characterize whether there is a specified item delivered by warehouse j in the order information. When Y j = 1, it means there is; when Y j = 0, it means there is not. Formula (1) characterizes minimizing the average expected delivery duration and the average expected delivery cost. In this example, the average expected delivery cost is equal to the weighted sum of the number of split orders. The constraint condition of formula (2) restricts that each item (uniquely identified by, for example, the SKU number) can only be delivered by a warehouse capable of meeting all the demand quantities of this item. The constraint condition of formula (3) restricts that each item is only delivered by one warehouse. The constraint conditions of formulas (4) and (5) restrict that a certain warehouse can only become an actual delivery warehouse if it delivers at least one item. The constraint conditions of formulas (6) and (7) restrict that X ij and Y j are variables in the [0 - 1] interval.

[0082] It can be understood that according to the embodiments of the present invention, the above at least one constraint condition is used to restrict at least one of the following: the number of delivery warehouses for each item of the first category; the storage quantity of the first category item in the delivery warehouses for each item of the first category; and the number of items of the first category delivered by each warehouse. Through the integer programming model, several warehouse delivery methods can be defined. Within this defined range, the optimal output result can be determined according to the objective function.

[0083] Continue to refer toFigure 3 In operation S238, it is determined whether the running duration of the second sub-model before obtaining the output result is greater than a predetermined duration. If so, operation S235 is executed again. If not, operation S239 is executed.

[0084] In operation S239, the respective distribution warehouses of the at least one first-category item are determined according to the output result of the second sub-model.

[0085] According to an embodiment of the present invention, operation S239 can be executed in the following manner: In operation S2391, it is determined whether the output result of the second sub-model is better than the output result of the first sub-model. If so, operation S2392 is executed. If not, operation S235 is executed again. In operation S2392, the warehouse allocation method corresponding to the output result of the second sub-model is used to determine the respective distribution warehouses of the at least one first-category item.

[0086] Figure 4 A schematic example flowchart of an order information processing method according to another embodiment of the present invention is shown, which is used to illustrate another example implementation process of the above operation S230 for processing the above order information and warehouse information by using a pre-constructed optimization model to determine at least one warehouse from the above multiple warehouses as the distribution warehouse according to the output result of the optimization model.

[0087] As Figure 4 shown, after starting to execute, the method may include operations S2310 to S2312.

[0088] In operation S2310, it is determined whether there is at least one second-category item among the at least one specified item. If so, operation S2311 is executed. If not, the start state is returned.

[0089] Exemplarily, the second-category item can be set as needed. For example, the second-category item can refer to all items that do not need to be packed, which can be called "original package items", that is, items that can be directly distributed according to the original package. If there are second-category items among the at least one specified item indicated by the order information, the possibility of these second-category items being combined and packed can be ignored during the performance decision-making process.

[0090] In operation S2311, for each second-category item, according to the identification information and the required quantity of the second-category item, a second candidate warehouse that stores the second-category item and has a storage quantity greater than or equal to the required quantity is determined from the above multiple warehouses.

[0091] In operation S2312, a second candidate warehouse with the shortest expected delivery duration for the specified address is determined from the second candidate warehouses as the distribution warehouse of the second-category item.

[0092] The implementation principles of the above operations S2311 - S2312 are the same as those of the operations S232 - S233 in the above text, and will not be elaborated here. After determining the second - candidate warehouse with the shortest expected delivery time for a second - category item, it can be directly used as the delivery warehouse for this second - category item.

[0093] According to an embodiment of the present invention, the optimization model may further include a third sub - model. The above operations S2310 - S2312 can all be executed using the third sub - model, and the delivery warehouse of each of the at least one second - category item determined above is used as the output result of the third sub - model. Thus, the delivery warehouse of each of the at least one second - category item can be determined according to the output result of the third sub - model.

[0094] Figure 5 A block diagram of an order information processing device according to an embodiment of the present invention is schematically shown.

[0095] As Figure 5 shown, the order information processing device 500 may include: a first acquisition module 510, a second acquisition module 520, and a model processing module 530.

[0096] The first acquisition module 510 is used to acquire order information, and the order information includes: information of a specified address and information of at least one specified item.

[0097] The second acquisition module 520 is used to acquire warehouse information, and the warehouse information includes: inventory information of multiple warehouses and delivery information of multiple warehouses.

[0098] The model processing module 530 is used to process the above - mentioned order information and warehouse information using a pre - constructed optimization model, so as to determine at least one warehouse from the above - mentioned multiple warehouses as the delivery warehouse according to the output result of the optimization model, so that the sum of a first value and a second value is less than or equal to a predetermined value. Wherein, the first value is used to represent the delivery duration for delivering at least one specified item from the above - mentioned delivery warehouse to the specified address, and the second value is used to represent the delivery cost for delivering at least one specified item from the above - mentioned delivery warehouse to the specified address.

[0099] It should be noted that the implementation manners, the technical problems solved, the functions achieved, and the technical effects achieved by each module / unit / sub - unit, etc. in the device part of the embodiments are the same as or similar to those of the corresponding steps in the method part of the embodiments, and will not be elaborated here.

[0100] Any of a plurality of modules, sub - modules, units, and sub - units according to embodiments of the present invention, or at least part of the functions of any of them, can be implemented in one module. Any one or more of the modules, sub - modules, units, and sub - units according to embodiments of the present invention can be split into multiple modules for implementation. Any one or more of the modules, sub - modules, units, and sub - units according to embodiments of the present invention can be at least partially implemented as a hardware circuit, such as a field - programmable gate array (FPGA), a programmable logic array (PLA), a system - on - chip, a system - on - substrate, a system - on - package, an application - specific integrated circuit (ASIC), or can be implemented by any other reasonable way of integrating or packaging circuits, etc., in hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, one or more of the modules, sub - modules, units, and sub - units according to embodiments of the present invention can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0101] For example, any of the first acquisition module 510, the second acquisition module 520, and the model processing module 530 can be combined and implemented in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to embodiments of the present invention, at least one of the first acquisition module 510, the second acquisition module 520, and the model processing module 530 can be at least partially implemented as a hardware circuit, such as a field - programmable gate array (FPGA), a programmable logic array (PLA), a system - on - chip, a system - on - substrate, a system - on - package, an application - specific integrated circuit (ASIC), or can be implemented by any other reasonable way of integrating or packaging circuits, etc., in hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the first acquisition module 510, the second acquisition module 520, and the model processing module 530 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0102] Figure 6 A block diagram of a computer device suitable for implementing the model training method and / or the map drawing method described above according to embodiments of the present invention is schematically shown. Figure 6 The computer device shown is merely an example and should not impose any limitation on the functions and usage scope of embodiments of the present invention.

[0103] As Figure 6As shown, the computer device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application-specific integrated circuit (ASIC)), and so on. The processor 601 may also include on-board memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0104] In the RAM 603, various programs and data required for the operation of the device 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to an embodiment of the present invention by executing the program in the ROM 602 and / or the RAM 603. It should be noted that the program may also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to an embodiment of the present invention by executing the program stored in the one or more memories.

[0105] According to an embodiment of the present invention, the device 600 may further include an input / output (I / O) interface 605, and the input / output (I / O) interface 605 is also connected to the bus 604. The device 600 may further include one or more of the following components connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read from it can be installed into the storage section 608 as needed.

[0106] According to an embodiment of the present invention, the method flow according to the embodiment of the present invention can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present invention are executed. According to an embodiment of the present invention, the above-described system, device, apparatus, module, unit, etc. can be implemented by computer program modules.

[0107] An embodiment of the present invention also provides a computer-readable storage medium, which can be included in the device / device / system described in the above embodiment; or can exist separately without being assembled into the device / device / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the model training method and / or the map drawing method according to the embodiment of the present invention are implemented.

[0108] According to an embodiment of the present invention, the computer-readable storage medium can be a non-volatile computer-readable storage medium, for example, it can include but is not limited to: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the embodiment of the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or device. For example, according to an embodiment of the present invention, the computer-readable storage medium can include the above-described ROM 602 and / or RAM 603 and / or one or more memories other than ROM 602 and RAM 603.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations 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 diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as combinations of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0110] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly recited in the embodiments of the present invention. In particular, without departing from the spirit and teachings of the embodiments of the present invention, the features recited in the various embodiments and / or claims of the embodiments of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the embodiments of the present invention.

Claims

1. A method for processing order information, comprising: Obtain order information, where the order information includes: information of a specified address and information of at least one specified item; Obtain warehouse information, where the warehouse information includes: inventory information of multiple warehouses and distribution information of the multiple warehouses; and Use a pre-constructed optimization model to process the information of the specified address, the information of each specified item in the order information, and the warehouse information, to determine whether there is at least one item of the first category or the second category among the at least one specified item indicated by the order information, and determine at least one warehouse from the multiple warehouses as the distribution warehouse according to the output result of the at least one item of the first category or the second category of the optimization model, so that the sum of the first value and the second value of all the specified items in the order information is less than or equal to a predetermined value. Among them, the item of the first category is used to represent an item that is not directly distributed in the original package but needs to be packed, and the item of the second category is used to represent an item that is directly distributed in the original package and does not need to be repacked. The first value is used to represent the distribution duration spent on distributing the at least one specified item from the distribution warehouse to the specified address, and the second value is used to represent the distribution cost spent on distributing the at least one specified item from the distribution warehouse to the specified address. Among them, the optimization model includes a first sub-model, a second sub-model, and a third sub-model. The second sub-model is an integer programming model, and the objective function of the integer programming model represents the sum of the first value and the second value. The integer programming model includes at least one constraint condition; Using a pre-constructed optimization model to process the information of the specified address, the information of each specified item in the order information, and the warehouse information includes: When the optimization model is the first sub-model, use the first sub-model to perform the following operations: When there is at least one item of the first category among the at least one specified item, for each item of the first category, according to the identification information and the required quantity of the item of the first category, determine a first candidate warehouse that stores the item of the first category and the stored quantity is greater than or equal to the required quantity from the multiple warehouses, and determine the first candidate warehouse with the shortest expected distribution duration for the specified address from the first candidate warehouses as the pending warehouse for the item of the first category; Use the pending warehouse of each of the at least one item of the first category as the output result of the first sub-model; When the optimization model is the second sub-model, use the second sub-model to perform the following operations: When the pending warehouses of each of the at least one item of the first category are not the same warehouse, based on the at least one constraint condition, the order information, and the warehouse information, determine at least one warehouse allocation method, where each warehouse allocation method includes: the distribution relationship between the at least one item of the first category and at least one of the multiple warehouses; Based on the at least one warehouse allocation method, determine the value range of the first value and the second value; Calculate the value of the objective function based on the value ranges of the first value and the second value; and Take the warehouse allocation method that minimizes the value of the objective function as the output result of the second sub-model; In the case where the optimization model is the third sub-model, perform the following operations using the third sub-model: When there is at least one second-category item among the at least one specified item, for each second-category item, based on the identification information and required quantity of the second-category item, determine a second candidate warehouse from the multiple warehouses that stores the second-category item and has a storage quantity greater than or equal to the required quantity, and determine, from the second candidate warehouses, the second candidate warehouse with the shortest expected delivery duration for the specified address as the delivery warehouse for the second-category item; Take the delivery warehouse of each of the at least one second-category item as the output result of the third sub-model.

2. The method according to claim 1, wherein The information of each specified item among the at least one specified item includes: the identification information of each specified item and the required quantity of each specified item; The inventory information of each warehouse among the multiple warehouses includes: the identification information of the items stored in each warehouse and the storage quantity of each item in each warehouse; and The delivery information of each warehouse among the multiple warehouses includes: the expected delivery duration of each warehouse for multiple addresses and the expected delivery cost of each warehouse.

3. The method according to claim 1, wherein The determining of at least one warehouse as the delivery warehouse from the multiple warehouses according to the output result of the optimization model includes: When the pending warehouses of each of the at least one first-category item are the same warehouse, determine the pending warehouses of each of the at least one first-category item as the delivery warehouses of each of the at least one first-category item.

4. The method according to claim 1, wherein The determining of at least one warehouse as the delivery warehouse from the multiple warehouses according to the output result of the optimization model further includes: Determine whether the running duration of the second sub-model before obtaining the output result is greater than a predetermined duration; If so, determine the pending warehouses of each of the at least one first-category item as the delivery warehouses of each of the at least one first-category item; and If not, determine the delivery warehouses of each of the at least one first-category item according to the output result of the second sub-model.

5. The method according to claim 1, wherein Each warehouse allocation method includes: the delivery relationship between the at least one first-category item and M warehouses, where M is an integer greater than or equal to 1; The determining of the value ranges of the first value and the second value based on the at least one warehouse allocation method includes: For each warehouse allocation method, Determine the first value for each warehouse allocation method according to the expected delivery duration of each of the M warehouses for the specified address; and Determine the second value for each warehouse allocation method according to the expected delivery cost of each of the M warehouses.

6. The method according to claim 1, wherein The at least one constraint condition is used to limit at least one of the following: The number of delivery warehouses for each first-category item; The storage quantity of the first category of items in the distribution warehouse for each item of the first category; and The number of items of the first category distributed by each warehouse.

7. The method according to claim 1, wherein Determining at least one warehouse as the distribution warehouse from the multiple warehouses according to the output result of the optimization model further includes: Determining the distribution warehouse for each of the at least one item of the second category according to the output result of the third sub-model.

8. An order information processing apparatus, comprising: A first acquisition module, configured to acquire order information, where the order information includes: information of a specified address and information of at least one specified item; A second acquisition module, configured to acquire warehouse information, where the warehouse information includes: inventory information of multiple warehouses and distribution information of the multiple warehouses; and A model processing module, configured to process the information of the specified address, the information of each specified item in the order information, and the warehouse information by using a pre-constructed optimization model, determine whether there is at least one item of the first category or the second category among the at least one specified item indicated by the order information, and determine at least one warehouse as the distribution warehouse from the multiple warehouses according to the output result of the optimization model for the at least one item of the first category or the second category, so that the sum of the first value and the second value of all the specified items in the order information is less than or equal to a predetermined value, where the optimization model includes a first sub-model, a second sub-model, and a third sub-model, the second sub-model is an integer programming model, the objective function of the integer programming model represents the sum of the first value and the second value, and the integer programming model includes at least one constraint condition; The model processing module is further configured to, when the optimization model is the first sub-model, perform the following operations by using the first sub-model: when there is at least one item of the first category among the at least one specified item, for each item of the first category, determine a first candidate warehouse that stores the first category of items and has a storage quantity greater than or equal to the required quantity from the multiple warehouses according to the identification information and the required quantity of the first category of items, and determine a first candidate warehouse with the shortest expected delivery duration for the specified address from the first candidate warehouses as the pending warehouse for the first category of items; use the pending warehouses of the at least one item of the first category as the output result of the first sub-model; The model processing module is further configured to, when the optimized model is the second sub-model, use the second sub-model to perform the following operations: when the to-be-determined warehouses of the at least one first-category item are not the same warehouse, based on the at least one constraint condition, the order information, and the warehouse information, determine at least one warehouse allocation method, where each warehouse allocation method includes: the distribution relationship between the at least one first-category item and at least one of the multiple warehouses; based on the at least one warehouse allocation method, determine the value ranges of the first value and the second value; based on the value ranges of the first value and the second value, calculate the value of the objective function; and use the warehouse allocation method that minimizes the value of the objective function as the output result of the second sub-model. The model processing module is further configured to, when the optimized model is the third sub-model, use the third sub-model to perform the following operations: when there is at least one second-category item among the at least one specified item, for each second-category item, according to the identification information and the required quantity of the second-category item, determine a second candidate warehouse from the multiple warehouses that stores the second-category item and has a storage quantity greater than or equal to the required quantity, and determine, from the second candidate warehouses, the second candidate warehouse with the shortest expected delivery duration for the specified address as the delivery warehouse for the second-category item; and use the delivery warehouses of the at least one second-category item as the output result of the third sub-model. Wherein, the first-category item is used to represent an item that needs to be packed instead of being directly delivered in its original packaging, the second-category item is used to represent an item that is directly delivered in its original packaging and does not need to be repacked, the first value is used to represent the delivery duration spent on delivering the at least one specified item from the delivery warehouse to the specified address, and the second value is used to represent the delivery cost spent on delivering the at least one specified item from the delivery warehouse to the specified address.

9. A computer device, comprising: A memory storing computer instructions thereon; and At least one processor; Wherein, when the processor executes the computer instructions, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, having stored thereon computer instructions, which when executed by a processor, implement the method according to any one of claims 1 to 7.

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