Warehouse picking location allocation method, device, medium and electronic equipment

Abstract

The embodiments of the present application provide a method, device, computer-readable medium, and electronic device for allocating warehouse picking locations. The method includes: obtaining attribute data and historical picking data of each model of goods; obtaining location distribution data of each picking location in the warehouse; based on the attribute data, the historical picking data, and the location distribution data, allocating picking locations for each model of goods, and obtaining a target binding relationship between the picking location number and the goods model, wherein the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency corresponding to the original binding relationship, and the original binding relationship is the historical binding relationship between the picking location number and the goods model. The technical solution of the embodiments of the present application can improve warehouse picking efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of computers and warehouse picking planning, and in particular to a warehouse picking location allocation method, device, medium and electronic equipment. Background Art

[0002] At present, the industry's warehouse goods picking method is the fruit picking method, which usually designates one layer of warehouse shelves for picking and the upper layers for storage. After obtaining the next day's picking goods list in advance, the goods involved in the picking need to be added from the upper layers to the picking locations on the first layer, and then picked again the next day according to the picking goods list. In the existing technical solutions, picking locations are usually evenly distributed for each type of goods, but this location allocation method is too rough and cannot guarantee the accuracy of the warehouse picking location allocation, resulting in low picking efficiency. Based on this, how to improve warehouse picking efficiency is a technical problem that needs to be solved urgently. Summary of the Invention

[0003] The embodiments of the present application provide a warehouse picking location allocation method, device, computer program product or computer program, computer-readable medium and electronic device, which can improve the accuracy of warehouse picking location allocation at least to a certain extent.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a warehouse picking location allocation method is provided, the method comprising: obtaining attribute data and historical picking data of each model of goods; obtaining location distribution data of each picking location in the warehouse; based on the attribute data, the historical picking data, and the location distribution data, allocating picking locations to each model of goods, and obtaining a target binding relationship between the picking location number and the goods model, wherein the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency corresponding to the original binding relationship, and the original binding relationship is the historical binding relationship between the picking location number and the goods model.

[0006] In some embodiments of the present application, based on the aforementioned scheme, allocating picking locations for each model of goods based on the attribute data, the historical picking data, and the location distribution data includes: inputting the attribute data, the historical picking data, and the location distribution data into a pre-built mixed integer programming model; and allocating picking locations for each model of goods through the mixed integer programming model.

[0007] In some embodiments of the present application, based on the aforementioned scheme, the attribute data includes at least the cargo model, cargo category, cargo brand, and cargo weight; the historical picking data includes at least the cargo picking time and cargo model distribution of each picking batch in history, as well as the cargo picking frequency and cargo picking quantity of the same model of cargo in each unit time in history.

[0008] In some embodiments of the present application, based on the aforementioned scheme, the mixed integer programming model includes the following constraints: picking locations are allocated to each model of goods in order of priority from high to low, wherein the order of allocating picking locations is in the order of the picking locations from the picking front end to the picking back end in the warehouse.

[0009] In some embodiments of the present application, based on the aforementioned solution, the priority of each cargo model is determined by the following formula:

[0010] Z=M×m+N×n

[0011] Among them, Z represents the priority of a certain type of goods; M represents the delivery frequency of a certain type of goods in history; m represents the weight corresponding to the delivery frequency; N represents the weight of a certain type of goods in history; n represents the weight corresponding to the weight of the goods.

[0012] In some embodiments of the present application, based on the aforementioned solution, the mixed integer programming model includes the following constraints: allocating picking locations in a location set to goods with model correlation, wherein the goods of model a and model b with model correlation satisfy the following conditions:

[0013] and

[0014] Among them, Xab represents the number of picking batches that historically contain both type a goods and type b goods, Xa represents the number of picking batches that historically contain type a goods, Xb represents the number of picking batches that historically contain type b goods, and Y represents the preset threshold.

[0015] In some embodiments of the present application, based on the aforementioned solution, the mixed integer programming model includes the following constraints: allocating picking locations in a location cluster to goods of the same brand, category, or model.

[0016] In some embodiments of the present application, based on the aforementioned solution, the mixed integer programming model includes the following constraint: the number of picking locations allocated to goods of the same model matches the number of goods picked in the same historical period.

[0017] According to one aspect of an embodiment of the present application, a warehouse picking location allocation device is provided, the device comprising: a first acquisition unit for acquiring attribute data and historical picking data of each model of goods; a second acquisition unit for acquiring location distribution data of each picking location in the warehouse; an allocation unit for allocating picking locations to each model of goods based on the attribute data, the historical picking data, and the location distribution data, and obtaining a target binding relationship between the picking location number and the goods model, wherein the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency corresponding to the original binding relationship, and the original binding relationship is the historical binding relationship between the picking location number and the goods model.

[0018] According to one aspect of an embodiment of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described in the above embodiment.

[0019] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above embodiment is implemented.

[0020] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the methods described in the above embodiments.

[0021] In the technical solutions provided in some embodiments of the present application, picking locations are allocated to each model of goods through the attribute data, the historical picking data, and the location distribution data, and a target binding relationship between the picking location number and the goods model is obtained. Since the goods attribute data, the historical picking data of the goods, and the location distribution data of the picking location are taken into consideration at the same time when allocating picking locations to the goods, the accuracy of the warehouse picking location allocation can be improved, so that the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency of the historical binding relationship between the picking location number and the goods model.

[0022] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, explaining the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0024] Figure 1 A diagram showing a scenario in which the embodiments of the present application can be applied to allocate picking locations for various types of goods in a warehouse;

[0025] Figure 2 A flowchart of a warehouse picking location allocation method according to one embodiment of the present application is shown;

[0026] Figure 3 A detailed flow chart of allocating picking locations for various types of goods according to one embodiment of the present application is shown;

[0027] Figure 4 A block diagram of a warehouse picking location allocation device according to an embodiment of the present application is shown;

[0028] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0030] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0033] It should be noted that the term "plurality" used in this document refers to two or more. "And / or" describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. The character " / " generally indicates an "or" relationship between the associated objects.

[0034] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described.

[0035] Figure 1 A diagram showing a scenario in which an embodiment of the present application can be applied to allocate picking locations for various types of goods in a warehouse.

[0036] In the present application, the warehouse 101 involved can be used to store goods. The warehouse 101 includes an entrance and exit 102, which can be used to input and output goods. The warehouse 101 also includes multiple picking locations 103. In actual application scenarios, the picking locations are used to place goods to be picked. In order to improve the efficiency of picking goods, it is necessary to allocate reasonable locations for different types of goods 104, such as Figure 1 As shown, picking locations 1 and 2 can be assigned to goods of model A, picking locations 3 and 4 can be assigned to goods of model E, picking locations 5 and 9 can be assigned to goods of model C, picking locations 6, 10 and 11 can be assigned to goods of model A, picking location 2 can be assigned to goods of model D, and picking locations 8 and 12 can be assigned to goods of model F.

[0037] The following is a detailed description of the implementation details of the technical solution of the embodiment of the present application:

[0038] Figure 2 The flowchart of the warehouse picking location allocation method according to one embodiment of the present application is shown. The warehouse picking location allocation method can be executed by a device with computing and processing functions. Figure 2 As shown, the warehouse picking location allocation method includes at least steps 210 to 250, which are described in detail as follows:

[0039] In step 210 , attribute data and historical picking data of each model of goods are obtained.

[0040] In the present application, the attribute data may include the model of the goods, such as the SKU used to identify the type of goods, and may also include the type of goods, such as toiletries, clothing, etc., and may also include the brand of the goods and the weight of the goods. It should be understood that the attribute data of each model of goods may also include others, not limited to those listed above.

[0041] In this application, the historical picking data may include the picking time and cargo model distribution of each picking batch (i.e., cargo order) in history. For example, the picking time of picking batch A is January 1, 2022. For example, the cargo model distribution of picking batch A includes anti-dandruff shampoo a, spicy instant hot pot b, etc.

[0042] In addition, the historical picking data may also include the frequency and quantity of picking of the same type of goods in each unit time in history. For example, the frequency of picking of spicy instant hot pot b on January 1, 2022 was 4 times, and the quantity of picking was 30 boxes, 28 boxes, 53 boxes, and 16 boxes respectively.

[0043] Continue to refer to Figure 2 In step 230, the location distribution data of each picking location in the warehouse is obtained.

[0044] In this application, the location distribution data of each picking location in the warehouse may include the picking location number, cargo storage capacity, coordinates in the warehouse, walking routes to each picking location, etc.

[0045] Continue to refer to Figure 2 In step 250, based on the attribute data, the historical picking data, and the location distribution data, picking locations are allocated to each model of goods, and a target binding relationship between the picking location number and the goods model is obtained, wherein the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency corresponding to the original binding relationship, and the original binding relationship is the historical binding relationship between the picking location number and the goods model.

[0046] In one embodiment of the present application, based on the attribute data, the historical picking data, and the location distribution data, picking locations are allocated to each type of goods, which can be as follows: Figure 3 Follow the steps shown.

[0047] See also Figure 3, shows a detailed flow chart of allocating picking locations for various types of goods according to an embodiment of the present application. Specifically, it includes steps 231 to 232:

[0048] Step 231 : Input the attribute data, the historical picking data, and the location distribution data into a pre-built mixed integer programming model.

[0049] Step 232: Allocate picking locations for each type of goods using the mixed integer programming model.

[0050] Mixed Integer Programming (MIP) models have a linear objective function and all constraints are also linear. By setting the constraints, accurate planning results can be output. This application uses the MIP model to accurately assign picking locations for each type of goods, improving goods picking efficiency.

[0051] In one embodiment of the present application, the mixed integer programming model may include the following constraints:

[0052] Picking locations are allocated to each type of goods in descending order of priority. The order of allocating picking locations is based on the order of the picking locations from the front end to the back end of the warehouse.

[0053] In this embodiment, for example, the priority scores of drinking water, dried fruits, and bread are 3, 2, and 1, respectively (the larger the priority score, the higher the priority). Therefore, when allocating picking locations for drinking water, dried fruits, and bread, the picking locations at the front of the warehouse (such as the picking locations near the warehouse exit) are first allocated to drinking water with the highest priority.

[0054] In this embodiment, the priority of each cargo model can be determined by the following formula:

[0055] Z=M×m+N×n

[0056] Among them, Z represents the priority of a certain type of goods; M represents the delivery frequency of a certain type of goods in history; m represents the weight corresponding to the delivery frequency; N represents the weight of a certain type of goods in history; n represents the weight corresponding to the weight of the goods.

[0057] In this application, when determining the priority for each type of goods, the frequency of shipment of the goods and the weight of the goods themselves can be considered at the same time, that is, the higher the frequency of shipment of the goods, the higher the priority, and the heavier the weight of the goods, the higher the priority. In this way, goods with high weight and high shipment frequency can be first allocated to the picking locations at the front of the picking locations in the warehouse, which is conducive to improving the efficiency of goods picking.

[0058] In this embodiment, in the formula "Z=M×m+N×n", "M" and "N" can also represent the values ​​after the shipment frequency and shipment weight are normalized, respectively. In this way, the shipment frequency and shipment weight can be dedimensionalized to avoid inaccurate priority determination due to dimensioning, thereby improving the accuracy of allocating picking locations for goods.

[0059] In one embodiment of the present application, the mixed integer programming model may further include the following constraints:

[0060] Assign picking locations in a location cluster to model-related goods, where model-related goods A and B meet the following conditions:

[0061] and

[0062] Among them, Xab represents the number of picking batches that historically contain both type a goods and type b goods, Xa represents the number of picking batches that historically contain type a goods, Xb represents the number of picking batches that historically contain type b goods, and Y represents the preset threshold.

[0063] In this application, goods with model correlation are allocated to picking locations with concentrated locations. For example, erasers and pencils are allocated to picking locations with concentrated locations. This can ensure that as many picking batches of goods as possible are picked through the shortest possible picking distance, thereby improving picking efficiency.

[0064] In one embodiment of the present application, the mixed integer programming model may further include the following constraints:

[0065] Assign centralized picking locations to goods of the same brand, category, or model.

[0066] In this application, allocating goods of the same brand, category or model to concentrated picking locations can also ensure that as many picking batches of goods as possible are picked through the shortest possible picking distance, thereby improving picking efficiency.

[0067] In one embodiment of the present application, the mixed integer programming model may further include the following constraints:

[0068] The number of picking locations allocated for the same type of goods matches the number of goods picked in the same period in history. In this application, if the number of goods picked in the same period in history is higher, then more picking locations will be allocated for that item. This can enhance the rationality of picking location allocation and improve goods picking efficiency.

[0069] In this application, it should be noted that it is possible to determine whether the goods are seasonal. Seasonality has two characteristics: seasonal differences and repeatability. Data from the past two years can be selected to calculate the ratio of the number of monthly picking batches (i.e., the number of shipping orders) to the number of picking batches for the whole year. If the ratio of any month exceeds a threshold x, it is considered to be different; if there is overlap between months with significant differences in the two years, it indicates repeatability and is considered a seasonal product.

[0070] In this application, it should be noted that the shipment volume of goods is calculated. For non-seasonal goods, the daily shipment volume of the past year is observed (converted into pallets using packaging data), and the 0.8 quantile can be taken as its shipment volume. For seasonal goods, if the current shipment period is peak, the daily shipment volume of the peak month in the past year is observed, and the 0.8 quantile can be taken as its shipment volume; the same applies to the trough period. Using quantiles instead of means can effectively avoid the influence of extreme values ​​and ensure that the number of picking locations remains sufficient for most of the year.

[0071] In this application, it is important to note that the shipping frequency is calculated. For non-seasonal goods, the shipping frequency is calculated by averaging the number of monthly picking batches over the past year. For seasonal goods, if the current shipping season is peak, the shipping frequency is calculated by averaging the number of monthly picking batches over the past year's peak months. The same applies to off-peak seasons.

[0072] In the technical solutions provided in some embodiments of the present application, picking locations are allocated to each model of goods through the attribute data, the historical picking data, and the location distribution data, and a target binding relationship between the picking location number and the goods model is obtained. Since the goods attribute data, the historical picking data of the goods, and the location distribution data of the picking location are taken into consideration at the same time when allocating picking locations to the goods, the accuracy of the warehouse picking location allocation can be improved, so that the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency of the historical binding relationship between the picking location number and the goods model.

[0073] The following describes an embodiment of the device of the present application, which can be used to implement the warehouse picking and location allocation method in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the warehouse picking and location allocation method in the above embodiment of the present application.

[0074] Figure 4 A block diagram of a warehouse picking location allocation device according to an embodiment of the present application is shown.

[0075] Reference Figure 4As shown, a warehouse picking location allocation device 400 according to an embodiment of the present application includes: a first acquisition unit 401, a second acquisition unit and an allocation unit 604.

[0076] Among them, the first acquisition unit 401 is used to obtain the attribute data and historical picking data of each model of goods; the second acquisition unit 402 is used to obtain the location distribution data of each picking location in the warehouse; the allocation unit 403 is used to allocate picking locations for each model of goods based on the attribute data, the historical picking data, and the location distribution data, and obtain the target binding relationship between the picking location number and the goods model, wherein the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency corresponding to the original binding relationship, and the original binding relationship is the historical binding relationship between the picking location number and the goods model.

[0077] In some embodiments of the present application, based on the aforementioned scheme, the allocation unit 403 is configured to: input the attribute data, the historical picking data, and the location distribution data into a pre-built mixed integer programming model; and allocate picking locations for each model of goods through the mixed integer programming model.

[0078] In some embodiments of the present application, based on the aforementioned scheme, the attribute data includes at least the cargo model, cargo category, cargo brand, and cargo weight; the historical picking data includes at least the cargo picking time and cargo model distribution of each picking batch in history, as well as the cargo picking frequency and cargo picking quantity of the same model of cargo in each unit time in history.

[0079] In some embodiments of the present application, based on the aforementioned scheme, the mixed integer programming model includes the following constraints: picking locations are allocated to each model of goods in order of priority from high to low, wherein the order of allocating picking locations is in the order of the picking locations from the picking front end to the picking back end in the warehouse.

[0080] In some embodiments of the present application, based on the aforementioned solution, the priority of each cargo model is determined by the following formula:

[0081] Z=M×m+N×n

[0082] Among them, Z represents the priority of a certain type of goods; M represents the delivery frequency of a certain type of goods in history; m represents the weight corresponding to the delivery frequency; N represents the weight of a certain type of goods in history; n represents the weight corresponding to the weight of the goods.

[0083] In some embodiments of the present application, based on the aforementioned solution, the mixed integer programming model includes the following constraints: allocating picking locations in a location set to goods with model correlation, wherein the goods of model a and model b with model correlation satisfy the following conditions:

[0084] and

[0085] Among them, Xab represents the number of picking batches that historically contain both type a goods and type b goods, Xa represents the number of picking batches that historically contain type a goods, Xb represents the number of picking batches that historically contain type b goods, and Y represents the preset threshold.

[0086] In some embodiments of the present application, based on the aforementioned solution, the mixed integer programming model includes the following constraints: allocating picking locations in a location cluster to goods of the same brand, category, or model.

[0087] In some embodiments of the present application, based on the aforementioned solution, the mixed integer programming model includes the following constraint: the number of picking locations allocated to goods of the same model matches the number of goods picked in the same historical period.

[0088] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0089] It should be noted that Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0090] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 503. The CPU 501, ROM 502 and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0091] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output section 505 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read therefrom can be installed into the storage section 508 as needed.

[0092] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 509, and / or installed from a removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the various functions defined in the system of the present application are executed.

[0093] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, 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 suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0095] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0096] As another aspect, the present application further provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described in the above embodiments.

[0097] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0098] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0099] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0100] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0101] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A warehouse picking location allocation method, characterized in that: The method comprises: Obtain attribute data and historical picking data for each model of goods; Obtain location distribution data for each picking location in the warehouse; Based on the attribute data, the historical picking data, and the location distribution data, a picking location is assigned to each model of goods, and a target binding relationship between the picking location number and the model of goods is obtained, wherein the picking efficiency of goods corresponding to the target binding relationship is higher than the picking efficiency of goods corresponding to the original binding relationship, wherein the original binding relationship is a historical binding relationship between the picking location number and the model of goods; Allocating picking locations for each type of goods based on the attribute data, the historical picking data, and the location distribution data includes: Inputting the attribute data, the historical picking data, and the location distribution data into a pre-built mixed integer programming model; Allocating picking locations for each type of goods using the mixed integer programming model; The attribute data includes at least the model, category, brand, and weight of the goods; the historical picking data includes at least the picking time and model distribution of goods for each picking batch in history, as well as the picking frequency and quantity of goods of the same model in each unit time in history; The mixed integer programming model includes the following constraints: Allocate picking locations for each type of goods in descending order of priority. The order of allocating picking locations follows the order from the front end of picking to the back end of picking within the warehouse. The priority of each cargo type is determined by the following formula: Z=M×m+N×n Among them, Z represents the priority of a certain type of goods; M represents the frequency of shipment of a certain type of goods in history; m represents the weight corresponding to the shipment frequency; N represents the weight of a certain type of goods in history; n represents the weight corresponding to the weight of the goods; Assign picking locations in a location cluster to model-related goods, where model-related goods A and B meet the following conditions: and Where Xab represents the number of picking batches that historically contain both type a and type b goods, Xa represents the number of picking batches that historically contain type a goods, Xb represents the number of picking batches that historically contain type b goods, and Y represents the preset threshold; Assign centralized picking locations to goods of the same brand, category, or model; To determine whether the goods are seasonal, for non-seasonal goods, observe the daily shipment volume of the past year and take the 0.8 quantile as its shipment volume. For seasonal goods, if the current shipment period is peak, observe the daily shipment volume of the peak month in the past year and take the 0.8 quantile as its shipment volume. In addition, for non-seasonal goods, the average number of picking batches per month in the past year is taken as its delivery frequency. For seasonal goods, if the current shipping period is peak, the average number of picking batches per month in the peak months in the past year is taken as its delivery frequency.

2. A warehouse picking and location allocation device based on the method of claim 1, characterized in that: The device comprises: A first acquisition unit is used to acquire attribute data and historical picking data of each model of goods; The second acquisition unit is used to obtain the location distribution data of each picking location in the warehouse; An allocation unit is used to allocate picking locations to each model of goods based on the attribute data, the historical picking data, and the location distribution data, and obtain a target binding relationship between the picking location number and the goods model, wherein the goods picking efficiency corresponding to the target binding relationship is higher than the goods picking efficiency corresponding to the original binding relationship, and the original binding relationship is a historical binding relationship between the picking location number and the goods model.

3. An electronic device, characterized in that: The electronic device includes one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method according to claim 1.

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