SKU shelving method and storage medium for mobile robot warehousing systems

Abstract

This invention discloses a SKU shelving method and storage medium for a mobile robot warehousing system. The method includes: acquiring data information from the mobile robot warehousing system; constructing an SKU shelving decision model based on the acquired data information; constructing a calculation method for an SKU shelving strategy based on the data information and the SKU shelving decision model; calculating the SKU shelving result of the mobile robot warehousing system according to the calculation method, and implementing the method. The SKU shelving method strategy for a mobile robot warehousing system provided by this invention proposes an SKU shelving method from a globally optimal perspective. Compared with existing technologies, it can more rationally allocate shelving locations for SKUs, reduce the number of shelves required to complete all orders, and improve picking efficiency. This invention has good practicality and operability, and the calculation method is simple, providing a new and efficient strategy for solving the SKU shelving problem in mobile robot warehousing systems.

Description

Technical Field

[0001] This invention relates to the field of warehousing system operation technology, and specifically to a SKU shelving method and storage medium for a mobile robot warehousing system. Background Technology

[0002] With the rapid development of the e-commerce industry and consumers' increasingly stringent requirements for delivery time, large e-commerce companies are placing greater emphasis on the development of intelligent warehousing and improving order picking efficiency within warehouses. As mobile robot technology continues to mature, mobile robot-based warehousing systems are widely used in order picking scenarios. The order picking process in such systems is as follows: First, consumer orders are assigned to picking stations. Then, the shelves that need to be moved to fulfill the required SKUs are determined, and the relevant moving tasks are sent to the mobile robots. Subsequently, the mobile robots move the designated shelves to the corresponding picking stations according to the moving tasks. Pickers are then responsible for retrieving the corresponding SKUs from the shelves at the picking stations and placing them in the corresponding baskets according to the order. After all the required SKUs have been retrieved from the shelf, the mobile robot needs to return the shelf to the shelf storage area, and this cycle repeats. We have found that how to store SKUs on the shelves (SKU shelving method) is a crucial decision-making issue affecting the order picking efficiency of this type of system.

[0003] Among existing companies using mobile robot warehousing systems, the vast majority employ experience-based SKU placement methods (such as random placement or placement based on product popularity). The main problem with these methods is that some shelves have a high hit rate, while the average hit rate is low, resulting in low overall picking efficiency. This increases the system's picking time and raises the company's operating costs. Summary of the Invention

[0004] The present invention provides a SKU shelving method, system, and equipment for a mobile robot warehousing system, which can at least solve one of the technical problems in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for SKU shelving in a mobile robot warehousing system, comprising:

[0007] Acquire data information from the mobile robot warehousing system;

[0008] Based on the acquired data, a decision-making model for SKU listing is constructed.

[0009] Based on data information and SKU listing decision models, a calculation method for SKU listing strategies is constructed.

[0010] Based on the calculation method, calculate the SKU shelving results of the mobile robot warehousing system and implement it.

[0011] Furthermore, the aforementioned data includes historical order quantity information, order content information, namely, the types of inventory units (hereinafter referred to as SKUs) contained in each order, the types of SKUs that need to be allocated shelf locations, the number of available shelves, and the shelf capacity limit information, namely, the maximum number of SKU types that can be placed on each shelf.

[0012] Furthermore, the above-mentioned SKU listing decision model includes: calculating the utility value between any two SKUs based on the acquired data information; and constructing the SKU listing decision model based on the utility value.

[0013] Furthermore, the formula for calculating the utility value between the two SKUs mentioned above is expressed by equation (1):

[0014] (1)

[0015] in, , For any two SKUs; It is a SKU With SKU The utility value; It includes SKUs and The number of orders; This refers to the total number of orders mentioned above.

[0016] Furthermore, the objective of the above SKU listing decision model is expressed by equation (2):

[0017] (2)

[0018] in, This refers to the number of available shelves mentioned above; It is a shelf The utility value.

[0019] Furthermore, the SKU shelving decision model of the aforementioned mobile robot warehousing system is constrained by the conditions expressed in equations (3) to (9):

[0020] (3)

[0021] (4)

[0022] (5)

[0023] (6)

[0024] (7)

[0025] (8)

[0026] (9)

[0027] in, This refers to the number of available shelves mentioned above; The above refers to the shelf capacity limitation; This refers to the number of SKU types that need to be allocated shelf space; The above SKUs With SKU The utility value; The aforementioned shelves The utility value; It is a 0-1 decision variable, when SKU The shelf location is on the shelf. If it is above, it equals 1; otherwise, it equals 0. It is a 0-1 decision variable, when SKU With SKU Their shelf positions are all on the shelves. If the value is 1, it equals 0; otherwise, it equals 0. Constraint (3) means that each SKU can only be stored in one location on one shelf; constraint (4) means that the number of SKU types stored on each shelf cannot exceed the shelf capacity; constraints (5) and (6) mean that... The value conditions; constraint condition (7) represents the shelf The method for calculating the utility value; constraints (8) and (9) give the 0-1 decision variables. , The range of values ​​for .

[0028] Furthermore, the above calculation method includes: constructing an original assignment matrix based on the above utility values; constructing a multi-stage dynamic assignment method based on the original assignment matrix; and correcting the results based on the results of the multi-stage dynamic assignment method.

[0029] Furthermore, the original assignment matrix described above is represented by equation (10):

[0030] (10)

[0031] In this matrix, each row and column represents a SKU, and the same index value indicates the same type of SKU. Sometimes, ,when Sometimes, .

[0032] Furthermore, the above-mentioned multi-stage assignment methods include:

[0033] Initialization: The current matrix is Current utility value ;

[0034] Step 1: Perform an assignment calculation on the current matrix to obtain the utility value. Assignment results ;

[0035] Step Two: If If the assignment fails, the assignment will stop, and the final assignment result will be obtained. Otherwise, proceed to step three.

[0036] Step 3: Set the current utility value And based on the assignment results Update the current matrix Return to step one.

[0037] Furthermore, the above-mentioned assignment solution for the current matrix includes:

[0038] Each row and column of the current matrix represents a SKU combination, and the same index value indicates that the combination is the same;

[0039] The above-mentioned assignment solution method is expressed by equations (11) to (15):

[0040] (11)

[0041] (12)

[0042] (13)

[0043] (14)

[0044] (15)

[0045] in, For the current matrix, the first... line, number The values ​​for the column; It is a 0-1 decision variable, when combined Assigned to the group The objective function (11) represents maximizing the assignment utility value; the constraint (12) represents each combination. It can only be assigned to one group Constraint (13) represents each combination It can only be assigned to one group Constraint (14) indicates that when the combination Assigned to the group When, combination It must also be assigned to the group at the same time. Constraint (15) gives the 0-1 decision variables. The range of values ​​for .

[0046] The above assignment results Combination of multiple This indicates that each combination Indicate combination With combination The union of included SKUs, in practical terms, means that the SKUs in these combinations are placed on the same shelf.

[0047] Furthermore, the above updates the current matrix. Includes: updating the current matrix Number of rows and columns; update the current matrix. The values ​​of the newly added rows and columns.

[0048] The rules for updating the number of rows and columns mentioned above are as follows: for the assignment results Each combination Perform: If the combination With the current matrix If no combination of any two elements is identical, then the combination is... Update to the current matrix respectively The rows and columns are used as the new SKU combination; otherwise, the combination is not updated. .

[0049] The above updates the current matrix The formula for calculating the values ​​of the newly added rows and columns is expressed by equation (16):

[0050] (16)

[0051] in, It is a combination With combination The number of SKU types contained in the union of the sets.

[0052] Furthermore, the above-mentioned correction method includes: adjusting the assignment results The included combinations are divided into several combination sub-blocks, such that no two sub-blocks have duplicate SKUs, and combination sub-blocks that appear only in one combination are assigned to a set. The combination sub-blocks that appear in multiple combinations are divided into sets. ; Calculate the utility values ​​between combined sub-blocks; Construct a correction model and perform correction calculations.

[0053] Furthermore, the formula for calculating the utility value between combined sub-blocks is expressed by equation (17):

[0054] (17)

[0055] in, It is a composite sub-block and The utility value, The SKU With SKU The utility value.

[0056] Furthermore, the above-mentioned modified model is represented by equations (18) to (24):

[0057] (18)

[0058] (19)

[0059] (20)

[0060] (twenty one)

[0061] (twenty two)

[0062] (twenty three)

[0063] (twenty four)

[0064] in, The above refers to the shelf capacity limitation; This is the number of SKUs contained in the combined sub-block; It is a 0-1 decision variable, when the combination sub-block and The value is 1 if the included SKUs are placed on the same shelf, and 0 otherwise. It is a 0-1 decision variable, when the combination sub-block , and The set is equal to 1 if the included SKUs are placed on the same shelf, and equal to 0 otherwise. Equation (19) represents the set. Each composite sub-block in the set can only be combined with the set. A composite sub-block Placed on the same shelf; Equation (20) represents the set The number of SKUs in each combination sub-block placed on the same shelf cannot exceed the capacity limit of the shelf; equations (21) and (22) represent The conditions for the value of ; Equations (23) and (24) respectively represent and The range of values ​​for .

[0065] Furthermore, the effectiveness of the above SKU shelving strategy is measured by the relative number of shelves required to satisfy all orders. The formula for calculating the relative number of shelves is expressed by equation (25):

[0066] (25)

[0067] in, The number of shelves obtained through empirical decision-making; The number of shelves is obtained using the method described above.

[0068] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0069] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0070] As can be seen from the above technical solution, this invention provides a SKU shelving method strategy for mobile robot warehousing systems to solve the problems of low picking efficiency and high operating costs in the aforementioned systems. The SKU shelving method strategy for mobile robot warehousing systems provided by this invention proposes an SKU shelving method from a globally optimal perspective. Compared with existing technologies, it can more rationally allocate shelving locations for SKUs, reduce the number of shelves required to complete all orders, and improve picking efficiency. Therefore, by applying the SKU shelving method strategy for mobile robot warehousing systems provided by this invention, the operating costs of e-commerce enterprises can be effectively reduced, and the service level of e-commerce enterprises can be improved. Overall, this invention has good practicality and operability, and the calculation method is simple, providing a new and efficient strategy for solving the SKU shelving problem in mobile robot warehousing systems.

[0071] Specifically, the SKU shelving method for mobile robot warehousing systems provided by this invention is based on a global optimum perspective. By applying this method, the optimal SKU shelving scheme can be obtained, significantly improving performance compared to numerous existing SKU shelving methods based on local optima. Furthermore, based on the global optimum property, this invention transforms the problem-solving process into a multi-stage assignment problem-solving process, proposing a simple and efficient algorithm. By continuously solving a simple assignment problem, the result of the proposed SKU shelving method can be obtained. The process is simple and easy to understand and apply. In addition, the algorithm proposed in this invention also has advantages such as fast solution efficiency and high result quality, enabling the calculation of the proposed SKU shelving method's result in a short time. Attached Figure Description

[0072] Figure 1 This is a flowchart of the SKU shelving method strategy for a mobile robot warehousing system provided in an embodiment of the present invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0074] like Figure 1 As shown in the figure, the SKU shelving method for a mobile robot warehousing system described in this embodiment is executed by computer equipment through the following steps:

[0075] Figure 1 The flowchart illustrates a SKU shelving method strategy for a mobile robot warehousing system provided by an embodiment of the present invention. Figure 1 As shown, the SKU listing method in this embodiment includes operations S110 to S140.

[0076] Operating S110 to acquire data information from the mobile robot warehousing system;

[0077] During the S120 operation, a SKU listing decision model is constructed based on the acquired data.

[0078] In operation S130, a calculation method for SKU listing strategy is constructed based on data information and SKU listing decision model;

[0079] In operation S140, the SKU shelving results of the mobile robot warehousing system are calculated according to the calculation method and then implemented.

[0080] According to an embodiment of the present invention, the above data information includes historical order quantity information, order content information, namely, SKU type information contained in each order, SKU type that needs to be allocated shelf space, available shelf quantity information, and shelf capacity limit information, namely, the maximum number of SKU types that can be placed in each shelf.

[0081] In this embodiment of the invention, the set of SKU types contained in all orders must be a subset of the set of SKU types that need to be allocated shelf positions.

[0082] According to an embodiment of the present invention, the above-mentioned construction of the SKU listing decision model includes: calculating the utility value between any two SKUs based on the acquired data information; and constructing the SKU listing decision model based on the utility value.

[0083] In this embodiment of the invention, the data information used to calculate the utility value includes order quantity information and order content information. According to an embodiment of the invention, the formula for calculating the above-mentioned utility value is expressed by equation (1):

[0084] (1)

[0085] in, , For any two SKUs; It is a SKU With SKU The utility value; It includes SKUs and The number of orders; This refers to the total number of orders mentioned above.

[0086] According to an embodiment of the present invention, the objective of the above SKU listing decision model is expressed by equation (2):

[0087] (2)

[0088] in, This refers to the number of available shelves mentioned above; It is a shelf The utility value.

[0089] According to an embodiment of the present invention, the SKU shelving decision model of the above-mentioned mobile robot warehousing system is constrained by the conditions expressed by equations (3) to (9):

[0090] (3)

[0091] (4)

[0092] (5)

[0093] (6)

[0094] (7)

[0095] (8)

[0096] (9)

[0097] in, This refers to the number of available shelves mentioned above; This refers to the capacity limitation of the aforementioned shelves; This refers to the number of SKU types that need to be allocated shelf space; The above SKUs With SKU The utility value; The aforementioned shelves The utility value; It is a 0-1 decision variable, when SKU The shelf location is on the shelf. If it is above, it equals 1; otherwise, it equals 0. It is a 0-1 decision variable, when SKU With SKU Their shelf positions are all on the shelves. If the value is 1, it equals 0; otherwise, it equals 0. Constraint (3) means that each SKU can only be stored in one location on one shelf; constraint (4) means that the number of SKU types stored on each shelf cannot exceed the shelf capacity; constraints (5) and (6) mean that... The value conditions; constraint condition (7) represents the shelf The method for calculating the utility value; constraints (8) and (9) give the 0-1 decision variables. , The range of values ​​for .

[0098] In this embodiment of the invention, the capacity limit of all available shelves can be set to be the same. The above-described SKU shelving decision model aims to find a shelving result that minimizes the number of shelves required to complete all orders, thereby improving the overall picking efficiency of the mobile robot warehousing system.

[0099] According to an embodiment of the present invention, the above calculation method includes: constructing an original assignment matrix based on the utility value; constructing a multi-stage dynamic assignment method based on the original assignment matrix; and correcting the result based on the result of the multi-stage dynamic assignment method.

[0100] According to an embodiment of the present invention, the original assignment matrix described above is represented by equation (10):

[0101] (10)

[0102] In this embodiment of the invention, the original matrix is ​​symmetric. A square matrix, where each row and column represents a SKU, and the same index value indicates the same SKU type. When Sometimes, ,when Sometimes, .

[0103] According to an embodiment of the present invention, the above-described multi-stage assignment method includes:

[0104] Initialization: The current matrix is Current utility value ;

[0105] Step 1: Perform an assignment calculation on the current matrix to obtain the utility value. Assignment results ;

[0106] Step Two: If If the assignment fails, the assignment will stop, and the final assignment result will be obtained. Otherwise, proceed to step three.

[0107] Step 3: Set the current utility value And based on the assignment results Update the current matrix Return to step one.

[0108] In this embodiment of the invention, any current matrix is ​​a symmetric square matrix, and each row and column of any current matrix represents a SKU combination, with identical index values ​​indicating identical combinations. Furthermore, in this embodiment of the invention, any initialized current matrix that satisfies the above requirements... After a limited number of assignments and updates, they will all reach [the desired outcome]. The conditions for stopping assignment.

[0109] According to an embodiment of the present invention, the above-mentioned assignment solution of the current matrix includes: the assignment solution method is represented by equations (11) to (15):

[0110] (11)

[0111] (12)

[0112] (13)

[0113] (14)

[0114] (15)

[0115] In this embodiment of the invention, the optimal assignment result can be obtained quickly using this assignment model. For the current matrix, the first... line, number The values ​​for the column; It is a 0-1 decision variable, when combined Assigned to the group The objective function (11) represents maximizing the assignment utility value; the constraint (12) represents each combination. It can only be assigned to one group Constraint (13) represents each combination It can only be assigned to one group Constraint (14) indicates that when the combination Assigned to the group When, combination It must also be assigned to the group at the same time. Constraint (15) gives the 0-1 decision variables. The range of values ​​for .

[0116] The above assignment results Combination of multiple This indicates that each combination Indicate combination With combination The union of included SKUs, in practical terms, means that the SKUs in these combinations are placed on the same shelf.

[0117] According to an embodiment of the present invention, the above-described update of the current matrix Includes: updating the current matrix Number of rows and columns; update the current matrix. The values ​​of the newly added rows and columns.

[0118] The rules for updating the number of rows and columns are as follows: For the assignment results... Each combination Perform: If the combination With the current matrix If no combination of any two elements is identical, then the combination is... Update to the current matrix respectively The rows and columns are used as the new SKU combination; otherwise, the combination is not updated. .

[0119] The update of the current matrix The formula for calculating the values ​​of the newly added rows and columns is expressed by equation (16):

[0120] (16)

[0121] in, It is a combination With combination The number of SKU types contained in the union of the sets.

[0122] According to an embodiment of the present invention, the above-mentioned correction method includes: assigning the result... The included combinations are divided into several combination sub-blocks, such that no two sub-blocks have duplicate SKUs, and combination sub-blocks that appear only in one combination are assigned to a set. The combination sub-blocks that appear in multiple combinations are divided into sets. ; Calculate the utility values ​​between combined sub-blocks; Construct a correction model and perform correction calculations.

[0123] In embodiments of the present invention, for example, the assignment result Given two combinations {A, B} and {B, C}, after partitioning, three sub-blocks {A}, {B}, and {C} are generated. Sub-blocks {A} and {C} are assigned to sets. The combined sub-block {B} is divided into sets. .

[0124] According to an embodiment of the present invention, the formula for calculating the utility value between combined sub-blocks is expressed by equation (17):

[0125] (17)

[0126] in, It is a composite sub-block and The utility value, The above SKUs With SKU The utility value.

[0127] According to an embodiment of the present invention, the above-mentioned modified model is represented by equations (18) to (24):

[0128] (18)

[0129] (19)

[0130] (20)

[0131] (twenty one)

[0132] (twenty two)

[0133] (twenty three)

[0134] (twenty four)

[0135] in, This refers to the capacity limitation of the aforementioned shelves; This is the number of SKUs contained in the combined sub-block; It is a 0-1 decision variable, when the combination sub-block and The value is 1 if the included SKUs are placed on the same shelf, and 0 otherwise. It is a 0-1 decision variable, when the combination sub-block , and The set is equal to 1 if the included SKUs are placed on the same shelf, and equal to 0 otherwise. Equation (19) represents the set. Each composite sub-block in the set can only be combined with the set. A composite sub-block Placed on the same shelf; Equation (20) represents the set The number of SKUs in each combination sub-block placed on the same shelf cannot exceed the capacity limit of the shelf; equations (21) and (22) represent The conditions for the value of ; Equations (23) and (24) respectively represent and The range of values ​​for .

[0136] In this embodiment of the invention, the difference between the modified model and the SKU shelf-listing decision model described above lies in that it considers a single SKU... Replacing it with SKU combination sub-blocks, the meanings of other expressions remain basically the same, but because the problem size is reduced, the model can solve it quickly. By using the above multi-stage dynamic assignment method, the size of the problem requiring SKU placement decisions can be effectively reduced, enabling the model to solve it quickly.

[0137] According to an embodiment of the present invention, the effectiveness of the above-mentioned SKU shelving strategy is measured by the relative number of shelves required to satisfy all orders. The formula for calculating the relative number of shelves is expressed by equation (25):

[0138] (25)

[0139] in, The number of shelves obtained through empirical decision-making; The number of shelves is obtained by the method according to the present invention.

[0140] To more intuitively demonstrate how this invention can reduce the number of shelves required to fulfill orders in a mobile robot warehousing system, this embodiment uses the shelving method of a leading company's actual mobile robot warehousing system as a comparison benchmark. This company's mobile robot warehousing system currently employs the following empirical SKU shelving scheme: first, SKUs of the same type are placed on the same shelf, and then related SKUs are placed on the same shelf. This embodiment uses the percentage difference in the number of shelves required to complete the same batch of orders between the empirical shelving method and the method proposed in this invention as an indicator (as shown in equation (25)) to measure the beneficial effects of this invention. Table 1 shows the comparison results of the relative required number of shelves:

[0141]

[0142] In summary, the SKU shelving method for a mobile robot warehousing system in this embodiment of the invention first calculates the utility value between SKUs to be assigned shelving positions based on historical order data. Then, a multi-stage assignment method is implemented based on the utility value, and the results are corrected to obtain the SKU shelving scheme. Comparison with results from empirical decision-making methods shows that the method in this embodiment can effectively reduce the number of shelves required to fulfill orders and improve the order picking efficiency of enterprises.

[0143] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0144] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0145] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the SKU shelving methods for mobile robot warehousing systems described above.

[0146] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0147] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus.

[0148] Memory, used to store computer programs;

[0149] When the processor executes the program stored in the memory, it implements the above-described SKU shelving method for a mobile robot warehousing system, the method comprising:

[0150] Acquire data information from the mobile robot warehousing system;

[0151] Based on the acquired data, a decision-making model for SKU listing is constructed.

[0152] Based on data information and SKU listing decision models, a calculation method for SKU listing strategies is constructed.

[0153] Based on the calculation method, calculate the SKU shelving results of the mobile robot warehousing system and implement it.

[0154] The communication bus mentioned in the aforementioned electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0155] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0156] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0157] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0158] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0159] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0160] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0161] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for SKU shelving in a mobile robot warehousing system, characterized in that, Includes the following steps, Acquire data information from the mobile robot warehousing system; Based on the acquired data, a decision-making model for SKU placement is constructed, including: calculating the utility value between any two SKUs based on the acquired data; and constructing the SKU placement decision-making model based on the utility value. The formula for calculating the utility value between the two SKUs is expressed by equation (1): （1） in, , For any two SKUs; It is a SKU With SKU The utility value; It includes SKUs and The number of orders; This is the total number of orders; The objective of the SKU listing decision model is expressed by equation (2): （2） in, This refers to the number of available shelves; It is a shelf The utility value; The SKU listing decision model is constrained by the conditions expressed in equations (3) to (9): （3） （4） （5） （6） （7） （8） （9） in, This refers to the number of available shelves; This refers to the capacity limitation of the aforementioned shelf; This refers to the number of SKU types that need to be allocated shelf space; The SKU With SKU The utility value; The shelf mentioned above The utility value, It is a 0-1 decision variable, when SKU The shelf location is on the shelf. If it is above, it equals 1; otherwise, it equals 0. It is a 0-1 decision variable, when SKU With SKU Their shelf positions are all on the shelves. When the value is 1, it equals 0; constraint (3) means that each SKU can only be stored in one location on one shelf; constraint (4) means that the number of SKU types stored on each shelf cannot exceed the capacity of the shelf; constraints (5) and (6) restrict The value conditions; constraint condition (7) represents the shelf The method for calculating the utility value; constraints (8) and (9) give the 0-1 decision variables. , The range of values ​​for ; Based on data information and SKU listing decision models, a calculation method for SKU listing strategies is constructed. The calculation method includes: constructing an original assignment matrix based on the utility values ​​between the two SKUs; constructing a multi-stage dynamic assignment method based on the original assignment matrix; and correcting the results based on the results of the multi-stage dynamic assignment method. Based on the calculation method, calculate the SKU shelving results of the mobile robot warehousing system and implement it.

2. The SKU shelving method for a mobile robot warehousing system according to claim 1, characterized in that: The data information acquired from the mobile robot warehousing system includes historical order quantity information, order content information, namely the inventory unit type information (SKU type information) contained in each order, the SKU types that need to be allocated shelf locations, the available shelf quantity information, and shelf capacity limit information, namely the maximum number of SKU types that can be placed in each shelf.

3. The SKU shelving method for a mobile robot warehousing system according to claim 1, characterized in that, The original assignment matrix is ​​represented by equation (10): （10） In this matrix, each row and column represents a SKU, and the same index value indicates the same type of SKU; when Sometimes, ,when Sometimes, .

4. The SKU shelving method for a mobile robot warehousing system according to claim 1, characterized in that, The multi-stage dynamic assignment method includes: Initialization: Set the current matrix to... Current utility value ; Step 1: Perform an assignment calculation on the current matrix to obtain the utility value. and assignment results ; Step Two: If If the assignment fails, the assignment will stop, and the final assignment result will be obtained. Otherwise, proceed to step three. Step 3: Set the current utility value And based on the assignment results Update the current matrix Return to step one.

5. The SKU shelving method for a mobile robot warehousing system according to claim 4, characterized in that, The process of assigning and solving the current matrix includes: Each row and column of the current matrix represents a SKU combination, and the same index value indicates that the combination is the same; The method for solving the assignment problem is represented by equations (11) to (15): （11） （12） （13） （14） （15） in, For the current matrix, the first... line, number The value corresponding to the column; It is a 0-1 decision variable, when combined Assigned to the group The value is 1 if the condition is met, and 0 otherwise; the objective function (11) represents maximizing the assignment utility value; the constraint (12) represents each combination It can only be assigned to one group Constraint (13) represents each combination It can only be assigned to one group Constraint (14) indicates that when the combination Assigned to the group When, combination It must also be assigned to the group at the same time. Constraint (15) gives the 0-1 decision variables. The range of values ​​for ; The assignment result Combination of multiple This indicates that each combination Indicate combination With combination The union of included SKUs, in practical terms, means that the SKUs in these combinations are placed on the same shelf.

6. The SKU shelving method for a mobile robot warehousing system according to claim 4, characterized in that, The update of the current matrix Includes: updating the current matrix Number of rows and columns; update the current matrix. The values ​​of the newly added rows and columns; The rules for updating the number of rows and columns are as follows: For the assignment results... Each combination Perform: If the combination With the current matrix If no combination of any two elements is identical, then the combination is... Update to the current matrix respectively The rows and columns are used as the new SKU combination; otherwise, the combination is not updated. ; The update of the current matrix The formula for calculating the values ​​of the newly added rows and columns is expressed by equation (16): （16） in, It is a combination With combination The number of SKU types contained in the union of the sets.

7. The SKU shelving method for a mobile robot warehousing system according to claim 1, characterized in that, The correction method includes: adjusting the assignment result The included combinations are divided into combination sub-blocks, with no duplicate SKUs between any two sub-blocks. Combination sub-blocks that appear only in one combination are assigned to a set. Combination sub-blocks appearing in multiple combinations are divided into sets. ; Calculate the utility values ​​between combined sub-blocks; Construct a correction model and perform correction calculations.

8. The SKU shelving method for a mobile robot warehousing system according to claim 7, characterized in that, The formula for calculating the utility value between combined sub-blocks is expressed by equation (17): （17） in, It is a composite sub-block and The utility value, The SKU With SKU The utility value.

9. The SKU shelving method for a mobile robot warehousing system according to claim 7, characterized in that, The modified model is represented by equations (18) to (24): （18） （19） （20） （21） （22） （23） （24） in, This refers to the shelf capacity limitation; This is the number of SKUs contained in the combined sub-block; It is a 0-1 decision variable, when the combination sub-block and The value is 1 if the included SKUs are placed on the same shelf, and 0 otherwise. It is a 0-1 decision variable, when the combination sub-block , and The value is equal to 1 if the included SKUs are placed on the same shelf, and equal to 0 otherwise; the objective function (18) represents maximizing the utility value; equation (19) represents the set Each composite sub-block in the set can only be combined with the set. A composite sub-block Placed on the same shelf; Equation (20) represents the set The number of SKUs in each combination sub-block placed on the same shelf cannot exceed the capacity limit of the shelf; equations (21) and (22) represent The conditions for the value of ; Equations (23) and (24) respectively represent and The range of values ​​for .

10. The SKU shelving method for a mobile robot warehousing system according to claim 1, characterized in that, The effectiveness of the SKU placement strategy is measured by the relative number of shelves required to satisfy all orders, where the formula for calculating the relative number of shelves is expressed by equation (25): （25） in, The number of shelves obtained through empirical decision-making; The number of shelves is the number obtained by the method according to any one of claims 1 to 9.

11. A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 10.

Images