Order batching and picking sequencing method and system for multi-picking station based on similarity

Abstract

This disclosure provides a similarity-based method and system for order batching and picking sorting for multiple picking stations. The method includes calculating the similarity of each order, determining whether different orders contain the same goods, and batching orders according to the objective of maximizing similarity. A mathematical model is constructed with the objective of minimizing the total number of goods entering and leaving the warehouse, and the picking order for different batches of orders is determined. The method fully considers the situation where the same goods appear in multiple orders, calculates the similarity of items in different orders, batches orders according to the principle of maximizing item similarity, and simultaneously sorts the picking order of different batches of orders with the objective of minimizing the number of goods entering and leaving the warehouse, thus determining a reasonable picking order and reducing operating costs.

Description

Technical Field

[0001] This disclosure relates to the field of order batching and picking, specifically to a method and system for order batching and picking sorting for multiple picking stations based on similarity. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Currently, B2C (B2C refers to a model of e-commerce) e-commerce orders are characterized by multiple varieties, small batches, and high frequency. Traditional order batching and picking methods can no longer meet current needs. Circular shuttle systems, as a highly effective "goods-to-person" order picking and replenishment system, are increasingly widely used. Current research on circular shuttle picking systems focuses primarily on achieving high efficiency, resulting in high energy consumption. Furthermore, research on circular shuttle picking systems mainly focuses on single-station order picking operations, with limited research on multi-station collaborative operations.

[0004] In e-commerce order processing, order batching and picking are crucial components of the warehousing process, significantly impacting order fulfillment efficiency. Before picking, orders within a given time window are pre-processed, determining the picking pattern. Current picking patterns batch and sort orders according to their order placement time and picking station capacity. However, this approach has drawbacks. When different orders contain the same item, the order placement times may not be adjacent, leading to repeated inbound and outbound movements of the same product. This results in high equipment occupancy and low picking efficiency. Treating each order as an isolated entity ignores the possibility of multiple orders containing the same product, neglecting the interconnectedness of orders and lacking a holistic view of order relationships. This increases the number of inbound and outbound movements, causing unnecessary equipment waste, increased operations, longer picking times, and overall inefficiency. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure proposes a similarity-based order batching and picking sorting method and system for multiple picking stations. Targeting the characteristics of orders with multiple varieties, small batches, and high frequency, it fully considers the situation where the same product appears in multiple orders. It calculates the similarity of product items across different orders, batches orders according to the principle of maximizing item similarity, and simultaneously sorts the picking order of different batches of orders with the goal of minimizing the number of inbound and outbound operations, thus determining a reasonable picking order and reducing operating costs.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] Similarity-based order batching and picking sorting methods for multi-picking stations include:

[0008] Obtain different order data within a certain time window, and divide each product in each order data;

[0009] Calculate the similarity of each order to determine whether the same products are contained in different orders, and then batch the orders according to the target with the highest similarity.

[0010] A mathematical model is constructed with the goal of minimizing the total number of goods entering and leaving the warehouse, and the picking order for different batches of orders is determined by solving the model.

[0011] Based on the determined picking order of different orders, the circular multiple picking stations work together to complete the picking of the orders.

[0012] According to other embodiments, the present disclosure adopts the following technical solutions:

[0013] A similarity-based order batching and picking sorting system for multiple picking stations includes a control center and a loop system. The control center includes:

[0014] The data processing center is used to acquire different order data within a certain time window and divide each product in each order data.

[0015] The data computing center is used to calculate the similarity of each order, determine whether different orders contain the same products, and batch orders according to the goal of maximizing similarity; and to build a mathematical model with the goal of minimizing the total number of goods entering and leaving the warehouse, and solve to determine the picking order of different batches of orders.

[0016] The ring-through system is connected to the control center for communication, and is used to make its multiple ring-shaped picking stations work together to complete the picking of orders according to the determined picking order of different orders.

[0017] Furthermore, the circumferential system includes:

[0018] The circular shuttle operates on a fixed circular track, automatically handling the entry and exit of goods and transporting them to designated locations.

[0019] Multiple picking stations are areas used for picking goods.

[0020] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0021] This disclosure establishes an optimization for order batching. First, based on order data, the similarity between any two orders is calculated, providing data support for subsequent order batching and sorting. An order batching optimization model is constructed, and the effectiveness of the order sorting model is verified based on similarity. The optimized batches show high similarity and strong correlation between orders, reducing the number of times the same goods are entered and exited within an order, improving picking efficiency and reducing operating costs.

[0022] This disclosure optimizes multi-picking station collaborative operation modes. For situations where different picking stations may share the same cargo box, it constructs an order picking sequence optimization model suitable for multi-picking station collaborative operation modes. Based on the order batching results, by solving the model, the total number of cargo box inbound and outbound operations is significantly reduced. Attached Figure Description

[0023] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0024] Figure 1 Here is a flowchart illustrating the implementation of the method disclosed herein;

[0025] Figure 2 This is a flowchart of the outbound operation of the circular system disclosed in this publication;

[0026] Figure 3 This is a flowchart of the warehousing operation of the circular system disclosed in this publication;

[0027] Figure 4 This is a layout plan of the ring-through system disclosed herein.

[0028] Among them, A. Shelving, B. Stacker crane, C. Outbound conveyor, 1-8. Inbound conveyor, 9-12. Picking station; Detailed implementation method:

[0029] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0030] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0031] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0032] To address the characteristics of e-commerce orders—multiple varieties, small batches, and high frequency—the system fully considers the possibility of the same product appearing in multiple orders. If a product included in the current order appears in a subsequent order to be picked, that product can be temporarily withheld from the warehouse until the subsequent order no longer contains that product, at which point it is then put into storage. Within a single time window, many orders are divided into multiple batches. The different picking orders within each batch will result in different inbound and outbound situations, leading to varying numbers of inbound and outbound transactions. For example, if order 1 contains item number 99, and order 2 also contains item number 99, if the two orders are in the same batch, they can be picked sequentially. If they are not in the same batch, the two batches are sorted sequentially during the order picking process. After item number 99 in order 1 is picked, it is not immediately returned to the warehouse; when picking order 2, item number 99 can be directly retrieved from the circular track, thus reducing one inbound and outbound transaction.

[0033] Example 1

[0034] One embodiment of this disclosure presents a similarity-based method for order batching and picking sorting across multiple picking stations, such as... Figure 1 As shown, it includes:

[0035] Step 1: Obtain different order data within a certain time window, and divide each product in each order data;

[0036] Step 2: Calculate the similarity of each order to determine whether the same products are contained in different orders, and then batch the orders according to the target with the highest similarity.

[0037] Step 3: Construct a mathematical model with the goal of minimizing the total number of goods entering and leaving the warehouse, and solve it to determine the picking order for different batches of orders;

[0038] Step 4: Based on the determined picking order of different orders, make the circular multi-picking stations work together to complete the order picking.

[0039] To acquire order data from e-commerce platforms within a specific timeframe, considering the possibility of identical products across different orders, if conditions permit, the product can be temporarily withheld from the warehouse until subsequent orders no longer contain it, at which point it can be returned to the warehouse. The similarity of product items across different orders is calculated, and orders are batched based on the principle of maximizing item similarity. Simultaneously, the picking order for different batches of orders is optimized by minimizing the number of inbound and outbound operations to determine a reasonable picking order. After acquiring the order data for a specific timeframe, this data is preprocessed by segmenting each product within each order to obtain basic information about the products within each order.

[0040] Then, the orders are divided into batches. When dividing the orders into batches, the number of orders in the same batch cannot exceed the capacity of the picking station, and each order can only be assigned to the same batch.

[0041] Orders in the same batch are stored in one order box. There are no volume or weight restrictions. Orders cannot be split and must be picked in one go. There is no order insertion.

[0042] In step 2, the similarity of each order is calculated to determine whether the same products are contained in different orders, and the orders are batched according to the target with the highest similarity.

[0043] Specifically, order batching based on item similarity refers to the degree of similarity between order information, which includes the ordered items and quantities. The similarity is calculated based on the order information, and orders with high similarity are grouped into the same batch. During picking, items with the same similarity are picked together, reducing the number of times the same goods are handled in and out of the warehouse and improving picking efficiency. This disclosure uses item similarity as the basis for order batching.

[0044] The commonly used methods for calculating item similarity are as follows:

[0045] Let α ij β represents the number of items contained in both order i and order j. ij γ represents the number of items included in order i but not in order j. ij This represents the number of items that are included in order j but not in order i.

[0046] Method 1:

[0047]

[0048] Method 2:

[0049]

[0050] Method 3:

[0051]

[0052] For example, order m1 contains goods items k1, k2, and k3, and order m2 contains goods items k2, k3, and k4. Then, the goods items that orders m1 and m2 together contain are k2 and k3, with a quantity of 2. The two orders together contain k1, k2, k3, and k4, with a total quantity of 4. Each of the two orders contains one unique item.

[0053] Using method one, the order similarity between orders m1 and m2 is: Solving using method two, then Calculate using method three

[0054] For the three commonly used methods for calculating item similarity mentioned above, considering potential drawbacks—for example, order 1 requires item 2, order 2 requires items 1 and 2, and order 3 requires items 1, 2, 3, and 4—if the similarity between orders 1 and 2 and between orders 2 and 3 are calculated separately, regardless of the method used, the final result will be Z. 12 =Z 23 There is no difference. However, since the quantity of each order batch is limited, and the ultimate goal is to minimize the number of inbound and outbound operations, it is necessary to consider minimizing the number of inbound and outbound operations when different orders are grouped together. When orders 1 and 2 are grouped into one batch, one less inbound and outbound operation can be reduced, and when orders 2 and 3 are grouped into one batch, two less inbound and outbound operations can be reduced. Therefore, if a batch can only contain two orders, orders 2 and 3 should be assigned to the same batch. The original three methods for calculating similarity are not suitable for the model that minimizes the number of inbound and outbound operations because the calculation results are indistinguishable. Therefore, a new method for calculating order similarity is proposed, namely:

[0055] Let α ij β represents the number of items contained in both order i and order j. ij γ represents the number of items included in order i but not in order j. ij Let represent the number of items that order j contains but order i does not. The formula for calculating the similarity between orders is:

[0056]

[0057] Where i and j represent any two different orders.

[0058] For an example using a commonly used order similarity calculation formula, the new order similarity calculation formula described above will be used to calculate the similarity of orders 1 and 2, and orders 2 and 3 respectively. Z 12 =1 / 2, Z 23 =2 / 3, Z 12 ≠Z 23 And Z 23 >Z 12 Z in the above example 23 and Z 12 To distinguish them, when a batch can contain only two orders, order 2 and order 3 are assigned to the same batch.

[0059] By using an improved method for calculating the similarity of order items, the similarity of all orders is calculated separately, and the orders are batched according to the calculation results. This method is closely linked to the order batching optimization model, which can effectively model and solve the order batching problem.

[0060] Define the objective function that maximizes order similarity:

[0061]

[0062] Among them, d n Z represents the similarity of order batches n. ij b represents the similarity between order i and order j. ij Let M represent the total number of orders, and let M represent the decision variable.

[0063] The constraints are:

[0064]

[0065]

[0066] b ij ∈{0,1},i,j=1,..,M;i≠j (8)

[0067] x in ∈{0,1},i=1,...,M; n=1,...,N (9)

[0068] x jn ∈{0,1},j=1,...,M; n=1,...,N (10)

[0069] Formula (5) is the objective function, indicating that the order similarity across all batches is maximized, and the order similarity within a batch is equal to the sum of the similarities of all orders within that batch. Formula (6) indicates that any two orders are either assigned to the same batch or to different batches. Formula (7) indicates that the number of orders in each batch should be less than or equal to the box capacity set at the picking station. Formula (8) is the decision variable, indicating whether order i and order j are assigned to the same batch; if they are in the same batch, b ij =1, otherwise b ij =0. Formula (9) represents the decision variable, x in This indicates whether order i is in batch n. If batch n contains order i, then x... in =1, otherwise equal to 0. Formula (10) is the decision variable, x jn This indicates whether order j is assigned to batch n; if so, then x... jn =1, otherwise x jn =0. M represents the total number of orders.

[0070] Based on the total number of orders M within a certain period, after batching according to the above model, the number of order batches is: p represents the capacity of the picking station; the order picking sequence is optimized according to the batch results. Different picking sequences correspond to different goods entry and exit situations. Therefore, the order picking sequence is optimized according to the order information between orders, so that when different orders in the same batch contain the same product, the product is not immediately returned to the warehouse after the previous order is picked, but waits for the next order to pick directly, without the need for the goods to be put back into the warehouse, thus reducing the number of goods entry and exit.

[0071] Step 3 mentions that a mathematical model is constructed with the goal of minimizing the total number of goods entering and leaving the warehouse, and the picking order for different batches of orders is determined by solving the model.

[0072] Since this disclosure uses a circular multi-picking station for picking goods, it is crucial to ensure a balanced distribution of orders across all picking stations during the picking operation. Simultaneously, as the number of picking stations increases, when different orders share a single cargo box, the number of circular shuttles carrying cargo boxes that will not be returned to the warehouse must be minimized to ensure that idle shuttles are available for other operations. The number of batches processed at each picking station is... If there are any remaining batches, they are divided sequentially according to the picking station order. The difference in the number of orders picked between each picking station in a circular multi-picking system should be as small as possible. Furthermore, the picking order for each batch is fixed, and there can only be one picking order.

[0073] A mathematical model is defined to minimize the total number of inbound and outbound operations of goods, while also minimizing the difference in the number of orders picked between each picking station. The model is as follows:

[0074]

[0075]

[0076] Where c represents the picking station, c = 1, ..., C; Q represents the total number of times the goods box is handled; s un Indicates whether the picking order of batch n is u; y ln Indicates whether batch n allows carton l to be shipped out; a cn Indicates whether batch n was picked by picking station c;

[0077] The constraints are as follows:

[0078]

[0079]

[0080] g ln ≤t ln ,l=1,...,L; n=1,...,N (15)

[0081]

[0082] t ln =g l,n-1 +y ln ,l=1,...,L; n=2,...,N (17)

[0083]

[0084] y l1 =t l1 ,l=1,...,L (19)

[0085] y ln ∈{0,1},l=1,...,L; n=1,...,N (20)

[0086] s nu ∈{0,1},n=1,...,N; u=1,...,U (21)

[0087] a cn ∈{0,1},c=1,...,C; n=1,...,N (22)

[0088] t ln ∈{0,1},l=1,...,L; n=1,...,N (23)

[0089] g ln ∈{0,1},l=1,...,L; n=1,...,N (24)

[0090] Among them, the objective function formula (11) is to minimize the number of inbound and outbound trips of cartons when picking orders in all batches. Formula (12) indicates that the difference in the number of orders picked between each picking station should be minimized as much as possible. Formula (13) indicates that if different orders between multiple picking stations contain the same goods, it is determined whether the goods need to be returned to the warehouse. If the number of goods is less than the ratio of the number of shuttle cars to the number of picking stations, they are not returned to the warehouse, that is, to ensure that there are always idle shuttle cars to carry out the following picking work. Formula (14) indicates that the picking order of each batch is determined, and there can only be one picking order. Formula (15) indicates that the number of cartons placed in the buffer area should be less than or equal to the number of cartons required for the batch. Formula (16) indicates that if the subsequent batch does not need the goods contained in the previous batch, the goods are returned to the warehouse. Formula (17) indicates that the cartons required for each order come from the cartons that are out of the warehouse in this batch, or they may be cartons from other batches picked by other picking stations and placed in the buffer area. Formula (18) indicates that only one picking station can be responsible for picking for each batch. Formula (19) indicates that all the boxes needed for the first batch come from the boxes that have been shipped out. Formula (20) is the decision variable, indicating whether batch n needs to have box l shipped out; if so, then y ln =1, otherwise the value is 0. Formula (21) is the decision variable, indicating whether the picking order of batch n is u. If it is, the value is 1, otherwise the value is 0. Formula (22) is the decision variable, indicating whether batch n is picked by picking station c. If it is, then a cn =1 otherwise a cn =0. Formula (23) is the decision variable, representing whether bin l is needed when picking batch n. If so, then t ln =1, otherwise t ln =0. Formula (24) is the decision variable, indicating whether bin l enters the circular buffer area after batch n is picked. If so, then g ln =1, otherwise g ln =0.

[0091] Example 2

[0092] One embodiment of this disclosure provides a similarity-based order batching and picking sorting system for multiple picking stations, including a control center and a loop system. The control center includes:

[0093] The data processing center is used to acquire different order data within a certain time window and divide each product in each order data.

[0094] The data computing center is used to calculate the similarity of each order, determine whether different orders contain the same products, and batch orders according to the goal of maximizing similarity; and to build a mathematical model with the goal of minimizing the total number of goods entering and leaving the warehouse, and solve to determine the picking order of different batches of orders.

[0095] The ring-through system is connected to the control center for communication, and is used to make its multiple ring-shaped picking stations work together to complete the picking of orders according to the determined picking order of different orders.

[0096] The circumferential system includes:

[0097] The circular shuttle operates on a fixed circular track, automatically handling the entry and exit of goods and transporting them to designated locations.

[0098] Multiple picking stations are areas used for picking goods.

[0099] It also includes stacker cranes, which generally use forks as picking devices to realize horizontal handling, vertical lifting and lowering of goods, and picking and placing of goods on high-rise shelves.

[0100] Specifically, e-commerce companies integrate order information and send it to the warehouse information system. After receiving the order information, the warehouse assigns the order tasks to designated shuttles for goods in and out of the warehouse. Finally, picking personnel complete the picking task for the ordered goods. The entire process can be roughly divided into four stages: order generation, order batching, order sorting, and order picking completion.

[0101] Phase 1: Order Picking Generation. After a customer places an order, the e-commerce company determines the shipping warehouse based on the customer's order information, generates order picking information, and sends the order information to the warehouse that needs to ship the goods.

[0102] Phase Two: Order Batching. After receiving order information, the warehousing system generally batches orders according to the order placement time. Considering the possibility of identical goods in different orders, an improved method is used to calculate the similarity between orders. Then, based on the order similarity calculation results, orders with high similarity are assigned to the same batch as much as possible.

[0103] Phase 3: Order Sorting. Based on the order batching results, orders within the same batch are sorted according to the principle of minimizing the number of inbound and outbound operations to determine the order of goods entering and leaving the warehouse.

[0104] Phase 4: Order Picking. The boxes containing the required goods for the order are moved to the picking station. Pickers select the necessary goods according to the order details, and the picking process is complete.

[0105] The circular shuttle mainly consists of a frame, drive wheels, follower wheels, bumpers, conveyor, wireless communication system, electrical components, and various covers. The main systems are as follows:

[0106] (1) Power supply system

[0107] The circular shuttle car uses a sliding contact line to supply power to the trolley. Compared to traditional cable power supply, which requires a cable chain to protect the cable as it moves back and forth with the RGV (Remote Transport Vehicle) to prevent friction between the cable and equipment or the ground, the sliding contact line method supplies power through contact between the sliding line and the current collector. This avoids the drawbacks of cable power supply and is also simple in structure and easy to install.

[0108] (2) Control System

[0109] The control system is a key system for the operation of the circular shuttle, mainly including speed control, position control, and direction control. The controller of the circular shuttle is a PLC, responsible for dynamically planning the shuttle's operation to ensure safe and efficient operation. For direction control, a dual-addressing method is used, adjusting the shuttle's speed and position based on the relative positions of the cargo and other shuttles.

[0110] (3) Self-testing system

[0111] To enable the control of the circular shuttle in different work locations and ensure that the shuttle completes its tasks safely and efficiently, the circular shuttle is equipped with multiple detection methods. By detecting various parameters such as the appearance of the goods, the weight of the goods, the direction of travel, and the operating status of the positional equipment, the shuttle can be dynamically planned in real time to provide the best solution for the system's operation and ensure the stable operation of the system.

[0112] (4) Data Communication

[0113] The circular shuttle system employs a unique communication method, with optical communication facilities installed both on the ground and on the shuttle itself to enable information communication between the various devices within the system. The optical communication equipment connects to both the circular shuttle and the PLC on the ground. The controllers between the devices form a hierarchical relationship, transmitting information between higher and lower-level devices through data packetization. The back-end control computer is also connected to the highest-level PLC, thereby dynamically scheduling the shuttles through the system.

[0114] (5) Security protection system

[0115] The circular shuttle's operating motor is equipped with thermal protection and overcurrent protection measures to ensure that the motor will not be damaged under any circumstances. The circular shuttle also has an emergency stop mechanism to prevent collisions.

[0116] In addition, anti-collision protection devices are installed at both ends of the circular track, and bumpers are installed at the front and rear of the shuttle car. The car body is also equipped with an emergency stop button. At the same time, the shuttle car will immediately issue an alarm when the equipment is in an abnormal state.

[0117] In this disclosure, after the order data is sorted and picked in batches using the method described in the embodiments, the loop-through system is controlled to perform outbound operations, such as... Figure 2 As shown, the system transmits order information to the stacker crane in the warehouse. The stacker crane moves to the goods based on the storage location information, and the pallet or tote box is transported onto the stacker crane via a conveyor. The stacker crane then transports the goods to the corresponding outbound conveyor belt. The system selects an idle and appropriately positioned shuttle vehicle (RGV) to perform the outbound operation based on the RGV's operational status. If no RGV is available, the goods continue to wait at the outbound gate. When the designated RGV arrives at the outbound gate, it sends a task signal. The chain conveyor transports the goods from the outbound gate onto the RGV. The RGV, loaded with goods, runs along a circular track to the designated picking area, where picking personnel perform the final picking task, simultaneously verifying the task list and completing the outbound operation.

[0118] Control the loop-through system to perform outbound operations, such as Figure 3 As shown, after the goods arrive at the warehouse, picking personnel will palletize them, and the system will assign storage locations to the goods that need to be stored. The handling equipment will move the palletized goods to the conveyor at the receiving station. Picking personnel will inspect and verify the goods against the inventory list. After verification, the goods will wait on the conveyor for storage. The system will issue a storage instruction and simultaneously assign an idle RGV in the loop system to perform the handling task. After receiving the handling task, the RGV will travel to the conveyor at the receiving station and send a task instruction to the conveyor. The conveyor will then transport the goods to the RGV. The RGV will continue running on the track after receiving the goods, reaching the warehouse entrance conveyor belt. The shuttle will then transport the goods via the conveyor belt to the warehouse entrance conveyor belt. Simultaneously, the system will issue a storage operation instruction to the stacker crane. After receiving the instruction, the stacker crane will fork up the goods and transport them to the storage location assigned by the system.

[0119] During operation, when the circular shuttle approaches the task location, the system automatically confirms the deceleration position and stopping position through various sensors. At the same time, the control system issues corresponding deceleration and stopping task commands. The shuttle stops accurately at the designated location, and the RGV's conveyor transports goods from the outbound point to the cart, or transports goods on the cart to the picking area.

[0120] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

[0125] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A similarity-based method for order batching and picking sorting across multiple picking stations, characterized in that, include: Obtain different order data within a certain time window, and divide each product in each order data; Calculate the similarity of each order to determine whether the same products are contained in different orders, and then batch the orders according to the target with the highest similarity. The method for calculating the order similarity is as follows: （1） i and j represent any two different orders, α ij β represents the number of items contained in both order i and order j; ij γ represents the number of items included in order i but not in order j. ij This represents the number of items included in order j but not in order i; The order picking order is optimized based on the batch results. Different picking orders correspond to different goods entering and leaving the warehouse. When different orders in the same batch contain the same product, the product is not immediately returned to the warehouse after the previous order is picked. Instead, it waits for the next order to pick directly, without the need for the goods to enter and leave the warehouse again. A mathematical model is defined to minimize the total number of inbound and outbound operations of goods, while also minimizing the difference in the number of orders picked between each picking station. The model is as follows: （2） （3） （4） （5） （6） （7） （8） （9） （10） （11） （12） （13） （14） （15） Where c represents the picking station, c=1,...C; Q Indicates the total number of times the cargo box was handled; s nu Indicates whether the picking order of batch n is u; Indicates whether batch n allows carton l to be shipped out; a cn Indicates whether batch n is picked by picking station c. Objective function (2) is to minimize the number of inbound and outbound trips of the goods when picking orders within all batches; (3) indicates that the difference in the number of orders picked between each picking station should be as small as possible; (4) indicates that if different orders between multiple picking stations contain the same goods, it is determined whether the goods need to be returned to the warehouse. If the quantity of goods is less than the ratio of the number of shuttle cars to the number of picking stations, they are not returned to the warehouse, that is, to ensure that there are always idle shuttle cars to carry out the following picking work; (5) indicates that the picking order of each batch is determined and there can only be one picking order. (6) indicates that the number of boxes placed in the buffer area should be less than or equal to the number of boxes required for this batch; (7) indicates that if subsequent batches do not need the goods contained in the previous batch, then the goods will be returned to the warehouse; (8) indicates that the boxes required for each order come from the boxes that are shipped out of the current batch, or boxes from other batches picked by other picking stations and placed in the buffer area; (9) indicates that only one picking station can be responsible for picking for each batch; (10) indicates that all the boxes required for the first batch come from the boxes that are shipped out; (11) is a decision variable, indicating whether batch n needs to have box l shipped out, if so... =1, otherwise the value is 0; (12) is a decision variable, indicating whether the picking order of batch n is u. If it is, the value is 1, otherwise the value is 0; (13) is a decision variable, indicating whether batch n is picked by picking station c. If it is, then acn=1, otherwise acn=0; (14) is a decision variable, indicating whether the material box l is needed when picking batch n. If it is needed, then =1, otherwise =0; (15) is the decision variable, indicating whether the bin l enters the circular buffer area after the batch n is picked. If yes, then =1, otherwise =0; A mathematical model is constructed with the goal of minimizing the total number of goods entering and leaving the warehouse, and the picking order for different batches of orders is determined by solving the model. Based on the determined picking order of different orders, the circular multiple picking stations work together to complete the picking of the orders; During operation, when the circular shuttle approaches the task location, the system automatically confirms the deceleration position and stopping position through various sensors. At the same time, the control system issues corresponding deceleration and stopping task commands. The shuttle stops accurately at the designated location, and the RGV's conveyor transports goods from the outbound point to the cart, or transports goods on the cart to the picking area.

2. The similarity-based order batching and picking sorting method for multiple picking stations as described in claim 1, characterized in that, When batching orders, the number of orders in the same batch cannot exceed the capacity of the picking station, and each order can only be assigned to the same batch.

3. The similarity-based order batching and picking sorting method for multiple picking stations as described in claim 1, characterized in that, Order similarity refers to the quantitative value of the similarity between order information, which includes the ordered items and quantities, and the item similarity is used as the basis for order batching.

4. The similarity-based order batching and picking sorting method for multiple picking stations as described in claim 1, characterized in that, The objective function is defined based on maximizing order similarity. The objective function is: （16） in, d n represents the similarity of order batch n, Zij represents the similarity between order i and order j, bij represents the decision variable, and M represents the total number of orders.

5. The similarity-based order batching and picking sorting method for multiple picking stations as described in claim 1, characterized in that, The difference in the number of orders picked between each picking station of a circular multi-picking station should be as small as possible.

6. The similarity-based order batching and picking sorting method for multiple picking stations as described in claim 1, characterized in that, The picking order for each batch is fixed, and there can only be one picking order.

7. A similarity-based order batching and picking sorting system for multiple picking stations, employing the method described in claim 1, characterized in that, Includes a control center and a loop system, wherein the control center includes: The data processing center is used to acquire different order data within a certain time window and divide each product in each order data. The data computing center is used to calculate the similarity of each order, determine whether different orders contain the same products, and batch orders according to the goal of maximizing similarity; and to build a mathematical model with the goal of minimizing the total number of goods entering and leaving the warehouse, and solve to determine the picking order of different batches of orders. The ring-through system is connected to the control center for communication, and is used to make its multiple ring-shaped picking stations work together to complete the picking of orders according to the determined picking order of different orders.

8. The similarity-based order batching and picking sorting system for multiple picking stations as described in claim 7, characterized in that, The circumferential system includes: The circular shuttle operates on a fixed circular track, automatically realizing the entry and exit of goods and transporting them to designated locations. Multiple picking stations are areas used for picking goods.

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