Warehouse picking system and methods

Abstract

A warehouse picking system and method are disclosed. The warehouse picking system includes multiple goods management stations and a management device. The management device receives multiple orders, each containing at least one item; calculates an order score for each order using an order score function, and selects at least one order as the target order based on the order score to determine a suitable target goods management station for handling the target order; selects a goods picking box containing at least one item from the target order from the picking boxes of the target goods management station as a candidate goods picking box; and determines a suitable target goods picking box for picking the items of the target order based on the workload of the goods picking equipment used to pick the items in the goods picking box, the workload of the goods transport equipment used to transport the goods, and the configuration of each candidate goods picking box.

Description

Technical Field

[0001] This disclosure relates to a warehouse picking system and method. Background Technology

[0002] In recent years, various manufacturers have invested in the field of smart warehousing. The operational efficiency of smart warehousing depends entirely on its own configured algorithms. However, factors affecting the efficiency of smart warehousing include warehousing architecture, equipment workload, turnover rate, shipment correlation, order specifications, volume, weight, etc. There are many types and complexities, making it difficult to find the best solution. Incorrect configuration will lead to waste and idle resources.

[0003] Common warehousing problems include limited storage space, limited picking equipment, and too many orders. These factors will affect delivery time, often causing delays of several days, and in special cases, even extending to one or more months. This not only causes significant losses due to delayed delivery, but also affects the capacity to receive new orders.

[0004] Against the backdrop of a surge in global logistics orders, rising labor costs, and the impact of the pandemic, the picking algorithms used in a warehouse will be a crucial factor in standing out among various smart warehouses. Summary of the Invention

[0005] This disclosure provides a warehouse picking system and method that can increase the picking volume in a factory without increasing manpower or equipment resources.

[0006] The warehouse picking system disclosed herein includes multiple cargo management stations and management devices. Each cargo management station includes multiple cargo picking bins, multiple cargo picking devices, and multiple cargo transport devices. Each cargo picking bin stores at least one item, the cargo picking devices are used to pick the item from the cargo picking bin, and the cargo transport devices are used to transport the item picked by the cargo picking devices. The management device includes a processor and a communication device for communicating with various cargo management stations. The processor is configured to: receive multiple orders, each order including at least one item; calculate an order score for each order using an order score function, and select at least one order as a target order based on the order score to determine a target cargo management station suitable for processing the target order from among multiple cargo management stations; select at least one cargo picking box containing at least one item from the target order as a candidate cargo picking box from the cargo picking boxes of the target cargo management station; and determine at least one target cargo picking box suitable for picking the item of the target order based on the workload of the cargo picking equipment used to pick the item in the cargo picking box, the workload of the cargo transport equipment used to transport the item, and the position of each candidate cargo picking box.

[0007] The warehouse picking method disclosed herein is applicable to management devices equipped with processors and communication devices. The management device is communicatively connected to multiple goods management stations via the communication devices. Each goods management station includes multiple goods picking bins for storing at least one item, multiple goods picking devices for picking items from the goods picking bins, and multiple goods transport devices for transporting the items picked by the goods picking devices. This method includes the following steps: receiving multiple orders, each order including at least one item; calculating an order score for each order using an order score function, and selecting at least one order as a target order based on the order score to determine a target goods management station suitable for processing the target order from among the multiple goods management stations; selecting at least one goods picking bin from the goods picking bins of the target goods management station that stores at least one item from the target order as a candidate goods picking bin; and determining at least one target goods picking bin suitable for picking the items of the target order based on the workload of the goods picking devices for picking items from the goods picking bins, the workload of the goods transport devices for transporting the goods, and the location of each candidate goods picking bin.

[0008] Based on the above, the warehouse picking system and method disclosed herein calculates an order score for all orders in the order pool according to an order content function, a product information function, and an order schedule function to determine a suitable goods management station for processing the order. Then, from that goods management station, candidate picking boxes that meet the order's product requirements are identified, and a picking box score is calculated based on the product content of the candidate picking boxes and the workload of the associated equipment to determine an optimized picking box configuration. This allows for the determination of an optimized picking process, thereby increasing the overall picking volume of the factory.

[0009] To make this disclosure more apparent and understandable, specific embodiments are described below, along with detailed descriptions in conjunction with the accompanying drawings. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of a warehouse picking system according to an embodiment of the present disclosure.

[0011] Figure 2 This is a flowchart of a warehouse picking method according to an embodiment of the present disclosure.

[0012] Figure 3 This is a schematic diagram of a target goods management station responsible for determining a target order according to an embodiment of the present disclosure.

[0013] Figure 4 This is a schematic diagram of a candidate goods picking box according to an embodiment of the present disclosure.

[0014] Figure 5 This is a schematic diagram of a target goods picking box according to an embodiment of the present disclosure.

[0015] Symbol Explanation

[0016] 1: Warehouse picking system

[0017] 10: Management device

[0018] 12: Communication device

[0019] 14: Processor

[0020] 22: Goods picking box

[0021] 24: Goods picking equipment

[0022] 26: Cargo transport equipment

[0023] 30: Target Orders

[0024] 40: Candidate Goods Picking Box

[0025] 50: Target Goods Picking Box

[0026] S202~S208: Steps Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0028] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0031] When using expressions such as "at least one of A, B, and C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). Similarly, when using expressions such as "at least one of A, B, or C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, or C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0032] This disclosure proposes an optimized picking planning algorithm. After considering factors such as warehouse orders, product information, and picking equipment operation information, it first groups similar orders based on the product data within each order to allocate them to corresponding goods management stations. Then, each goods management station calculates the optimal picking plan based on the assigned orders and the current location of the product bins and the status of its equipment. Finally, it picks the required products from the determined picking bins. This method efficiently calculates picking plans, maximizes picking volume, and minimizes order completion time, significantly increasing the overall picking volume of the factory without increasing manpower or equipment resources.

[0033] Figure 1 This is a schematic diagram of a warehouse picking system according to an embodiment of the present disclosure. Please refer to... Figure 1 The warehouse picking system 1 includes N cargo management stations #1 to #N and a management device 10, where N is a positive integer. Each cargo management station #1 to #N includes multiple cargo picking bins, multiple cargo picking devices, and multiple cargo transport devices. The cargo picking devices may be, for example, robotic arms or radioshuttle systems, and the cargo transport devices may be, for example, automated guided vehicles, conveyor belts, pallet trucks, or mobile shelves, but this embodiment is not limited to these.

[0034] Taking cargo management station #1 as an example, it includes multiple cargo picking bins 22 storing at least one cargo, multiple cargo picking devices 24 for picking cargo from the cargo picking bins 22, and multiple cargo transport devices 26 for transporting cargo picked by the cargo picking devices 24. The management device 10 includes a communication device 12 and a processor 14.

[0035] The communication device 12 is, for example, a wireless communication device that supports communication protocols such as Wi-Fi, RFID, Bluetooth, infrared, near-field communication (NFC), or device-to-device (D2D). In some embodiments, the communication device 12 is, for example, a network card that supports wired network links such as Ethernet or a wireless network card that supports wireless communication standards such as IEEE 802.11n / b / g. The communication device 12 can communicate with cargo management stations #1 to #N via wired or wireless means.

[0036] The processor 14 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose microprocessor, digital signal processor (DSP), programmable controller, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), programmable logic controller (PLC), or other similar device or combination of these devices, used to control the operation of the management device 10, and can load and execute computer programs to perform the warehouse picking method of the embodiments of this disclosure.

[0037] Figure 2 This is a flowchart of a warehouse picking method according to an embodiment of the present disclosure. Please also refer to... Figure 1 and Figure 2 The method in this embodiment is applicable to Figure 1 The following describes the detailed steps of the warehouse picking method disclosed herein, which is described in conjunction with the various components of the warehouse picking system 1.

[0038] In step S202, processor 14 receives multiple orders, each order including at least one item. For example, processor 14 selects the top n orders from a pool of M orders in management device 10, sorted by time or importance, as the orders to be processed, where M and n are positive integers. The orders in the order pool are either received by processor 14 from an external device via communication device 12 or entered locally by a warehouse manager; there are no restrictions on this.

[0039] In step S204, the processor 14 calculates the order score for each order using an order score function, and selects at least one order as the target order based on the order score, in order to process the goods from multiple cargo management stations (such as...). Figure 1 The target cargo management station suitable for processing the target order is determined from cargo management stations #1 to #N. In one embodiment, the order score function includes an order content function, a product information function, and an order schedule function, and the order score is the weighted sum of the function values ​​of the order content function, the product information function, and the order schedule function. In other embodiments, the order score function may also include other functions; this embodiment does not limit the type and number of functions included.

[0040] In one embodiment, the order content function is the ratio of the number of items in each order to the maximum number of items in the order pool, the product information function is the ratio of the distance from the order product to the exit to the longest distance from the product to the exit, and the order schedule function is the ratio of the order creation time to the time spent processing the order.

[0041] For example, assuming the order content function is T, the product information function is P, and the order schedule function is S, the formula for calculating the order score O(m) of the m-th order out of the extracted n orders is as follows:

[0042] O(m)=tT+pP+S (1)

[0043] Where t and p are coefficients, the values ​​of which can be adjusted according to actual needs, and no limit is set here.

[0044] Figure 3 This is a schematic diagram of a target goods management station responsible for determining a target order, according to an embodiment of this disclosure. Please refer to... Figure 3 The management device in this embodiment receives multiple orders, including order x0001 (including goods p1), order x0002 (including goods p4), order x0007 (including goods p2 and p3), ..., order x0025 (including goods p25), order x0029 (including goods p29 and goods p44), and order x0032 (including goods p32 and goods p21), etc.

[0045] In the above formula (1), the order content function T can be: number of goods in this order / maximum number of goods in the order pool, the product information function P can be: distance from the order product to the exit / longest distance from the product to the exit, and the order time schedule function S can be: order establishment time / time spent.

[0046] In one embodiment, since the quantity of goods included in order x0001 (including goods p1, with a quantity of 1) is less than the quantity of goods included in order x0007 (including goods p2 and goods p3, with a quantity of 2), order x0001 is easier to complete than order x0007. In this case, the value of the order function T of order x0001 can be set to be greater than the value of the order function T of order x0007.

[0047] In another embodiment, assuming that the distance between the location of goods p1 (in order x0001) and the warehouse exit is less than the distance between the location of goods p4 (in order x0002) and the warehouse exit, order x0001 is easier to complete than order x0002. In this case, the value of the product information function P of order x0001 can be set to be greater than the value of the product information function P of order x0002.

[0048] In another embodiment, assuming that the order establishment time of order x0001 is earlier than the order establishment time of order x0002, the management device can set the value of the order time schedule function S of order x0001 to be greater than the value of the order time schedule function S of order x0002.

[0049] After setting the values ​​of the order content function F, the product information function P, and the order schedule function S for each received order, the management device can calculate the order score for each order according to formula (1) and sort these orders according to the order score.

[0050] exist Figure 2 In the example shown, the management device can select a predetermined number (e.g., 25 orders) of orders from the sorted orders as target orders 30, and determine the target cargo management station suitable to be responsible for these target orders, thereby assigning these target orders to the target cargo management station to instruct the target cargo management station to be responsible for the picking and transportation of the goods in the order.

[0051] Back Figure 2 In the process, after determining the target cargo management station, the management device 10 can further decide which cargo picking box to pick the cargo from.

[0052] In detail, in step S206, the processor 14 selects at least one picking box containing at least one item from the target order from among the multiple picking boxes of the target cargo management station as a candidate picking box.

[0053] For example, Figure 4 This is a schematic diagram of a candidate goods picking box according to an embodiment of the present disclosure. Please refer to... Figure 4 In this embodiment, it is assumed that the target order includes two goods a and one goods b, and that the target goods management station includes goods picking boxes 1 to 8.

[0054] The management device can select multiple candidate picking boxes from picking boxes 1 to 8. For example... Figure 4 As shown, since the 10 goods a included in goods picking box 1 can be used to complete the target order (two goods a can be picked from goods picking box 1 to complete the target goods), the management device can determine goods picking box 1 as a candidate goods picking box. In addition, since the 3 goods a, 7 goods b, and 2 goods c included in goods picking box 7 can be used to complete the target order (two goods a and one goods b can be picked from goods picking box 7 to complete the target goods), the management device can also determine goods picking box 7 as a candidate goods picking box.

[0055] The results are as follows Figure 4 As indicated by label 40, the management device can select cargo picking box 1 (containing 10 goods a), cargo picking box 3 (containing 12 goods b), cargo picking box 4 (containing 5 goods a and 5 goods b), and cargo picking box 7 (containing 3 goods a, 7 goods b, and 2 goods c) as candidate cargo picking boxes.

[0056] Back Figure 2 In the process, in step S208, the processor can determine the target goods picking box suitable for picking the goods of the target order based on the workload of the goods picking equipment used to pick the goods in the picking box, the workload of the goods transport equipment used to transport the goods, and the configuration of each candidate goods picking box. The configuration of the candidate goods picking box includes, for example, the location of the candidate goods picking box (distance from the warehouse exit) and the correlation between the goods in it and the target goods, which is not limited here.

[0057] In one embodiment, the processor 14 can calculate a first ratio of a first coefficient to the workload of the picking equipment, a second ratio of a second coefficient to the workload of the transport equipment, a first product of the position of the candidate picking box and a third coefficient, and a second product of the correlation between the picking box and the goods in the order and a fourth coefficient. The processor 14 then calculates the sum of the first ratio, the second ratio, the first product, and the second product as the picking box score for each picking box. Finally, based on the calculated picking box scores of each picking box, the processor 14 determines the target picking box suitable for picking the goods of the target order.

[0058] For example, assuming the current workload of the picking equipment is A, the current workload of the transport equipment is B, the location of the picking bin is C, and the relevance between the goods in the picking bin and the target goods is D, then the formula for calculating the picking bin score is as follows:

[0059] score=a / A+b / B+cC+dD (2)

[0060] Where a, b, c, and d are coefficients, the values ​​of which can be adjusted according to the actual situation, and no limit is set here.

[0061] In one embodiment, if the manufacturer wants to make the workload of each picking device more even, it can set a condition where the workload A of a certain device is greater than the average workload of all devices. If the value of parameter a is less than 1, then the value of parameter a will be decreased; otherwise, it will be increased.

[0062] In one embodiment, if the manufacturer wants to make the workload of each cargo transport device more even, it can set a condition where the workload B of a certain device is greater than the average workload of all devices. The value of parameter b decreases if the parameter b decreases, and vice versa.

[0063] In one embodiment, based on the fact that the closer the picking box is to the outlet in all situations, the higher the score of the box. If the manufacturer's warehouse processing speed is greatly affected by the distance between the box and the outlet, the value of parameter c can be increased. Otherwise, parameter c can be set to a fixed value, such as 0.25.

[0064] In one embodiment, the parameter d is related to the arrangement pattern of stored goods. For example, if the arrangement pattern of goods X is that they are scattered among multiple picking bins, then there are many bins that meet the condition of this goods, and the value of parameter d can be set to a small value or a normal value; while if the arrangement pattern of goods X is that they are concentrated in a few picking bins, then bins that meet the condition of this goods will be relatively scarce, and the value of parameter d can be increased, as shown in the following examples:

[0065] d = 0.25 × number of orders including the goods / number of containers storing the goods (3)

[0066] For example, Figure 5 This is a schematic diagram of a target goods picking box according to an embodiment of the present disclosure. Please also refer to... Figure 4 and Figure 5 This embodiment is for Figure 4 In the embodiment, the candidate goods picking boxes 1, 3, 4, and 7 are further calculated for their picking equipment workload score, transportation equipment workload score, box transportation distance score, and goods association score within the box, and their total score is calculated according to the above formula (2).

[0067] Since the picking box score calculated by candidate goods picking box 7 using formula (2) is greater than the picking box scores of candidate goods picking boxes 1, 3, and 4, the management device can determine candidate goods picking box 7 as target goods picking box 50 (i.e., picking from candidate goods picking box 7 to complete the target order).

[0068] Finally, based on the determined target goods picking box 50, the management device can determine which goods picking equipment will pick the goods and which goods transport equipment will transport the goods, thereby instructing the corresponding goods picking equipment and goods transport equipment to pick and transport the goods.

[0069] In one embodiment, the processor 14 may further calculate a weighted sum of the picking box score and at least one logistics score to determine the target goods picking box suitable for picking the goods of the target order.

[0070] For example, if picking goods needs to be considered in conjunction with other processes besides the picking process itself, the picking box score mentioned above can be multiplied by a default weighting parameter. For instance, considering the number of truck drivers and their scheduling processes for delivering to supermarkets and convenience stores outside the warehouse, a logistics score L can be calculated based on the above process. Then, the picking box score is multiplied by the default weighting parameter f and added to the logistics score L to obtain the final goods picking score F, which serves as the basis for picking goods. The formula is as follows:

[0071] F = f*score + L (4)

[0072] In summary, the warehouse picking system and method disclosed herein calculates an order score for all orders in the order pool based on a weighted sum of an order content function, a product information function, and an order schedule function. This order score then determines the priority of picking orders and assigns them to the corresponding goods management station. The warehouse picking system can identify candidate picking boxes that meet the order's product requirements from within the goods management station, calculate a picking box score based on the product content of each candidate picking box and the workload of the associated equipment, find an optimized picking box configuration, and finally determine the picking and transportation tasks based on the selected picking boxes. This significantly increases the picking volume of the entire factory without increasing manpower or equipment resources.

[0073] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A warehouse picking system, comprising: Multiple cargo management stations, each of which includes: Multiple goods picking bins, each of which stores at least one item; Multiple goods picking devices pick up the goods from the goods picking bins; Multiple cargo transport devices transport the cargo picked up by the cargo picking device; and The management device includes a processor and a communication device for communicatively connecting each of the cargo management stations, the processor being configured to: Receive multiple orders, wherein each of the orders includes at least one of the goods described; An order score is calculated for each of the orders using an order score function, and at least one of the orders is selected as the target order based on the order score, so as to determine the target cargo management station suitable for processing the target order from the plurality of cargo management stations; At least one picking box containing at least one of the goods in the target order is selected from the picking boxes of the target cargo management station as a candidate picking box; Calculate the first ratio of the first coefficient to the workload of the goods picking equipment, the second ratio of the second coefficient to the workload of the goods transport equipment, the first product of the transport distance of the candidate goods picking box and the third coefficient, and the second product of the correlation score between the goods in the goods picking box and the goods in the order and the fourth coefficient. Then calculate the sum of the first ratio, the second ratio, the first product, and the second product as the picking box score for each goods picking box; and Based on the picking box fraction, at least one target goods picking box suitable for picking the goods of the target order is determined.

2. The warehouse picking system according to claim 1, wherein the order score function includes an order content function, a product information function, and an order time schedule function, and the order score is a weighted sum of the function values ​​of the order content function, the product information function, and the order time schedule function.

3. The warehouse picking system according to claim 2, wherein the order content function is the ratio of the number of order items in each order to the maximum number of order items in the order pool.

4. The warehouse picking system according to claim 2, wherein the commodity information function is the ratio of the distance from the order commodity to the exit to the longest distance from the commodity to the exit for each order.

5. The warehouse picking system according to claim 2, wherein the order time schedule function is the ratio of the order creation time to the order processing time for each order.

6. The warehouse picking system according to claim 1, wherein the processor includes selecting the first n orders from M orders in the order pool as the orders to be processed.

7. The warehouse picking system of claim 1, wherein the processor further calculates a weighted sum of the picking box score and at least one logistics score to determine the target goods picking box suitable for picking the goods of the target order.

8. The warehouse picking system of claim 1, wherein the processor includes determining the target goods picking box suitable for picking the goods of the target order based on the calculated size of the picking box fraction of each of the goods picking boxes.

9. The warehouse picking system according to claim 1, wherein the goods picking equipment includes a robotic arm or shuttle car, and the goods transport equipment includes an automated guided vehicle, a conveyor belt, a flatbed cart, or a mobile rack.

10. A warehouse picking method, applicable to a management device equipped with a processor and a communication device, wherein the management device is communicatively connected to multiple cargo management stations via the communication device, each cargo management station comprising multiple cargo picking bins for storing at least one type of cargo, multiple cargo picking devices for picking the cargo from the cargo picking bins, and multiple cargo transport devices for transporting the cargo picked by the cargo picking devices, the method comprising the following steps: Receive multiple orders, wherein each of the orders includes at least one of the goods described; An order score is calculated for each of the orders using an order score function, and at least one of the orders is selected as the target order based on the order score, so as to determine the target cargo management station suitable for processing the target order from the plurality of cargo management stations; At least one picking box containing at least one of the goods in the target order is selected from the picking boxes of the target cargo management station as a candidate picking box; Calculate the first ratio of the first coefficient to the workload of the goods picking equipment, the second ratio of the second coefficient to the workload of the goods transport equipment, the first product of the transport distance of the candidate goods picking box and the third coefficient, and the second product of the correlation score between the goods in the goods picking box and the goods in the order and the fourth coefficient. Calculate the sum of the first ratio, the second ratio, the first product and the second product as the picking box score of each goods picking box. as well as Based on the picking box fraction, at least one target goods picking box suitable for picking the goods of the target order is determined.

11. The warehouse picking method according to claim 10, wherein the order score function includes an order content function, a product information function, and an order time schedule function, and the order score is a weighted sum of the function values ​​of the order content function, the product information function, and the order time schedule function.

12. The warehouse picking method according to claim 11, wherein the order content function is the ratio of the number of order items in each order to the maximum number of order items in the order pool.

13. The warehouse picking method according to claim 11, wherein the commodity information function is the ratio of the distance from the order commodity to the exit to the longest distance from the commodity to the exit for each order.

14. The warehouse picking method according to claim 11, wherein the order time schedule function is the ratio of the order creation time to the order processing time for each order.

15. The warehouse picking method according to claim 10, wherein the step of receiving multiple orders includes taking the first n orders from M orders in the order pool as the orders to be processed, where M and n are positive integers.

16. The warehouse picking method of claim 10, wherein the step of determining at least one target picking box suitable for picking the goods of the target order based on the workload of the picking equipment for picking the goods in the picking box, the workload of the transport equipment for transporting the goods, and the position of each of the candidate picking boxes further comprises: Calculate the weighted sum of the picking box score and at least one logistics score to determine the target goods picking box suitable for picking the goods of the target order.

17. The warehouse picking method of claim 16, wherein the step of determining at least one target picking box suitable for picking the goods of the target order based on the workload of the picking equipment for picking the goods in the picking box, the workload of the transport equipment for transporting the goods, and the position of each of the candidate picking boxes comprises: Based on the calculated fraction of each of the picking boxes, the target picking box suitable for picking the goods of the target order is determined.

18. The warehouse picking method according to claim 10, wherein the picking equipment includes a robotic arm or a shuttle, and the transport equipment includes an automated guided vehicle, a conveyor belt, a flatbed cart, or a mobile rack.

Images