Apparatus and method for constrained replenishment zone storage and flexible robot task assignment using order association matching algorithm

Abstract

The present invention relates to an apparatus and a method for managing products in a logistics center. The apparatus comprises: a memory including at least one instruction; and at least one processor executing the at least one instruction stored in the memory. The processor identifies a plurality of order combinations for sales products included in pre-existing order data, analyzes product-specific associations among a plurality of products forming each order combination, and arranges the sales products on the basis of the product-specific associations to determine a picking order of products for a plurality of new orders.

Description

Device and method for constrained replenishment zone stacking and robot task flexible assignment using an order association matching algorithm

[0001] The present invention relates to a product management device and method in a logistics center.

[0002] With the rapid development of e-commerce, such as internet shopping and home shopping, technological development for logistics information management systems (Logistics Information Management Systems) to computerize logistics functions is continuing. These Logistics Information Management Systems consist of Warehouse Management Systems (WMS), Order Management Systems (OMS), and Transportation Management Systems (TMS). Among these, the Warehouse Management System serves as the foundation of the Logistics Information Management System, designed to enable logistics centers to efficiently handle the entire process from inventory management to receiving and shipping within the warehouse.

[0003] Warehouse management systems manage the shapes, packaging types, and characteristics of various products, enabling the inspection of incoming goods during the receiving process, the input of product information, and location-based storage. Since incoming goods or pallets move within a location based on movement instructions, technologies are being researched to minimize movement within the warehouse.

[0004] Traditionally, the Person to Goods (PTG) picking method was used; however, this method was effective when product inventory was relatively concentrated and identical items were stored in adjacent locations on fixed shelves, resulting in fixed picking paths for workers. Conversely, when handling a large volume of goods and handling high-volume orders, there was a problem where work efficiency was significantly reduced due to the need to sort products using the conventional PTG method.

[0005] In addition, with the rapid advancement of robot technology, the Goods to Person (GTP) method using robots has been introduced. By distributing various products across multiple cargo shelves and having robots transport the shelves, the number of product transports can be reduced, thereby improving the efficiency of picking operations. However, this GTP method also has a problem in that there is no fundamental solution regarding how to distribute and arrange products on cargo shelves when handling multi-product items or when orders are large.

[0006] Embodiments of the present invention for solving these conventional problems provide a product management device and method in a logistics center that can identify related products based on past order history and arrange related products adjacently.

[0007] Furthermore, embodiments of the present invention for solving conventional problems provide a product management device and method in a logistics center that can minimize the movement of a transport robot when picking products included in a new order by considering the location where related products are placed and the traffic of the transport robot.

[0008] A product management device in a logistics center according to an embodiment of the present invention comprises a memory including at least one instruction and at least one processor that executes the at least one instruction stored in the memory, wherein the processor identifies a plurality of order combinations for products for sale included in pre-order data, analyzes product-specific associations for a plurality of products forming the order combinations, and arranges the products for sale based on the product-specific associations to determine the picking order of products for a plurality of new orders.

[0009] In addition, the processor is characterized by identifying the number of existing order data items having the same order combination in the above existing order data, and analyzing that the correlation between products forming the order combination is high in order of the largest number.

[0010] In addition, the processor is characterized by sorting the priority of the sales products in order of high correlation with each product and placing the sales products starting from the zone located at the position with the shortest movement path based on the starting point of a plurality of transport robots.

[0011] In addition, the processor is characterized by dividing a plurality of zones into a plurality of placement areas, and placing a plurality of products with high correlation to each product into a plurality of placement areas divided from the same zone.

[0012] In addition, the processor is characterized by determining the picking order of the above products in the order of the zones with the fewest number of working transport robots.

[0013] In addition, the processor is characterized by identifying the number of new orders forming the same order combination among the plurality of new orders and determining the picking order of the products in order of the largest number.

[0014] In addition, the processor is characterized by determining the picking order of the products by considering the number of zones that the transport robot must visit to pick products for order combinations confirmed in the plurality of new orders.

[0015] In addition, the processor is characterized by controlling the plurality of transport robots by setting the movement paths of the plurality of transport robots according to the picking order of the above-mentioned products.

[0016] In addition, the processor is characterized by setting the storage location of the sales product based on the above-mentioned pre-order data using an objective function and at least one constraint expression.

[0017] In addition, a product management method in a logistics center according to an embodiment of the present invention is characterized by comprising the steps of: a processor confirming a plurality of order combinations for products for sale included in pre-order data; the processor analyzing product-specific associations for a plurality of products forming the order combinations; and the processor arranging the products for sale based on the product-specific associations to determine the picking order of products for a plurality of new orders.

[0018] As described above, the product management device and method in a logistics center according to the present invention has the effect of minimizing the movement path of a transport robot picking products, even when handling multi-product items or when orders are in large quantities, by identifying related products based on past order history and arranging related products adjacently.

[0019] In addition, the product management device and method in a logistics center according to the present invention has the effect of minimizing the movement path of a transport robot during product picking and preventing traffic situations of the transport robot by considering the location where related products are placed and the traffic of the transport robot during product picking.

[0020] FIG. 1 is a block diagram showing the main configuration of a system for managing goods in a logistics center according to an embodiment of the present invention.

[0021] FIG. 2 is a block diagram showing the main configuration of an electronic device for managing goods in a logistics center according to an embodiment of the present invention.

[0022] FIG. 3 is a flowchart illustrating a method for managing goods in a logistics center according to an embodiment of the present invention.

[0023] Figure 4 is a diagram illustrating a method for confirming product-specific associations by verifying order combinations in pre-order data according to an embodiment of the present invention.

[0024] FIGS. 5 to 7 are drawings for explaining a method for arranging products according to an order combination according to an embodiment of the present invention.

[0025] Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present invention and is not intended to represent the only embodiment in which the present invention can be practiced. In order to clearly explain the present invention in the drawings, parts unrelated to the description may be omitted, and the same reference numerals may be used for identical or similar components throughout the specification.

[0026]

[0027] FIG. 1 is a block diagram showing the main configuration of a system for managing goods in a logistics center according to an embodiment of the present invention.

[0028] Referring to FIG. 1, the system (10) according to the present invention may include a plurality of transport robots (100) and electronic devices (200).

[0029] A plurality of transport robots (100) are AGVs (auto guided vehicles) that move along a set path for communication with an electronic device (200), pick up a product placed at a corresponding location, and deliver it to a line for packaging. To this end, the plurality of transport robots (100) can communicate with the electronic device (200) via Wi-Fi (wireless fidelity) or the like.

[0030] The electronic device (200) identifies multiple order combinations for products for sale included in the pre-order data and analyzes the product-specific associations for multiple products forming the order combinations among the products for sale. Based on the analyzed product-specific associations, the electronic device (200) arranges the products for sale to determine the picking order of products for multiple new orders and controls the movement of multiple transport robots (100). The operation of such an electronic device (200) will be explained in more detail using FIG. 2 below. FIG. 2 is a block diagram showing the main configuration of an electronic device for managing products in a logistics center according to an embodiment of the present invention.

[0031] Referring to FIG. 2, the electronic device (200) according to the present invention may include a communication unit (210), an input unit (220), a display unit (230), a memory (240), and a processor (250).

[0032] The communication unit (210) can set the movement path of the transport robot (100) through communication with the transport robot (100). To this end, the communication unit (201) can perform wireless communication with the transport robot (100), such as Wi-Fi (wireless fidelity).

[0033] The input unit (220) generates input data in response to user input of the electronic device (200). To this end, the input unit (203) may include input means such as a keyboard, a key pad, a dome switch, a touch panel, a touch key, a mouse, and a menu button.

[0034] The display unit (230) displays display data according to the operation of the electronic device (200). The display unit (230) includes a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a micro electro mechanical systems (MEMS) display, and an electronic paper display. The display unit (230) can be combined with the input unit (220) to be implemented as a touch screen.

[0035] The memory (240) stores operation programs of the electronic device (200). The memory (240) can store the type of product for sale, a unique ID for the product for sale, pre-order data, etc. The memory (240) can store an algorithm for setting the optimal storage area of ​​the product for sale based on the pre-order data, and can store an algorithm for arranging products according to product associations by verifying order combinations based on the pre-order data. In addition, the memory (240) can store programs for controlling the transport robot (100), etc.

[0036] The processor (250) checks the pre-order data. At this time, the pre-order data may refer to the order history of products sold by a consumer in the past by the manager or management company of the electronic device (200).

[0037] The processor (250) establishes an optimized storage strategy for the products for sale using an objective function, multiple constraint expressions, and pre-order data, and sets the storage location of the products for sale. At this time, the storage of the products for sale means storing the products by stacking them in pallet units.

[0038] The processor (250) analyzes the pre-order data to identify order combinations for products included in the pre-order data among the products for sale, and analyzes the correlation between products based on this. More specifically, the processor (250) can identify the order details, i.e., order combinations, for the types of products ordered for each order data in the pre-order data. The processor (250) identifies the number of order data items that have the same order combination and can analyze that the correlation between products forming the order combination is high in order of the number of order data items that have the same order combination.

[0039] The processor (250) can arrange the priority of product placement for sales products in order of highest product relevance, and place the sales products starting from the zone located at the shortest travel path based on the starting point of multiple transport robots. The processor (250) can place multiple products with high product relevance into multiple placement zones divided within the same zone. At this time, the placement of products is managed separately from the placement of products, as it is arranged so that the transport robot (100) can pick the products according to the order.

[0040] For example, if the processor (250) has all the products included in a specific order combination that are identical to a part of the products included in another order combination, the processor (250) can combine the order count of the specific order combination with the order count of the other order combination to update the order count of the other order combination, and reorder the priority of product placement for sales products based on the updated order count.

[0041] Additionally, if the entire product included in a specific order combination is identical to a part of the product included in two other order combinations, the processor (250) can update the order count for the two other order combinations by combining the order count of the specific order combination with the order counts of the two other order combinations. Then, the processor (250) updates the order count of the order combination with the smaller order count among the two other order combinations. At this time, the processor (250) can update the order count of the order combination with the smaller order count by dividing the order count of the specific order combination evenly according to the number of other order combinations and combining the divided order count with the initial order count of the order combination with the smaller order count. The processor (250) can reorder the priority of product placement for sales products based on the updated order count.

[0042] The processor (250) selects the placement location starting with the product with the highest priority, that is, the product with the highest product relevance. At this time, the processor (250) may place the product in the same zone or the placement area included in the same zone as the product has higher product relevance. At this time, in the case of an order combination consisting of products with low product relevance, the placement location for the product may not be separately selected.

[0043] The processor (250) can control the transport robot (100) to place multiple products included in the order combination at each placement location according to the set placement location, or guide the manager placing the products.

[0044] When a new order is received, the processor (250) determines the picking order of the products for each new order and sets the movement path of the transport robot (100) according to the determined picking order, thereby controlling the movement, picking, etc. of the transport robot (100).

[0045] More specifically, when a plurality of new orders are received, the processor (250) checks the order combination for each new order. The processor (250) determines the picking order for the products included in the new orders by considering the zone with the fewest number of working transport robots, the order with the most identical order combinations, and the number of zones that must be visited to pick the products for the order combinations.

[0046] That is, the processor (250) can identify the zone with the fewest number of transport robots among the zones where products are placed and primarily assign a picking priority for products included in a new order. In this way, the processor (250) can assign a priority by considering the traffic of transport robots occurring in the same zone.

[0047] And the processor (250) secondarily assigns a picking priority for the product in the order of the new order with the most identical order combinations, moves the transport robot to the zone where the product included in the new order is placed, and causes the transport robot to perform operations in that zone.

[0048] Finally, the processor (250) may assign a picking priority by considering the number of zones that must be visited to pick products for order combinations included in new orders that have not been assigned a priority after assigning a first and second picking priority. For example, in the case of an order combination consisting of products with low product-specific correlation, the location where the products are placed may not be separately selected, so multiple zones must be visited to pick the products included in the order combination. Therefore, for new orders consisting of products with low product-specific correlation in this way, the picking priority may be assigned last.

[0049] In this way, the processor (250) can control the transport robot based on the picking priority set for the new order so that the transport robot picks the goods included in the new order according to the priority and moves to the outgoing line for shipping the goods.

[0050]

[0051] FIG. 3 is a flowchart illustrating a method for managing goods in a logistics center according to an embodiment of the present invention.

[0052] Referring to FIG. 3, in step 301, the processor (250) checks the pre-order data. At this time, the pre-order data may refer to the history of past orders for at least one product among the products sold by the manager or management company of the electronic device (200).

[0053] In step 303, the processor (250) establishes an optimized storage strategy for the products for sale using pre-order data and sets the storage location of the products for sale. To do this, the processor may use the following mathematical formulas 1 to 4. Mathematical formula 1 is an objective function for selecting the product with the most completed order combinations among the products for sale, mathematical formula 2 is a constraint formula for checking which products are included in each order, mathematical formula 3 is a constraint formula for checking the number of products included in the order combinations among the products for sale, and mathematical formula 4 is a constraint formula for checking the number of completed orders.

[0054] [Mathematical Formula 1]

[0055]

[0056] [Mathematical Formula 2]

[0057]

[0058] [Mathematical Formula 3]

[0059]

[0060] [Mathematical Formula 4]

[0061]

[0062] Here, M is big-M, which can be a large positive number to ensure that the constraint in mathematical equation 2 can be solved correctly, and generally, a number greater than or equal to 10^6 is used. m is the number of products for sale, n is the number of orders, and is a binary variable, and is a matrix of the number of products for each order number, n( ) represents the number of product types included in order i, N represents the number of products to be selected, and s and c represent slack variables to convert inequality operations into equality.

[0063] For example, there are 1 to 5 products, and the products included in orders 1 to 5 may be as shown in Table 1 below. In this case, 1 represents a product that has been ordered, and 0 represents a product that has not been ordered.

[0064] Product 1 Product 2 Product 3 Product 4 Product 5 Order 1 1 1 00 Order 2 1 0 10 Order 3 0 1 1 10 Order 4 1 0 11 Order 5 1 1 0 10

[0065] If there is order data such as Table 1 above, the processor (250) substitutes each order combination into Equation 2, and for each order , , , , ...can be derived. In addition, the processor (250) applies to the inequality derived for each order. inside Generates the number of cases where the value for is set to 0 or 1, and inside Using the number of cases for inside The value of is calculated as 0 or 1. Using this, the processor (250) can find the point where the sum of the y values ​​is minimized as the optimal solution. For example, the processor (250) can use mathematical formulas 1 to 4 to set the products included in the first set, which is the set of products that complete the most orders among the pre-order data, to be stored in area A, set the products included in the second set, which is the set of products that complete the most orders, to be stored in area B excluding the products included in the first set stored in area A, and set the products excluding the products stored in areas A and B to be stored in area C.

[0066] In step 305, the processor (250) analyzes the pre-order data to identify order combinations for products included in the pre-order data among the products for sale, and analyzes product-specific associations based on this. This will be explained in more detail using the following Fig. 4. Fig. 4 is a diagram illustrating a method for identifying product-specific associations by identifying order combinations in pre-order data according to an embodiment of the present invention.

[0067] Referring to FIG. 4, FIG. 4(a) shows the stock keeping unit (SKU) and order quantity included in the stock keeping unit data. As shown in FIG. 4(a), the processor (250) can confirm, as a result of analyzing the stock keeping unit data, that in the stock keeping unit data, for example, product a, product b, product c, product d, and product e are ordered in quantities of 120, 70, 60, 100, and 30, respectively.

[0068] Figure 4(b) shows the result of the processor (250) verifying the order combinations included in the pre-order data. For example, the number of orders ordered as the first order combination of product a, product b, and product c may be 40, the number of orders ordered as the second order combination of product a and product d may be 40, the number of orders ordered as the third order combination of product b, product c, and product d may be 20, the number of orders ordered as the fourth order combination of product a, product d, and product e may be 30, and the number of orders ordered as the fifth order combination of product a, product b, and product d may be 10.

[0069] Looking at this, since there are 40 orders each for the first order combination and the second order combination out of 140 orders, the processor (250) can confirm that the product association for the products included in the first order combination and the second order combination is 1st rank, the product association for the products included in the fourth order combination is 3rd rank, the product association for the products included in the third order combination is 4th rank, and the product association for the products included in the fifth order combination is 5th rank.

[0070] In step 307, the processor (250) selects a placement location for placing each product in a picking zone such as (c) of FIG. 4. For example, the picking zone may include a first zone, a second zone, and a third zone, and each zone may include three placement areas (1-1, 1-2, 1-3, 2-1, 2-2, 2-3, 3-1, 3-2, 3-3). The selection of the product placement location will be explained using FIGS. 5 to 7 below. FIGS. 5 to 7 are drawings for explaining a method for placing products according to an order combination according to an embodiment of the present invention.

[0071] Referring to FIGS. 4 and 5, it can be seen that the second order combination includes product a and product d, the fourth order combination includes product a, product d, and product e, and the fifth order combination includes product a, product b, and product d. Accordingly, the processor (250) can set the priority of product placement by resetting the product-specific associations by combining the 40 orders for the second order combination with the fourth order combination and the fifth order combination, respectively. As shown in FIG. 5, if the number of orders for the second order combination is included in the fourth order combination, the total number of orders for the fourth order combination becomes 70, and the total number of orders for the fifth order combination becomes 50. Accordingly, the processor (250) can set the fourth order combination as the first priority for placement and select placement locations to place the products included in the fourth order combination in 1-1, 1-2, and 1-3 of the first zone, respectively.

[0072] Next, the processor (250) resets the priority for the remaining order combinations as shown in FIG. 6. At this time, since the processor (250) has set the priority by including the 40 orders of the second order combination in the fourth order combination and the fifth order combination as shown in FIG. 5 and has selected the placement location of the products included in the fourth order combination, the 40 orders included in the fifth order combination are divided equally into 20 orders, thereby readjusting the number of orders in the fifth order combination to 30 orders. Through this, it can be confirmed that the first order combination has the highest priority, with the number of orders in the first order combination, the third order combination, and the fifth order combination being 40, 20, and 30 orders, respectively. Accordingly, the processor (250) can set the placement location to place the products included in the first order combination in 2-1, 2-2, and 2-3 of the second zone, respectively.

[0073] Next, the processor (250) can check the priority of the remaining order combinations as shown in FIG. 7 and confirm that the priority of the 5th order combination is higher than the priority of the 3rd order combination. Accordingly, the processor (250) can set placement locations to place the products included in the 5th order combination in 3-1, 3-2, and 3-3 of the 3rd zone, respectively. As shown in FIG. 5 to 7, the processor (250) can set placement locations to place multiple products with high product-specific correlation in multiple placement zones divided within the same zone, respectively. In this way, when placement locations for products are set in all placement zones included in the 1st to 3rd zones, the processor (250) does not set separate placement locations for products included in combinations with low product-specific correlation, such as the 3rd order combination.

[0074] In step 309, the processor (250) may control the transport robot (100) to place multiple products included in the first to fifth order combinations at each placement location according to the placement location set in step 307, or guide the manager placing the products.

[0075] In step 311, when a new order is received, the processor (250) performs step 313 to determine the picking order of the products for each new order, and in step 315, the processor (250) sets the movement path of the transport robot (100) according to the determined picking order to control the movement, picking, etc. of the transport robot (100).

[0076] More specifically, when a plurality of new orders are received, the processor (250) checks the order combination for each new order. The processor (250) determines the picking order for the products included in the new orders by considering the zone with the fewest number of working transport robots, the order with the most identical order combinations, and the number of zones that must be visited to pick the products for the order combinations.

[0077] That is, the processor (250) can identify the zone with the fewest number of transport robots among the zones where products are placed and primarily assign a picking priority for products included in a new order. In this way, the processor (250) can assign a priority by considering the traffic of transport robots occurring in the same zone.

[0078] The processor (250) secondarily assigns a picking priority for the product in the order of the new orders with the most identical order combinations, moves the transport robot to the zone where the product included in the new order is placed, and causes the transport robot to perform operations in that zone. For example, in the case where the first order combination and the second order combination are most numerous among the new orders, the processor (250) can move the transport robot to the second zone where the product for the first order combination is placed, and since the product for the second order combination can be picked in both the first zone and the third zone, the transport robot can be moved to the first zone and the third zone respectively.

[0079] Finally, the processor (250) may assign a picking priority by considering the number of zones that must be visited to pick products for order combinations included in new orders that have not been assigned a priority after assigning a first and second picking priority. For example, in the case of a third order combination, since it must visit the first zone and the second zone, or the second zone and the third zone, the picking priority may be assigned last for new orders having a third order combination.

[0080] In this way, the processor (250) can control the transport robot so that the transport robot can pick the goods included in the new order according to the priority based on the picking priority set for the new order.

[0081] The embodiments of the invention disclosed in this specification and drawings are provided merely as specific examples to facilitate the explanation of the technical content of the invention and to aid in understanding the invention, and are not intended to limit the scope of the invention. Accordingly, the scope of the invention should be interpreted to include all modifications or variations derived based on the technical concept of the invention, in addition to the embodiments disclosed herein.

Claims

1. Memory containing at least one instruction; and It includes at least one processor that executes at least one instruction stored in the memory, The above processor is, An electronic device characterized by identifying order combinations for multiple sales products included in each of multiple existing order data, analyzing product-specific associations among multiple products forming the identified order combinations, and determining the picking order of products included in multiple new orders by arranging the sales products based on the product-specific associations.

2. In Paragraph 1, The above processor is, An electronic device characterized by identifying the number of existing order data having the same order combination among the above multiple existing order data, and analyzing that the correlation between products forming the order combination is high in order of the largest number.

3. In Paragraph 2, The above processor is, An electronic device characterized by sorting the priority of the sales products in order of high correlation with each product and placing the sales products starting from the zone located at the position with the shortest movement path based on the starting point of a plurality of transport robots.

4. In Paragraph 3, The above processor is, An electronic device characterized by dividing a plurality of zones into a plurality of placement areas, and placing a plurality of products with high correlation to each product into a plurality of placement areas divided from the same zone.

5. In Paragraph 4, The above processor is, An electronic device characterized by determining the picking order of the above products in the order of the zone with the fewest number of working transport robots.

6. In Paragraph 5, The above processor is, An electronic device characterized by identifying the number of new orders forming the same order combination among the above multiple new orders, and determining the picking order of the above products in order of the largest number.

7. In Paragraph 6, The above processor is, An electronic device characterized by determining the picking order of the product by considering the number of zones that the transport robot must visit to pick the product for the order combination confirmed in the plurality of new orders.

8. In Paragraph 7, The above processor is, An electronic device characterized by controlling a plurality of transport robots by setting the movement paths of the plurality of transport robots according to the picking order of the above-mentioned products.

9. In Paragraph 1, The above processor is, An electronic device characterized by setting the storage location of the sales product based on the above-mentioned pre-order data using an objective function and at least one constraint.

10. A step in which the processor verifies an order combination for multiple sales products included in each of multiple pre-order data; The step of the processor analyzing product-specific associations among multiple products forming the identified order combination; and A step in which the processor arranges the sales products based on the product-specific associations to determine the picking order of products included in a plurality of new orders; A method for managing goods in a logistics center characterized by including

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