Multi-dimensional order integration method

Abstract

The present invention provides a multi-dimensional order integration method, which belongs to the field of warehousing technology. Taking into consideration multiple dimensions such as the arrival time required for an order, the destination of the order, the degree of matching of the picking position in the order, the order size, weather conditions, road condition information, transport vehicle conditions, and picking and logistics costs, and considering the combination and arrangement order of each dimension, a multi-dimensional order integration method is determined, which can be used in various scenarios in the warehousing field. The order processing task integrated by this method can simultaneously deliver multiple orders, and on the premise of meeting the individual delivery needs of customers, can reduce costs as much as possible and improve delivery efficiency.

Description

[Technical field]

[0001] The present invention relates to a multi-dimensional order consolidation method, and belongs to the field of warehouse technology. [Background technology]

[0002] In the field of warehouse management technology, the two most basic warehouse picking methods are the "fruit picking" method and the "seeding" method. The so-called "fruit picking" method is a method in which picking is done for each order, and pickers and equipment go around to each storage location, pick the order to be processed, package it, and pick the fruit, so it is called the "fruit picking" method. On the other hand, the "seeding" method is also called "fruit picking, sowing later", and it is a method in which multiple orders are consolidated into one lot, and first, the quantity of each variety is picked together like picking fruit, and then the related multiple orders are sorted and packaged by variety, like sowing seeds, so it is called the "fruit picking, sowing later" method. Summary of the Invention [Problem to be solved by the invention]

[0003] Of the two methods mentioned above, the "seeding" picking method can simultaneously pick and sow fruit for multiple orders, sometimes even for orders of tens or hundreds of pieces, and has a much higher picking efficiency than the "fruit picking" picking method, but it also requires an additional "seeding" area, which means that after picking the fruit, an additional large space is required to sort and package the related multiple orders by variety. When dealing with orders of hundreds of pieces, a space is required to store the cargo boxes of hundreds of orders at the same time, and the business process is long and involves many processing steps.

[0004] The inventor has proposed a "direct seeding" picking method that combines the above two methods and directly realizes "seeding" for multiple orders at the same time as picking. In this method, it is necessary to consolidate orders before distribution in consideration of picking efficiency. However, the order consolidation rules in the prior art are all single. For example, in the above "seeding" picking method, when picking fruits simultaneously for multiple issues, or sometimes tens or hundreds of orders, only one lot of orders are consolidated in the order received time order and fruit harvesting is performed, and there is no relation to other consolidation rules. In the transaction order processing method, device, and system of CN108573423A, it is proposed to consolidate orders in which the products included in the order are in the same picking area. In the time presentation method, device, electronic device, and computer-readable storage medium of CN108154328A, it is proposed to consolidate orders based on the similarity of orders, the receiving address, etc. These relatively single integration rules cannot meet the needs of some warehouse centers that want to meet the personalized shipping needs of customers, reduce logistics costs as much as possible, and improve picking efficiency, so they need to develop multi-dimensional order integration methods to meet the needs of the "direct seeding" picking method. [Means for solving the problem]

[0005] To solve the current problems that exist, the present invention provides a multi-dimensional order consolidation method. The method includes: step S1: acquiring order information to be processed based on a predetermined condition, and grouping orders that satisfy the predetermined condition into one lot; step S2: pre-integrating the orders based on the receiving address of the orders, the arrival time, and storage location information of the products included in the orders, and putting the pre-integrated orders into an order pool together with other orders that have not been pre-integrated as one order, and executing step S3 described below; step S3: determining whether the size of each order in the order pool exceeds a first threshold determined based on a maximum order size that can be loaded onto an RFID picking cart, and if the first threshold is exceeded, generating one delivery task for that order alone, and if the first threshold is not exceeded, proceeding to step S4; step S4: sorting orders whose order size does not exceed the first threshold based on the area to which the receiving address of the orders belongs, the arrival time, and storage location information of the products included in the orders; and step S5: generating one delivery task for each of N orders determined based on the picking location set on the RFID picking cart for each order in the same category, based on the degree of agreement of the picking location.

[0006] Alternatively, the predetermined condition in step S1 includes time, area, product, or others.

[0007] Alternatively, step S1 may include consolidating into one lot all orders received within a predetermined time range based on the time of placing the orders, or consolidating into one lot orders whose receiving addresses belong to the same area, or consolidating into one lot orders including a particular product.

[0008] Alternatively, step S2 includes pre-integrating orders that simultaneously satisfy condition 1 that the arrival times are within the same time period, condition 2 that the receiving addresses are completely the same, and condition 3 that the included products are stored in the same area into one order.

[0009] Alternatively, step S2 further includes determining whether or not the size of each of the orders that simultaneously satisfy conditions 1 to 3 exceeds a second threshold, which is an artificially set threshold, before pre-integrating the orders that simultaneously satisfy conditions 1 to 3 into one order, and if the size of the orders that simultaneously satisfy conditions 1 to 3 exceeds the second threshold, placing the orders directly in the order pool without pre-integrating them.

[0010] Alternatively, step S4 includes sorting into one category orders that simultaneously satisfy condition 1 that the receiving address belongs to the same area, which is an administrative area or an area divided based on a retail sales model, condition 2 that the arrival time is within the same time period, and condition 3 that the included products are stored in the same area.

[0011] Alternatively, step S5 includes treating all orders sorted into the same category as one processing unit, selecting the order with the largest order size from among any processing unit, calculating the degree of match of picking positions between other orders in the processing unit and the largest order size, generating one delivery task for the largest order size and the N-1 orders with the highest degree of match of picking positions, excluding the N orders corresponding to the delivery task from the processing unit, and executing the above process with the remaining orders in the processing unit as new processing units until delivery tasks are generated for all orders in the category.

[0012] Alternatively, step S5 includes treating all orders sorted into the same category as one processing unit, selecting an order with the largest order size from among any processing unit, calculating the degree of coincidence of picking positions between other orders and the largest order size, generating a first virtual order for the largest order size and the one order with the highest degree of coincidence of picking positions with the largest order size, calculating the degree of coincidence of picking positions between other orders and the first virtual order, generating a second virtual order for the one order with the highest degree of coincidence of picking positions with the first virtual order size, ..., until an N-1th virtual order is generated, generating one shipping task for N orders included in the N-1th virtual order, excluding the N orders corresponding to the delivery task from the processing unit, and executing the above process with the remaining orders in the processing unit as new processing units until delivery tasks are generated for all orders in the category.

[0013] Alternatively, the picking location may be an aisle in which a storage location of the product is located, or the picking location may be a specific location of the aisle corresponding to the storage location of the product.

[0014] Alternatively, the method further includes a step S6 of setting a priority for each distribution task based on custom factors including arrival time, area to which the receiving address belongs, order size, and inventory of goods included in the order or goods at the warehouse center. Effect of the Invention

[0015] The present invention provides a multi-dimensional order integration method, which considers multiple dimensions such as the arrival time required for an order, the destination of the order, the degree of matching of the picking position in the order, the order size, weather conditions, road condition information, transport vehicle conditions, and picking and logistics costs, and determines the multi-dimensional order integration method by considering the combination and arrangement sequence of each dimension, and can be applied to various scenarios in the warehousing field. The order processing task integrated by this method can simultaneously deliver multiple orders, and on the premise of meeting the individual delivery needs of customers, can reduce costs as much as possible and improve delivery efficiency. [Brief description of the drawings]

[0016] In order to more clearly describe the technical aspects in the embodiments of the present invention, the following briefly describes the drawings that need to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without paying creative labor.

[0017] [Figure 1] Figure 1 is a schematic diagram of a "direct seed" warehouse management system. [Diagram 2] FIG. 2 is a flow chart of a multi-dimensional order consolidation method according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] In order to make the objectives, technical aspects and advantages of the present invention clearer, the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0019] FIG. 1 shows a "direct broadcast" warehouse management system proposed by the inventor. The system includes a data processing center, an RFID picking cart 1, an RFID channel machine 2, and an automatic sorting line 3. Here, a picking processor is arranged in the RFID picking cart 1. A first inspection processor is arranged in the RFID channel machine 2. The RFID channel machine 2 is installed at the cargo box input port of the automatic sorting line 3. A DWS device 4 is also arranged in the automatic sorting line 3. The DWS device 4 is installed in front of each sorting port of the automatic sorting line 3. A box code scanner and an RFID reader / writer are also arranged in the RFID channel machine 2. A second inspection processor is arranged in the DWS device 4.

[0020] The workshop required for this "direct seeding" warehouse management system is only used to set up the warehouse 5 and the automatic sorting line 3. Multiple rows of cargo shelves 6 are installed in the warehouse. A collection area 7 is installed at one end of the cargo shelves 6. The collection area 7 is used to temporarily store packed cargo boxes and empty boxes. Each RFID picking cart 1 moves back and forth within the aisle of the cargo shelves, simultaneously realizing the packing process of picking multiple orders, the first inspection, box sealing, printing and attaching box codes. The picker places the cargo box after packing directly in the collection area 7. The transporter who specializes in transporting the cargo boxes transports them to the cargo box insertion port of the automatic sorting line 3. After passing through the RFID channel machine 2, it enters the automatic sorting line 3. When the cargo box passes through the RFID channel machine 2, the first inspection processor reads the box code attached to the cargo box, obtains the product information in the cargo box, and performs a second inspection with the RFID information of the product in the cargo box read by RFID. The cargo box then enters the automatic sorting line 3. There are multiple sorting ports on the automatic sorting line 3. The cargo box passes through the DWS device 4 before arriving at each sorting port. The second inspection processor calculates the theoretical weight and / or volume of the corresponding cargo box based on the box code information scanned by the DWS device 4, compares it with the measured weight and / or volume of the cargo box, and completes the third inspection. After passing the inspection, the cargo box enters the corresponding sorting port according to the destination of the order, and is loaded into a vehicle for transportation.

[0021] In the above system, in order for the RFID picking cart 1 to pick multiple orders simultaneously, the orders of all stores received by the data processing center need to be consolidated in advance, and only the orders consolidated according to specified conditions can be picked simultaneously by the RFID picking cart 1, thereby reducing costs as much as possible and improving picking efficiency while meeting the individual needs of customers for shipping or arrival times.

[0022] A warehousing center is a warehouse and a shipping center. For example, a warehousing center in the apparel industry receives orders from stores in various cities across the country. Store orders are usually different from ordinary individual orders. Store orders usually include a certain number of various types of apparel products to be sold in the store. Therefore, store orders usually have requirements for shipping and arrival times. In addition, in real-life scenarios, there are cases where a store places multiple separate orders in a short period of time. A warehousing center must take the following into consideration:

[0023] (1) The loading efficiency of freight vehicles into each city. Without order consolidation, the processing time of cargo to a certain city may vary, and freight cars may not be able to depart for that city. Therefore, when consolidating orders, it is necessary to consider the centralized processing of orders to a certain city in order to increase the loading efficiency of freight cars.

[0024] (2) Picking efficiency of RFID picking carts. Typically, for single order dispatch, optimal route planning can be done based on the storage location of each item, but for multiple orders, optimal picking efficiency may not be achieved just by optimal route planning based on the storage location of the items in each order. When consolidating orders, dispatching orders containing the same items together can greatly improve picking efficiency. Or, if the items are different but the storage locations are adjacent, pickers can pick them up nearby, which also improves picking efficiency.

[0025] (3) The difference in the scale of multiple orders that RFID picking carts process simultaneously. If one RFID picking cart is simultaneously performing the distribution tasks of six orders, and five of the orders are completed, and only the distribution of the remaining order requires a lot of additional time, the picker will only be distributing this one order during this time, which is detrimental to improving distribution efficiency.

[0026] (4) Weather conditions. If heavy rain is to occur in a certain city in three days, then if orders are processed in order, an order to this city may be processed the day after tomorrow, arriving in this city on the day of the heavy rain, which is unfavorable for delivery.

[0027] (5) Road condition information. If the highway between the warehouse city and the receiving city of an order is going to be closed for repairs in three days, the order to that city needs to be processed quickly in order to be delivered before the highway is closed for repairs.

[0028] (6) Other factors, such as the condition of transportation vehicles.

[0029] The above is an example of the situation, and in actual application, there may be other situations that need to be considered, which are not listed in this application. In order to reduce costs as much as possible, improve picking efficiency, and avoid unnecessary trouble under the premise of meeting the individual needs of customers for shipping time or arrival time, this application provides a multi-dimensional order integration method, which is specifically shown in the following embodiment.

[0030] Example 1: This example provides a multi-dimensional order consolidation method. As shown in Figure 2, the method includes: In step S1, obtain order information to be processed based on a predetermined condition, and consolidate orders that meet the predetermined condition into one lot. The predetermined conditions include time, area, product, or others.

[0031] In step S2, pre-integration of orders is performed based on the receiving address, arrival time, and storage location information of the product included in the order, and the pre-integrated orders are put into an order pool together with other orders that have not been pre-integrated as one order, and the following step S3 is executed. In step S3, it is determined whether the size of each order in the order pool exceeds a first threshold, and if it exceeds the first threshold, one delivery task is generated for the order alone, and if it does not exceed the first threshold, the process proceeds to step S4. The first threshold is determined based on the maximum order size that can be loaded on the RFID picking cart arranged in the warehouse sensor. In step S4, orders whose order size does not exceed the first threshold are sorted based on the area to which the receiving address of the order belongs, the arrival time, and storage location information of the product included in the order. The area to which the receiving address of the order belongs is an administrative area or an area divided based on a sales model of a retail business. In step S5, for each order in the same category, one delivery task is generated for each N orders based on the degree of agreement of the picking position. N is determined based on the picking position set in the RFID picking cart. The picking location is an aisle in which a storage location of the product is located, or the picking location is a specific location of the aisle corresponding to the storage location of the product.

[0032] The specific method for generating a delivery task is method 1 or method 2.

[0033] Method 1: All orders sorted into the same category are treated as one processing unit. From that processing unit, select the order with the largest order size. Calculate the degree of match between the picking positions of other orders and the largest order size, and generate one delivery task for the largest order size and the N-1 orders whose picking positions most closely match those of the largest order size. Exclude the N orders corresponding to that delivery task from the processing unit. Repeat the above process with the remaining orders in the processing unit as new processing units until delivery tasks have been generated for all orders in the category.

[0034] Method 2: All orders sorted into the same category are treated as one processing unit. The order with the largest order size is selected from the processing unit. The degree of match between the picking positions of the other orders and the largest order is calculated, and a first virtual order is generated for the largest order and the order with the highest degree of match between the picking positions. The degree of match between the other orders and the first virtual order is calculated, and a second virtual order is generated for the first virtual order and the order with the highest degree of match between the picking positions. .... Analogically, one dispatch task is generated for the N orders included in the N-1 virtual order until the N orders corresponding to the delivery task are excluded from the processing unit. The remaining orders in the processing unit are treated as new processing units and the above process is executed until delivery tasks are generated for all orders in the category.

[0035] Example 2: This example provides a multi-dimensional order integration method. Specifically, the integration is performed by considering 1. the arrival time required for the order, 2. the destination of the order, 3. the degree of coincidence of the picking position of the product in the order, 4. the order size, 5. the product in the order, 6. weather conditions, 7. transport vehicle status and road condition information, etc.

[0036] As shown in Fig. 2, this embodiment takes a warehouse center as an example. This warehouse center receives orders from stores in various cities across the country. The specific order consolidation strategy adopted is as follows: In step S1, order information to be processed is obtained based on a predetermined condition, and orders that satisfy the predetermined condition are consolidated into one lot. The predetermined conditions include time, area, product, and others.

[0037] For example, all orders received within a certain time range may be grouped into one lot based on the time of placing the orders, orders whose receiving addresses belong to the same area may be grouped into one lot, and orders containing a specific product may be grouped into one lot. Other predetermined conditions may also be used depending on the actual application scenario.

[0038] In practical applications, the selected orders can be combined into one lot according to specific circumstances. For example, if the weather forecast predicts heavy rain in the Wuhan area in three days, the data processing center can select and prioritize all orders whose destination is Wuhan before consolidating orders. For example, if the traffic department notifies a section of the highway from the warehouse center to Guangzhou to be closed for repairs in three days, the data processing center can select and prioritize all orders whose destination is Guangzhou.

[0039] In addition, for example, if the temperature suddenly drops across the country and orders including a specific product such as down jackets need to be processed with priority, the data processing center will select orders including down jackets as one lot and process them with priority.

[0040] The order information includes arrival time, product type, size and corresponding quantity, receiving information (including recipient, contact phone number, receiving address), etc.

[0041] In step S2, the orders are pre-integrated based on the receiving address, arrival time and storage location information of the products contained in the orders, which are stored in the data processing center in advance.

[0042] In step S2.1, in practical applications, in consideration of the case where multiple individual orders are placed at the same store within a short period of time, orders that simultaneously satisfy the following three conditions can be pre-integrated: Condition 1: The arrival time is within the same time period, e.g., the same day; Condition 2: The receiving address is at the same store; Condition 3: The included products are stored in the same area.

[0043] After pre-consolidation, orders that are placed separately multiple times in a short period of time at the same store and contain items in the same picking location area are consolidated into a single order.

[0044] In step S2.2, in practical applications, after selecting orders that simultaneously satisfy the above three conditions, the order size is used as a secondary pre-screening condition, and pre-integration is not performed for orders whose order size exceeds a predetermined second threshold. The second threshold is an artificially set threshold. Considering that large orders may exist among multiple individual orders placed in a short period of time at the same store, the second threshold is set based on experience, and pre-integration is not performed for orders whose size exceeds the second threshold.

[0045] After steps S2.1 and S2.2 above are performed, the pre-consolidated orders are placed as one order in an order pool with other orders that have not been pre-consolidated, and the following steps are performed:

[0046] In step S3, it is determined whether the size of each order in the order pool exceeds a first threshold. If the first threshold is exceeded, a single delivery task is generated for the order. If the first threshold is not exceeded, the process proceeds to step S4. The first threshold is determined based on the maximum order size that can be loaded onto an RFID picking cart. The first threshold is greater than the second threshold.

[0047] In this step, the first threshold is determined based on the maximum order size that can be loaded onto the RFID picking cart. For example, the first threshold may be set to 800 items, and in step S2.2, the second threshold may be set to 300 items.

[0048] In step S4, orders whose order size does not exceed the first threshold are sorted based on the area to which the receiving address of the order belongs, the arrival time, and the storage location information of the product included in the order. Here, the area to which the receiving address belongs is an administrative area or an area divided based on the sales model of the retail business.

[0049] For example, the administrative region includes the same city or the same province or other administrative divisions, such as East China region, North China region, etc. The area divided by the sales model of the retail format refers to the area divided by the enterprise based on its own sales model. For example, if there are two retailers in a city, the city is divided into two sales areas.

[0050] For example, orders that simultaneously meet the following three conditions will be classified into one category: Condition 1: The receiving address belongs to the same province; Condition 2: The arrival time is within the same time zone, e.g., the same day; Condition 3: The included products are stored in the same area.

[0051] In step S5, for each order in the same category, one delivery task is generated for each of the N orders based on the matching degree of the picking location, where the picking location is defined as the aisle in which the storage location of the product is located, or the specific location of the aisle corresponding to the storage location of the product.

[0052] If a picking location is defined as an aisle of a product storage location, the picking locations of all products on both sides of the cargo shelves in the same aisle are the same.

[0053] If a picking location is defined as a specific location in an aisle corresponding to a product storage location, the picking locations of products in the relative storage locations of cargo shelves on either side of the same aisle are the same.

[0054] In practical applications, in consideration of prioritizing delivery of large-scale orders, the specific method for generating delivery tasks is method 1 or method 2.

[0055] Method 1: Select the order with the largest order size from the same category. Calculate the degree of match of the picking positions between other orders and the largest order, and generate one delivery task for the largest order and the N-1 orders with the highest degree of match of picking positions with the largest order. Then, select the largest order from the remaining orders, and calculate the degree of match of the picking positions between each of the other orders and the largest order. ... This step is repeated until all orders are integrated and a delivery task is generated. The degree of match of picking positions refers to the percentage of products with the same picking positions as any of the largest orders among the largest order size.

[0056] Method 2: Select the order with the largest order size from the same category. Calculate the degree of matching of the picking positions between the other orders and the largest order in order, and generate one order with the highest degree of matching of the picking position with the largest order and one virtual order for the largest order. Calculate the degree of matching of the picking positions between the other orders and the virtual order, and generate one order with the highest degree of matching of the picking position with the virtual order and an even larger virtual order for the virtual order. Repeat the above steps until N orders are integrated into one virtual order and one delivery task is generated for all orders in the virtual order. Then, select the largest order from the remaining orders, and repeat the above steps again until all orders are integrated and delivery tasks are generated.

[0057] In step S6, a priority is set for each delivery task. In practical application, the priority may be set based on the total amount of goods included in the delivery task, or based on the arrival time, the receiving address and its origin, or other factors.

[0058] For example, the total quantity of goods included in each delivery task is checked, and delivery tasks with a larger total quantity of goods are processed preferentially.

[0059] For example, depending on the arrival time considered in step S4, a delivery task that arrives first is given priority for processing.

[0060] For example, customize the priority of each province, city, or area based on the receiving address considered in step S4.

[0061] For example, taking into consideration the stock amount of each product in the warehouse center, delivery tasks related to products with large stock amounts are processed with priority.

[0062] The subsequent distribution process can sort and distribute items to RFID picking carts according to the set priorities.

[0063] In the above steps, some steps may be adjusted according to the actual situation. For example, in step S1, the predetermined condition is area, that is, orders belonging to the same area are processed together into one lot order, in step S4, when classifying orders whose order size does not exceed the first threshold based on the area to which the receiving address of the order belongs, the arrival time, and the storage information of the product included in the order, the area to which the receiving address of the order belongs is taken into consideration, and it may be an area belonging to the next lower level. For example, in step S1, after the orders belonging to the same province are grouped into one lot, in step S4, when classifying based on the area to which the receiving address of the order belongs, it can be classified based on the same city or other level area.

[0064] The method of the present application determines a multi-dimensional order integration method according to the combination arrangement of each dimension, and can meet the order integration response needs of the "direct broadcast" warehouse system proposed by the inventor. The order processing task integrated by this method can simultaneously deliver multiple orders, and reduce costs as much as possible and improve delivery efficiency under the premise of meeting the personalized delivery needs of customers.

[0065] Some of the steps in the embodiments of the present invention can be implemented using software, and the corresponding software program is stored in a readable storage medium, such as an optical disk or a hard disk.

[0066] The above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

[0067] (Additional Note) (Appendix 1) 1. A method for multi-dimensional order consolidation, the method comprising: Step S1: Obtaining order information to be processed based on a predetermined condition, and consolidating orders that satisfy the predetermined condition into one lot; Step S2: Pre-integrating the orders based on the receiving address, arrival time, and storage location information of the products included in the orders, and putting the pre-integrated orders into an order pool together with other orders that have not been pre-integrated, and then executing the following step S3; Step S3: Determine whether the size of each order in the order pool exceeds a first threshold determined based on the maximum order size that can be loaded onto an RFID picking cart, and if the first threshold is exceeded, generate a single delivery task for the order, and if the first threshold is not exceeded, proceed to step S4; Step S4: Sort the orders whose order size does not exceed the first threshold according to the area to which the receiving address of the order belongs, the arrival time, and the storage location information of the product included in the order; Step S5: For each order in the same category, for each of N orders determined based on the picking positions set on the RFID picking cart, generating one delivery task based on the degree of coincidence of the picking positions.

[0068] (Appendix 2) A multi-dimensional order consolidation method according to claim 1, wherein the predetermined conditions in step S1 include time, area, product, or the like.

[0069] (Appendix 3) A multidimensional order consolidation method according to the method described in Appendix 2, wherein step S1 includes consolidating into one lot all orders received within a predetermined time range based on the order time of the order, or consolidating into one lot orders whose receiving addresses of the orders belong to the same area, or consolidating into one lot orders including a specific product.

[0070] (Appendix 4) A multidimensional order consolidation method according to the method described in Appendix 1, characterized in that step S2 includes pre-consolidating into one order orders that simultaneously satisfy condition 1 that the arrival times are within the same time zone, condition 2 that the receiving addresses are completely the same, and condition 3 that the included products are stored in the same area.

[0071] (Appendix 5) A multidimensional order consolidation method according to the method described in Appendix 4, wherein step S2 further includes: determining whether or not a size of each of the orders that simultaneously satisfy conditions 1 to 3 exceeds a second threshold, which is an artificially set threshold, before pre-combining the orders that simultaneously satisfy conditions 1 to 3 into one order; and if a size of an order that simultaneously satisfies conditions 1 to 3 exceeds the second threshold, placing the order as is in an order pool without pre-combining the orders.

[0072] (Appendix 6) A multidimensional order consolidation method according to the method described in Appendix 1, characterized in that step S4 includes classifying into one category orders that simultaneously satisfy condition 1 that the receiving address belongs to the same area, which is an administrative area or an area divided based on a retail sales model, condition 2 that the arrival time is within the same time period, and condition 3 that the included products are stored in the same area.

[0073] (Appendix 7) A multidimensional order consolidation method according to the method described in Supplementary Note 6, wherein step S5 includes treating all orders sorted into the same category as one processing unit, selecting an order having a maximum order size from among any processing unit, calculating a degree of match between picking positions of other orders in the processing unit and the maximum order size, generating one delivery task for the maximum order size and N-1 orders having the highest degree of match between the picking positions of the maximum order size and the processing unit, excluding the N orders corresponding to the delivery task from the processing unit, and performing the above process with the remaining orders in the processing unit as a new processing unit until delivery tasks are generated for all orders in the category.

[0074] (Appendix 8) a processing unit for processing the order having the largest order size among the processing units; calculating a degree of coincidence of picking positions between other orders and the largest order size, and generating a first virtual order for the largest order size and the order having the highest degree of coincidence of picking positions between the other orders and the first virtual order size, and generating a second virtual order for the first virtual order and the order having the highest degree of coincidence of picking positions between the other orders and the first virtual order size, ..., until an (N-1)th virtual order is generated, generating one shipping task from N orders included in the (N-1)th virtual order, excluding the N orders corresponding to the delivery task from the processing unit, and performing the above process with the remaining orders in the processing unit as a new processing unit until delivery tasks are generated for all orders in the category.

[0075] (Appendix 9) 2. The method for multi-dimensional order consolidation according to claim 1, wherein the picking location is an aisle in which a storage location of the product is located, or the picking location is a specific location of the aisle corresponding to the storage location of the product.

[0076] (Appendix 10) A multidimensional order consolidation method according to the method described in Appendix 1, further comprising a step S6 of setting a priority for each distribution task based on custom factors including arrival time, area to which the receiving address belongs, order size, and inventory of products included in the order or products at the warehouse center.

Claims

1. 1. A method for multi-dimensional order consolidation, the method comprising: Step S1: acquiring order information to be processed based on a predetermined condition, and consolidating orders that satisfy the predetermined condition into one lot; Step S2: Pre-integrating the orders based on the receiving address, arrival time, and storage location information of the products included in the orders, and putting the pre-integrated orders into an order pool together with other orders that have not been pre-integrated, and then executing the following step S3; Step S3: Determine whether the size of each order in the order pool exceeds a first threshold determined based on the maximum order size that can be loaded onto an RFID picking cart. If the first threshold is exceeded, generate a single delivery task for the order. If the first threshold is not exceeded, proceed to step S4. Step S4: Sort out orders whose order size does not exceed a first threshold based on the area to which the receiving address of the order belongs, the arrival time, and storage location information of the product included in the order; Step S5: For each order in the same category, for each of N orders determined based on the picking positions set on the RFID picking cart, generating one delivery task based on the degree of coincidence of the picking positions.

2. 2. The method of claim 1, wherein the predetermined conditions in step S1 include time, area, product, or the like.

3. 3. The method of claim 2, wherein step S1 includes consolidating into one lot all orders received within a predetermined time range based on the order time of the order, consolidating into one lot orders whose receiving addresses belong to the same area, or consolidating into one lot orders including a specific product.

4. 2. The method according to claim 1, wherein said step S2 includes pre-integrating into one order those orders which simultaneously satisfy condition 1 that the arrival times are within the same time zone, condition 2 that the receiving addresses are completely the same, and condition 3 that the included products are stored in the same area.

5. The multidimensional order consolidation method according to claim 4, wherein step S2 further includes: determining whether or not a size of each of the orders that simultaneously satisfy conditions 1 to 3 exceeds a second threshold, which is an artificially set threshold, before pre-integrating the orders that simultaneously satisfy conditions 1 to 3 into one order; and if a size of an order that simultaneously satisfies conditions 1 to 3 exceeds the second threshold, placing the order as is into an order pool without pre-integrating the orders.

6. 2. A method for multidimensional order consolidation according to claim 1, wherein step S4 includes classifying into one category orders that simultaneously satisfy condition 1 that the receiving address belongs to the same area, which is an administrative area or an area divided based on a retail sales model, condition 2 that the arrival time is within the same time period, and condition 3 that the included products are stored in the same area.

7. 7. The method according to claim 6, wherein step S5 includes treating all orders sorted into the same category as one processing unit, selecting an order having a maximum order size from among any processing unit, calculating a degree of match between picking positions of other orders in the processing unit and the maximum order size, generating one delivery task for N-1 orders having the highest degree of match between the maximum order size and the maximum order size, excluding N orders corresponding to the delivery task from the processing unit, and executing the above process with remaining orders in the processing unit as new processing units until delivery tasks are generated for all orders in the category.

8. 7. The method according to claim 6, wherein the step S5 includes: treating all orders sorted into the same category as one processing unit; selecting an order having a maximum order size from among the processing units; calculating a degree of coincidence of picking positions between other orders and the maximum order size, and generating a first virtual order for the one order having the highest degree of coincidence of picking positions with the maximum order size and the maximum order size; calculating a degree of coincidence of picking positions between other orders and the first virtual order, and generating a second virtual order for the one order having the highest degree of coincidence of picking positions with the first virtual order and the first virtual order size; ..., until an N-1th virtual order is generated, generating one dispatch task from N orders included in the N-1th virtual order; excluding N orders corresponding to the delivery task from the processing unit; and executing the above process with remaining orders in the processing unit as a new processing unit until delivery tasks are generated for all orders in the category.

9. 2. The method of claim 1, wherein the picking location is an aisle in which a storage location of the product is located, or the picking location is a specific location of an aisle corresponding to the storage location of the product.

10. 2. The method according to claim 1, further comprising a step S6 of setting a priority for each distribution task based on custom factors including arrival time, area to which the receiving address belongs, order size, and inventory of the product included in the order or the product in the warehouse center.

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