Multidimensional 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 "fruit picking" and "seeding." The so-called "fruit picking" method involves picking items for each order, with pickers and equipment patrolling each storage location, retrieving, packaging, and harvesting the items for the order to be processed. This is why it's called "fruit picking." Meanwhile, the "seeding" method, also known as "harvest first, sow later," involves combining multiple orders into a single lot, first picking each variety in bulk, like harvesting fruit, and then sorting and packaging the related orders by variety, like sowing seeds. This is why it's called "harvest first, sow later." 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 even hundreds of fruit, and is much more efficient than the "fruit picking" method. However, it also requires an additional "seeding" area, which means that after the fruit is picked, it is sorted and packaged by variety for the multiple related orders, requiring additional space. When dealing with orders of hundreds of fruit, space is required to store the cargo boxes for hundreds of orders at the same time, and the business process is long and involves multiple 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, the picking efficiency is taken into consideration, and the distribution load It is necessary to consolidate orders before picking. However, the order consolidation rules in the prior art are all single. For example, in the above-mentioned "seeding" picking method, when multiple orders, even tens or hundreds of orders, are simultaneously picked, the orders are simply consolidated for one lot in the order-received order chronological order and harvested, without any relation to other consolidation rules. CN108573423A proposes consolidating orders whose items are in the same picking area. CN108154328A proposes integrating orders based on order similarity, receiving address, etc. These relatively single consolidation rules cannot meet the needs of some warehouse centers that consider meeting customers' individual shipping needs, minimizing logistics costs, and improving picking efficiency. Therefore, a multidimensional order consolidation method needs to be developed to meet the needs of the "direct-seeding" picking method. [Means for solving the problem]

[0005] In order to solve the current problems, the present invention provides a multi-dimensional order consolidation method, which includes the steps of: step S1: acquiring order information to be processed based on predetermined conditions, and consolidating orders that satisfy the predetermined conditions into one lot; step S2: pre-consolidating orders based on the order receiving address, arrival time, and product storage location information of the orders, and placing the pre-consolidated orders into an order pool together with other orders that have not been pre-consolidated; and step S3: determining 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, assigning a single lot to the order. loadA task is generated, and if the first threshold is not exceeded, the process proceeds 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 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, one allocation is made for each of N orders determined based on the picking location set on the RFID picking cart based on the degree of coincidence of the picking location. load and generating a task.

[0006] Alternatively, the predetermined condition in step S1 may include time, area, product, or the like.

[0007] Alternatively, step S1 may include aggregating all orders received within a predetermined time range into one lot based on the order placement time of the orders, or aggregating orders whose receiving addresses belong to the same area into one lot, or aggregating orders containing a specific product into one lot.

[0008] Alternatively, step S2 may include 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 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 into the order pool without pre-integrating them.

[0010] Alternatively, step S4 may include 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 may be a step of 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 coincidence of picking positions between other orders in the processing unit and the largest order size, and calculating one allocation method for the largest order size and the N-1 orders with the highest degree of coincidence of picking positions. load generating a task and distributing the task from the processing unit; load By excluding N orders corresponding to the task and distributing all orders in that category, load and executing the above process for the remaining orders in the processing unit as new processing units until a task is generated.

[0012] Alternatively, step S5 may be performed by treating all orders sorted into the same category as one processing unit, selecting the order with the largest order size from among the processing units, calculating the degree of coincidence of picking positions between other orders and the order with the largest order size, and selecting the order with the largest order size as the one with the highest degree of coincidence of picking positions. of generating a first virtual order for the order and the maximum order; calculating a degree of coincidence of picking positions between other orders and the first virtual order; and selecting the first virtual order with the highest degree of coincidence of picking positions. of generating a second virtual order for the first virtual order and the first virtual order; ..., by analogy therewith, generating an N-th virtual order, generating one shipping task for the N orders included in the N-th virtual order; load By excluding N orders corresponding to the task and distributing all orders in that category, loadand executing the above process for the remaining orders in the processing unit as new processing units until a task is generated.

[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 in the aisle corresponding to the storage location of the product.

[0014] Alternatively, the method may select each delivery based on custom factors including arrival time, area of ​​the receiving address, order size, product included in the order, or product inventory at the warehouse center. load The method further includes a step S6 of setting priorities for the tasks. [Effects 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 location in the order, the order size, weather conditions, road condition information, transport vehicle status, and picking and logistics costs, and determines the multi-dimensional order integration method by considering the combination and arrangement order of each dimension, and can be applied to various scenarios in the warehousing field. The order processing task integrated by this method can be the simultaneous delivery of multiple orders. load This allows us to reduce costs as much as possible and improve shipping efficiency while meeting the individual shipping needs of our customers. [Brief explanation of the drawings]

[0016] In order to more clearly describe the technical aspects of 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. [Figure 2] FIG. 2 is a flow chart of a multi-dimensional order consolidation method according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] In order to make the objects, 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 seed" 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. A picking processor is installed in the RFID picking cart 1. A first inspection processor is installed in the RFID channel machine 2. The RFID channel machine 2 is installed at the cargo box insertion port of the automatic sorting line 3. A DWS device 4 is also installed 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 installed in the RFID channel machine 2. A second inspection processor is installed in the DWS device 4.

[0020] The workspace required for this "direct seeding" warehouse management system is dedicated to the installation of the warehouse 5 and the automatic sorting line 3. The warehouse is equipped with multiple rows of cargo shelves 6. A collection area 7 is installed at one end of the cargo shelves 6. The collection area 7 is used for temporarily storing packed cargo boxes and empty boxes. Each RFID picking cart 1 moves back and forth within the aisles of the cargo shelves, simultaneously carrying out the packing process of picking multiple orders, performing a primary inspection, sealing the boxes, and printing and attaching box codes. The picker places the packed cargo boxes directly into the collection area 7. A specialized transporter transports the cargo boxes to the cargo box insertion port of the automatic sorting line 3. After passing through the RFID channel machine 2, the cargo box enters the automatic sorting line 3. As 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 inside the cargo box, and performs a secondary inspection by matching the RFID information of the products inside the cargo box read by RFID. The cargo box then enters the automatic sorting line 3. The automatic sorting line 3 is equipped with multiple sorting ports. Before arriving at each sorting port, the cargo box passes through the DWS device 4. 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 onto a vehicle for transportation.

[0021] In the above system, in order for the RFID picking cart 1 to pick multiple orders simultaneously, it is necessary to consolidate all store orders received by the data processing center in advance, and only orders that have been consolidated according to predetermined conditions can be picked simultaneously by the RFID picking cart 1, thereby reducing costs as much as possible and improving picking efficiency while meeting customers' individual needs for shipping or arrival times.

[0022] A warehousing center is a warehouse and shipping center. For example, a warehousing center in the apparel industry receives orders from stores in cities across the country. Store orders are typically 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 regarding shipping and arrival times. In real-world situations, stores may place multiple separate orders within a short period of time. A warehousing center must consider the following:

[0023] (1) Loading efficiency of freight vehicles to each city. Without order consolidation, the processing time for cargo to a certain city may vary, which may cause delays in the departure of freight vehicles to that city. Therefore, when consolidating orders, it is necessary to consider the centralized processing of orders to a certain city in order to improve the loading efficiency of freight vehicles.

[0024] (2) Picking efficiency of RFID picking carts. Typically, when shipping a single order, optimal route planning can be performed based on the storage location of each item. However, when shipping multiple orders, optimal picking efficiency may not be achieved simply by optimal route planning based on the storage location of the items in each order. When consolidating orders, shipping orders containing the same items together can significantly improve picking efficiency. Alternatively, if the items are different but in adjacent storage locations, pickers can pick them nearby, which also improves picking efficiency.

[0025] (3) The difference in the scale of multiple orders processed simultaneously by RFID picking carts. One RFID picking cart can simultaneously deliver six orders. load If five orders are completed and only one remaining order requires additional time to be delivered, the picker will be tasked with delivering only this one order during this time. load And distribution load This is detrimental to improving efficiency.

[0026] (4) Weather conditions. If heavy rain occurs 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 would be unfavorable for delivery.

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

[0028] (6) Other factors, such as the condition of the transport vehicle.

[0029] The above are exemplary situations, and actual applications may include other situations that need to be considered, which are not listed here. To meet the individual needs of customers regarding shipping or arrival times, while minimizing costs, improving picking efficiency, and avoiding unnecessary hassle, this application provides a multi-dimensional order integration method, which is specifically shown in the following examples.

[0030] Example 1: This example provides a multi-dimensional order integration method. As shown in Figure 2, the method includes the following steps: In step S1, order information to be processed is obtained based on a predetermined condition, and orders that meet the predetermined condition are aggregated into one lot. The predetermined condition may include time, area, product, or other factors.

[0031] In step S2, the orders are pre-integrated based on the receiving address, arrival time, and storage location information of the products included in the orders, and the pre-integrated orders are placed in an order pool together with other orders that have not been pre-integrated, 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 does, a single delivery is made for that order. loadA task is generated, and 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 placed in a warehouse sensor. In step S4, orders whose order size does not exceed the first threshold are sorted based on the area to which the order's receiving address belongs, the arrival time, and the storage location information of the products included in the order. The area to which the order's receiving address belongs is an administrative area or an area divided based on a retail sales model. In step S5, for each of N orders in the same category, one allocation is assigned based on the degree of match of the picking location. load A task is generated, and N is determined based on the picking location set on the RFID picking cart. The picking location is an aisle where the product storage location is located, or the picking location is a specific location on the aisle corresponding to the product storage location.

[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. The order with the largest order size is selected from that processing unit. The degree of coincidence of the picking positions between other orders and the largest order is calculated, and the largest order is identified by one allocation for the N-1 orders with the highest degree of coincidence of picking positions. load A task is generated from the processing unit. load Remove N orders corresponding to the task. load The above process is executed for the remaining orders in the processing unit as new processing units until a task is generated.

[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 other orders and the largest order is calculated, and the largest order is selected as the one with the highest degree of match between the picking positions. ofA first virtual order is generated for the order and the maximum order. The degree of coincidence of the picking positions between the other orders and the first virtual order is calculated, and the first virtual order is selected from the list of orders with the highest degree of coincidence of the picking positions. of A second virtual order is generated for the first virtual order and the order itself. ... The same process is repeated until the N-1th virtual order is generated, and one shipping task is generated for the N orders included in the N-1st virtual order. load Remove N orders corresponding to the task. load The above process is executed for the remaining orders in the processing unit as new processing units until a task is generated.

[0035] Example 2: This example provides a multi-dimensional order integration method, which specifically considers 1. the arrival time required for the order, 2. the order destination, 3. the degree of coincidence of the picking location of the items in the order, 4. the order size, 5. the items in the order, 6. weather conditions, 7. transport vehicle status and road condition information, etc.

[0036] As shown in Figure 2, this embodiment takes a warehouse center as an example. This warehouse center receives orders from stores in 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 predetermined conditions, and orders that meet the predetermined conditions are consolidated into one lot. The predetermined conditions include time, area, product, and others.

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

[0038] In practical applications, 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 with a destination of Wuhan before merging orders. For example, if the transportation department notifies the warehouse center that a section of the highway between Guangzhou and the warehouse center will be closed for repairs in three days, the data processing center can select and prioritize all orders with a destination of Guangzhou.

[0039] Also, for example, if temperatures suddenly drop 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, and receiving information (including the recipient, contact phone number, receiving address).

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

[0042] In step S2.1, in actual applications, it is possible to take into account the case where multiple individual orders are placed at the same store within a short period of time, and pre-consolidate orders that simultaneously meet the following three conditions: 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 are consolidated into a single order.

[0044] In actual applications, in step S2.2, after selecting orders that simultaneously satisfy the above three conditions, 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. Taking into account that there may be large orders among multiple individual orders placed within 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 executing steps S2.1 and S2.2 above, the pre-integrated orders are placed as a single order in an order pool together with other orders that have not been pre-integrated, and the following steps are executed.

[0046] In step S3, it is determined whether the size of each order in the order pool exceeds the first threshold, and if the first threshold is exceeded, a single distribution is made for that order. load If the task is generated and 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 an 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 may include the same city, the same province, or other administrative divisions, such as the East China region and the North China region. The area divided by the retail sales model refers to the area divided by a company based on its own sales model. For example, if there are two retailers in a city, the city will be divided into two sales areas.

[0050] For example, orders that simultaneously meet the following three conditions are classified into one category: Condition 1: The receiving address belongs to the same province. Condition 2: The arrival time is within the same time period, for example, on 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 allocation is generated for each of N orders based on the degree of coincidence of the picking positions. load A picking location is defined as the aisle in which the storage location of the item is located, or as a specific location in the aisle that corresponds to the storage location of the item.

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

[0053] When a picking location is defined as a specific location in an aisle corresponding to a product storage location, the picking locations of products in relative storage locations on cargo shelves on both sides 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 coincidence of the picking positions between other orders and the largest order, and create one allocation for the largest order and the N-1 orders with the highest degree of coincidence of picking positions. loadThen, the largest order is selected from the remaining orders, and the degree of coincidence between the picking positions of each of the other orders and the largest order is calculated. All orders are integrated and distributed. load The step is repeated until a task is generated. The degree of coincidence of picking locations refers to the percentage of products in the order with the largest order size that have the same picking location as any other order, out of the total number of products in the order with the largest order size.

[0056] Method 2: Select the order with the largest order size from the same category. Calculate the degree of match between the picking locations of other orders and the largest order in order of size, and generate one virtual order for the largest order with the order that has the highest degree of match in picking location with the largest order in order of size. Calculate the degree of match between the picking locations of other orders and the virtual order in order of size, and generate one virtual order for the virtual order with the order that has the highest degree of match in picking location with the virtual order in order of size. Repeat the above steps until N orders are combined 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 have been combined and delivery tasks have been generated.

[0057] In step S6, each load Prioritize the tasks. In practical applications, the priorities may be set based on the total quantity of items included in the delivery task, or based on the arrival time, the receiving address and its affiliation, 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 with priority.

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

[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 inventory amount of each product in the warehouse center, delivery tasks relating to products with large inventory amounts are given priority.

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

[0063] In the above steps, some steps may be adjusted appropriately according to actual circumstances. For example, if the predetermined condition in step S1 is area, i.e., orders belonging to the same area are processed together as one lot, 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 product storage information included in the order, the area to which the receiving address of the order belongs is taken into consideration, and the order may belong to an area immediately below. For example, after orders belonging to the same province are grouped together as one lot in step S1, in step S4, when classifying based on the area to which the receiving address of the order belongs, it may be classified based on the same city or other level area.

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

[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] (Addendum) (Appendix 1) 1. A multi-dimensional order consolidation method, the method comprising: Step S1: Obtaining order information to be processed based on predetermined conditions, and grouping orders that satisfy the predetermined conditions into one lot; Step S2: Pre-integrating the orders based on the order receiving address, arrival time, and product storage location information 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, perform a single delivery for the order. load generating a task, and if the first threshold is not exceeded, proceeding to step S4; Step S4: sorting 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, one distribution is determined for each of the N orders determined based on the picking positions set on the RFID picking cart, based on the degree of coincidence of the picking positions. load and generating a task.

[0068] (Appendix 2) 10. The method for multi-dimensional order integration 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 aggregating all orders received within a predetermined time range into one lot based on the order placement time of the orders, or aggregating orders whose receiving addresses belong to the same area into one lot, or aggregating orders containing a specific product into one lot.

[0070] (Appendix 4) A multidimensional order consolidation method according to claim 1, wherein step S2 includes pre-consolidating into one order 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.

[0071] (Appendix 5) A multidimensional order consolidation method according to the method described in Appendix 4, wherein step S2 further includes: determining whether 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-combining the orders that simultaneously satisfy conditions 1 to 3 into one order; and if the size of the order that simultaneously satisfies conditions 1 to 3 exceeds the second threshold, placing the order directly into an order pool without pre-combining.

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

[0073] (Appendix 7) In the method described in Supplementary Note 6, 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 coincidence of picking positions between other orders in the processing unit and the largest order size, and calculating one allocation method for the largest order size and the N-1 orders with the highest degree of coincidence of picking positions. load generating a task and distributing the task from the processing unit; load By excluding N orders corresponding to the task and distributing all orders in that category, load and executing the above process for the remaining orders in the processing unit as new processing units until a task is generated.

[0074] (Appendix 8) In the method according to Supplementary Note 6, 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 the processing units; calculating the degree of coincidence of picking positions between other orders and the order with the largest order size; and selecting the order with the largest order size as the one with the highest degree of coincidence of picking positions. of generating a first virtual order for the order and the maximum order size; calculating the degree of coincidence of picking positions between other orders and the first virtual order; and selecting the first virtual order with the highest degree of coincidence of picking positions. of generating a second virtual order for the first virtual order and the first virtual order; ..., by analogy therewith, generating an N-1th virtual order, generating one shipping task from the N orders included in the N-1th virtual order; load By excluding N orders corresponding to the task and distributing all orders in that category, load and executing the above process for the remaining orders in the processing unit as new processing units until a task is generated.

[0075] (Appendix 9) 10. The multidimensional order consolidation method 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) The method according to claim 1, further comprising: determining whether each delivery is made based on custom factors including arrival time, an area to which the receiving address belongs, an order size, and an inventory amount of the products included in the order or the products at the warehouse center. load The multidimensional order integration method further comprises a step S6 of setting priorities for the tasks.

Claims

1. 1. A multi-dimensional order consolidation method, the method comprising: Step S1: Obtaining order information to be processed based on predetermined conditions, and grouping orders that satisfy the predetermined conditions into one lot; Step S2: Pre-integrating the orders based on the order receiving address, arrival time, and product storage location information included in the orders, and placing the pre-integrated orders as a single order in 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 one delivery task for that order alone; if the first threshold is not exceeded, proceed to step S4; Step S4: sorting 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 of the orders in the same category sorted by the sorting, one delivery task is generated for each of N orders determined based on the picking positions set on the RFID picking cart, 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 combining all orders received within a predetermined time range into one lot based on the order placement time of the orders, combining orders whose receiving addresses belong to the same area into one lot, or combining orders containing a specific product into one lot.

4. 2. The method of claim 1, wherein step S2 includes pre-combining 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.

5. 5. The multidimensional order consolidation method according to claim 4, wherein step S2 further includes: before pre-combining the orders that simultaneously satisfy conditions 1 to 3 into one order, determining whether the size of each of the orders that simultaneously satisfy conditions 1 to 3 exceeds a second threshold that is an artificially set threshold; and if the size of the order that simultaneously satisfies conditions 1 to 3 exceeds the second threshold, placing the order directly into an order pool without pre-combining.

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

7. 7. The method of claim 6, wherein step S5 includes the processes of 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 coincidence of picking positions between other orders in the processing unit and the order with the largest order size, generating one delivery task for the N-1 orders with the highest degree of coincidence of picking positions with the order with the largest order size and for the order with the largest order size, and excluding the N orders corresponding to the delivery task from the processing unit, and executing the process with remaining orders in the processing unit as new processing units until delivery tasks have been generated for all orders in the category.

8. 10. The method of claim 6, wherein step S5 includes the processes of: treating all orders sorted into the same category as one processing unit; for any processing unit, selecting the order with the largest order size from among the processing units; calculating the degree of coincidence of picking locations between the other orders and the order with the largest order size, generating a first virtual order for the order with the largest order size and the one order with the largest order size; calculating the degree of coincidence of picking locations between the other orders and the first virtual order, generating a second virtual order for the one order with the largest order size and the first virtual order, ..., performing similar analogy until an N-1th virtual order is generated; generating one shipping task from N orders included in the N-1th virtual order; and excluding the N orders corresponding to the delivery task from the processing unit; and executing the process with remaining orders in one processing unit as new processing units 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 the aisle corresponding to the storage location of the product.

10. 2. The method of claim 1, further comprising the step S6 of prioritizing each delivery task based on custom factors including arrival time, the area to which the receiving address belongs, the order size, and the inventory of the products included in the order or the product at the warehouse center.

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