WO2018188476A1

WO2018188476A1 - Method and device for optimizing combination of orders

Info

Publication number: WO2018188476A1
Application number: PCT/CN2018/080525
Authority: WO
Inventors: 李建奇
Original assignee: 北京京东尚科信息技术有限公司; 北京京东世纪贸易有限公司
Priority date: 2017-04-10
Filing date: 2018-03-26
Publication date: 2018-10-18
Also published as: CN108694636A

Abstract

Provided in the present invention are a method and device for optimizing the combination of orders, allowing the combination of orders be automatically completed for similar orders, and increasing production efficiency. The method comprises: when an order enters an order pool, determining the priority of the order; setting the order of the highest priority in a current wave of the order pool as a foundation order, creating an aggregated order with the foundation order serving as the foundation; selecting in the order pool an order between which and the foundation order a preset similarity criterion is satisfied, adding into the aggregated order; and when the aggregated order satisfies a preset aggregation criterion, completing the combination of orders.

Description

Method and device for optimizing group list

Technical field

The present invention relates to the field of computer technologies, and in particular, to a method and apparatus for optimizing a group list.

Background technique

With the development of e-commerce, the storage management of large-scale retail e-commerce enterprises has different requirements from the management of traditional warehouse operations. Large retail e-commerce companies receive orders from users 24/7. The maximum order volume for a single warehouse will exceed 1 million orders, and the order in the order pool will exceed 300,000 orders.

The current warehouse picking task scheduling technology uses the following methods:

1. Calculate the walking distance according to the storage location of the order, select the walking distance as short as possible, and arrange the order production. Referring to Figure 1, the grid represents the shelf, the black squares indicate the location of the goods in the order, and the dashed lines indicate the calculated walking routes.

2. The area of the warehouse is divided into work areas, and the order production is arranged.

In the process of implementing the present invention, the inventors have found that at least the following problems exist in the prior art: the method for calculating the walking route is too large for the warehouse of more than 300,000 order pools, and the calculation method is too large to be used; Improve work efficiency and make it difficult to optimize production.

Summary of the invention

In view of this, the embodiments of the present invention provide a method and apparatus for optimizing a group list, which can automatically complete a group order of similar orders and improve production efficiency.

In order to achieve the above object, according to an aspect of the present invention, a method for optimizing a group order is provided, comprising: determining an priority of the order after the order enters the order pool; and prioritizing the current wave of the order pool The highest order is set as the base order, and the collection order is established based on the basic order; the order that meets the preset similarity condition between the order pool and the basic order is selected, and the collection order is added; When the collection order satisfies the preset collection condition, the group order is completed.

Optionally, the determining the priority of the order comprises: calculating a priority of the order according to the quantity of the order, the quantity of the order storage, and/or the volume of all the goods in the order.

Optionally, selecting an order that meets a preset similarity condition between the order pool and the basic order, and adding the collection order includes: determining a similarity between an order in the order pool and the basic order The orders in the order pool are sequentially added to the collection order in order of similarity from high to low.

Optionally, the determining the similarity between the order in the order pool and the basic order includes: according to the storage location of the goods in the order, the similar storage location of the goods in the order, the owner information, and/or other information, Determining the similarity between the order in the order pool and the base order.

Optionally, selecting an order that meets a preset similarity condition between the order pool and the basic order further includes: dividing the order in the current wave of the current order pool into at least a preset time point. 2 intervals; the orders in the order pool are divided into corresponding intervals according to the order release time; when an order that satisfies the preset similarity condition between the order pool and the basic order is selected, the priority is selected An order in which the underlying order is located in the same stated interval.

In order to achieve the above object, according to another aspect of an embodiment of the present invention, an apparatus for optimizing a group list is provided, including: a priority calculation module, configured to determine a priority of the order after the order enters the order pool; a single generation module, configured to set an order with the highest priority among the current wave pools of the current order pool as a basic order, and establish a collection order based on the basic order; a collection order processing module, configured to select the order pool and the location The order that meets the preset similarity condition between the basic orders is added to the collection list; the group single module is configured to complete the group list when the collection order satisfies the preset collection condition.

Optionally, the priority calculation module is further configured to calculate a priority of the order according to the quantity of the order, the quantity of the order storage, and/or the volume of all the goods in the order.

Optionally, the collection order processing module includes: a similarity calculation module, configured to determine a similarity between an order in the order pool and the basic order; an order merge module, configured to use a similarity from high to low The order in which the orders in the order pool are sequentially added to the collection list.

Optionally, the similarity calculation module is further configured to determine an order in the order pool and the basic order according to a storage location of the goods in the order, a similar storage location of the goods in the order, cargo owner information, and/or other information. Similarity between the two.

Optionally, the collection order processing module further includes: a deadline management module, configured to divide an order in a current wave of the current order pool into at least two intervals according to a preset time point; The order in the current scheduling wave is divided into corresponding intervals according to the order release time; and is also used for preferentially selecting an order that satisfies the preset similarity condition between the order pool and the basic order. The base order is located in the same range of orders.

In order to achieve the above object, according to still another aspect of the embodiments of the present invention, an AA electronic device is provided, including:

One or more processors;

a storage device for storing one or more programs,

When the one or more programs are executed by the one or more processors, such that the one or more processors enable at least: determining an order of the order after the order enters the order pool; The order with the highest priority among the current wave of the pool is set as the basic order, and the collection order is established based on the basic order; and the order that meets the preset similarity condition between the order pool and the basic order is selected, and the order is added. The collection list is completed; when the collection list satisfies the preset collection condition, the group order is completed.

In order to achieve the above object, according to still another aspect of the embodiments of the present invention, a computer readable medium storing a computer program, the program being executed by the processor at least: after the order enters the order pool, determining the location Determining an order of the order; setting an order with the highest priority among the current wave of the order pool as a base order, establishing a collection order based on the basic order; and selecting between the order pool and the basic order The order of the preset similarity condition is added to the collection list; when the collection order satisfies the preset collection condition, the group order is completed.

One embodiment of the above invention has the following advantages or advantages: since the base order is selected with priority and based on it, searching for other similar orders to form a collection order for the technical means of arranging subsequent picking solves the existing The technology can not adapt to the large-scale storage needs, can not effectively improve the technical problems of production efficiency, and achieve the technical effect of improving the efficiency of storage and scheduling. By adopting the appropriate priority calculation method, the order with higher priority is used as the base order, and when other orders are selected, the similarity between other orders and the basic order will be higher, and the position of the goods in the two orders will be more easily matched. It can effectively reduce the walking distance of staff and improve the efficiency of picking. By adding time conditions to the group order, it is better able to adapt to the time limit requirements of large-scale storage, effectively avoiding order delays and improving the user experience.

Further effects of the above-described non-conventional alternatives will be described below in connection with specific embodiments.

DRAWINGS

The drawings are intended to provide a better understanding of the invention and are not intended to limit the invention. among them:

1 is a schematic diagram of prior art group scheduling according to the storage location of an order in the prior art;

2 is a schematic diagram of main steps of a method for optimizing a group list according to an embodiment of the present invention;

3 is a schematic diagram of main modules of an apparatus for optimizing a group list according to an embodiment of the present invention;

4 is a block diagram showing the structure of a computer system suitable for implementing a terminal device or a server of an embodiment of the present invention.

detailed description

The exemplary embodiments of the present invention are described with reference to the accompanying drawings, and are in the Therefore, it will be apparent to those skilled in the art that various modifications and changes may be made to the embodiments described herein without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The embodiment of the invention provides a method for combining orders, firstly calculating an order priority according to a preset rule, and then establishing a collection order based on the order with the highest priority, and adding other orders satisfying the similarity and the like to the combination list. To complete the group order. In this way, it can meet the calculation requirements of large-scale warehousing, and because the similarity between the orders in the collection order is high, the efficiency can be effectively improved during picking, thereby improving the warehousing production efficiency.

2 is a schematic diagram of the main steps of a method of optimizing a group list according to an embodiment of the present invention.

As shown in FIG. 2, a method for optimizing a group list according to an embodiment of the present invention mainly includes the following steps:

S20: After the order enters the order pool, calculate the priority of the order. The priority of the order is determined by factors such as the quantity of the goods in the order (the quantity, volume, weight, etc. of the goods), the distribution of the goods in each storage place, and the total volume of the goods. The higher the quantity of goods, the more storage positions are covered, and the larger the capacity required for picking, the higher the priority.

S21: Obtain an order with the highest priority among the current scheduling waves as a basic order, and build a collection order based on the basic order. A collection order can be viewed as a larger “order”, but to be precise a collection of orders. The collection order based on the base order initially contains only one order for the base order. In the subsequent steps, the number of orders included in the collection order will gradually increase.

S22. Select an order in the order pool that meets a preset similarity condition between the basic order and join the collection order. In the process of adding other orders in the order pool to the collection order, an order that satisfies a preset similarity condition with the basic order is preferentially selected; for example, an order with a high similarity to the basic order is preferentially selected, and the orders are The information of the originator, the storage location of the goods in the order is similar to the basic order, and a considerable part of the picking can be completed together, thereby reducing the walking distance of the staff and improving the picking efficiency. When calculating the similarity, it can be completed by a search engine such as elastic search, which can meet the calculation requirements of large-scale storage.

S23. When the collection list satisfies a preset collection condition, the group order is completed. There is no limit to the number of orders included in the collection order: for example, when picking goods by picking trucks, the capacity of the picking trucks is limited, and the goods that can be accommodated at one time are also limited; in addition, the carrying capacity of the picking trucks is limited. Therefore, the goods contained in the order within the collection order are subject to restrictions. According to these restrictions, or the combination of these restrictions, an aggregation condition is established, and when the goods of each order in the collection order reach the collection condition, it is determined that the order is completed.

By performing the above steps S20-S23, the assembly order process of the collection order is completed, and the order added to the collection order is taken out from the order pool; by repeating the above steps, the number of orders in the order pool can be gradually reduced.

As can be seen from the above, the method for optimizing the group list provided by one embodiment of the present invention, because the priority order is selected, and based on the basic order, searches for other similar orders to form a collection order for scheduling subsequent picking. The invention solves the problem that the prior art cannot adapt to the large-scale storage requirement and cannot effectively improve the production efficiency, and achieves the technical effect of improving the storage and discharge efficiency.

In some optional embodiments, S20, calculating the priority of the order, including:

The priority of the order is calculated based on the quantity of the order, the quantity of the order storage, and/or the volume of all the goods in the order. As explained above, the priority of the order is determined by factors such as the quantity of the goods in the order (the quantity, volume, weight, etc. of the goods), the distribution of the goods in each storage place, and the total volume of the goods. How much is equal. In general, the more goods, the more storage positions are covered, and the larger the capacity required for picking, the higher the priority. The calculation of the priority-related parameters in this step, as well as the calculation of the order priority in the previous step, have high requirements for concurrent computing power, and can be processed by distributed computing tools, such as akaka-based Actor concurrent model processing. .

The quantity of goods in the order is used to describe the status of the goods in the order, such as the quantity and weight of the goods; the distribution of the goods in each storage position can reflect the area covered by the order and the difficulty of picking the goods. The amount of cargo can roughly indicate the time it takes for the order to pick up, and the larger the amount of cargo, the more time it takes. Due to the different storage distribution, the two orders have the same order quantity, but the time taken for picking up the goods can be completely different. For example, if an order has 4 goods, which are distributed in different storage positions, then work at the time of picking. The personnel will go to the four storage positions to pick up the goods separately to complete the order; the other order also has 4 goods, but all of the same goods, then the staff can complete the order directly in the same storage position. It can be seen that the quantity of goods in the order and the distribution of the storage space together determine the time it takes to complete the order.

The following is an example to briefly explain the relevant content of the priority calculation.

With order A and order B, the status of the two orders is as follows:

Order A, containing 4 kinds of goods, as follows:

[Goods 1, storage 1, cargo volume = 2]

[Goods 2, storage 2, cargo quantity=1]

[Goods 3, storage level 3, cargo quantity = 2]

[Goods 4, storage 4, cargo volume = 1]

Order B, containing 2 kinds of goods, as follows:

[Goods 1, storage 1, cargo volume = 2]

[Goods 2, storage 2, cargo quantity=1]

Then the quantity of goods in order A = 4 + 2 + 2 + 1 = 9

The quantity of goods in order B = 4 + 2 = 6

The number of deposits in order A = 4

The number of deposits in order B = 2

The volume of the goods of order A and order B can be further calculated based on the pre-recorded data, for example, according to the SKU. SKU is the abbreviation of Stock Keeping Unit. That is, the basic unit of inventory in and out measurement can be in units of parts, boxes, trays, and the like. SKU This is a necessary method for logistics management of large chain supermarket DC (distribution center). It has now been extended to the abbreviation of Product Uniform Number, each product has a unique SKU number. Single product: For a product, when its brand, model, configuration, grade, color, packaging capacity, unit, date of manufacture, shelf life, use, price, origin and other attributes are different from other products, it can be called a Single product.

The total volume of the goods in the order determines the volume of the warehouse that needs to be occupied when picking. In a single pick, the warehouse that wants to pick the truck is filled as much as possible to maximize efficiency. Therefore, as the basic order of the collection order, the total volume of the goods should be as large as possible in order to reach the target of filling the warehouse of the truck.

The calculation of the order priority can be expressed by a calculation formula as follows:

Order Priority = Cargo Quantity * Density Weight + Order Storage Quantity * Storage Quantity Weight + Volume of All Items in Order * Volume Weight. The density weight, the number of storage bits and the weight of the volume can be determined according to actual needs.

According to the method of the embodiment, when the order with the higher priority is used as the basic order, and then the other orders are selected, the similarity between the other orders and the basic order will be higher, and the position of the goods stored in the two orders will be more easily matched, which can be effective. Reduce the walking distance of staff and improve the efficiency of picking.

In some optional embodiments, S22, selecting an order between the order pool and the basic order that meets a preset similarity condition, and adding the order, includes:

Calculating the similarity between the order in the order pool and the base order. Orders with high similarity indicate that they contain all or part of the same kind of goods, the same or close storage of goods, the same order originator, etc., which can be effectively reduced when picking according to the collection of orders with high similarity. The walking distance of the staff and the efficiency of picking.

Orders in the order pool are sequentially added to the collection order in order of high to low similarity.

When performing the above sorting step, due to some special circumstances, it is not possible to completely rely on the similarity selection order to join the collection order. For example, the total quantity of goods for each order in the collection order is almost up to the capacity of the trucking warehouse, and the quantity of the next order in the order of similarity will be greater than the remaining capacity of the trucking warehouse. Then at this time you can continue to find other orders with lower similarity until you find an order that meets the capacity conditions and complete the order. It can be seen that the similarity is the main reference value when selecting an order to join the collection list, but for special considerations, the selection of the order can also be combined with other requirements.

In some optional embodiments, the calculating the similarity between the order in the order pool and the base order includes:

The similarity between the order in the order pool and the base order is calculated based on the storage location of the goods in the order, the similar storage location of the goods in the order, the owner information, and/or other information. Each order contains some project information, such as cargo code, cargo name, cargo storage, similar storage, cargo owner information and other information. These project information is the basis for judging the similarity between two orders. The item to be explained is “similar storage place”, and the existing order does not include this feature. In this embodiment, since the goods considering similar storage positions can be taken out together at the time of picking, the project is added. . A similar storage location may include other storage locations where the actual storage location of the cargo is adjacent, relative or separated by a storage location, ie a storage location close to the actual storage location of the cargo, which facilitates the picking of the goods by the staff; by adding "near storage" The project can expand the original point-like cargo storage area into small areas, so that the accuracy of the similarity calculation can be effectively improved when judging the order similarity.

In the previous embodiment, it is pointed out that the similarity is the main reference value when the order is added to the collection list, but for special considerations, the selection of the order can also be combined with other requirements. For example, due to the consideration of the order shipping time, the time limit can be further added. In the process of warehousing and logistics scheduling, it is arranged according to the time of “waves”. For example, there are 8 waves in a day, 8 to 10 is a wave (the length of one wave is different during day and night). For example, one wave at 4 hours in the evening, one wave at 2 hours during the day, etc., there is no interval between two adjacent waves. The order for each wave to complete the order is determined based on the predetermined time limit of the order. The solution of the embodiment of the present invention can be set to start at the beginning of each wave, and the order group list is executed until the order processing of the group order in the wave is completed, and then the next wave is processed again.

In some optional embodiments, S22, selecting an order between the order pool and the basic order that meets a preset similarity condition, further includes:

The current scheduling wave is divided into at least two intervals according to a preset time point. Although multiple orders are in the same wave, but the actual order release time is not the same, then the order can be divided according to the time of the order, combined with the order quantity of the wave, and the order is divided into time groups. . For example, place an order with an earlier order release time in the upper range.

The orders in the order pool and in the current scheduling wave are divided into the intervals according to the order release time. For example, if the wave is between 8:00 and 10:00, it can be an interval every half hour, divide the wave into 4 intervals, and put the wave order into these intervals according to the above classification.

When an order that satisfies a preset similarity condition is selected between the order pool and the base order, an order that is in the same section as the base order is preferentially selected. In some special cases, such as when the order quantity is very large, the orders in each interval may not be completed in the interval, and the orders that are not completed will be classified into the next interval, and the order will be continued.

The method of the embodiment can better adapt to the time limit requirement of the large-scale storage by adding the time condition in the group order, effectively avoiding the order delay and improving the user experience.

According to the method for optimizing the group list according to the embodiment of the present invention, it is found that the priority order is selected, and based on the basic order, searching for other similar orders to form a collection order for the technical means of arranging subsequent picking is solved. The prior art cannot meet the technical problems of large-scale storage requirements and cannot effectively improve production efficiency, and achieves the technical effect of improving storage and scheduling efficiency. By adopting the appropriate priority calculation method, the order with higher priority is used as the base order, and when other orders are selected, the similarity between other orders and the basic order will be higher, and the position of the goods in the two orders will be more easily matched. It can effectively reduce the walking distance of staff and improve the efficiency of picking. By adding time conditions to the group order, it is better able to adapt to the time limit requirements of large-scale storage, effectively avoiding order delays and improving the user experience.

The apparatus 30 for optimizing the group in the embodiment of the present invention mainly includes: a priority calculation module 301, a collection order generation module 302, a collection order processing module 303, and a group list module 304. among them:

The priority calculation module 301 can be configured to calculate the priority of the order after the order enters the order pool; the collection order generation module 302 can be configured to obtain the highest priority order among the current scheduling waves as the basic order, and the basic order The collection order processing module 303 is configured to select an order that meets a preset similarity condition between the order pool and the basic order, and join the collection order; the group order module 304 can be used to When the single collection condition is met, the group order is completed.

In some optional embodiments, the priority calculation module 301 is further configured to calculate a priority of the order according to an order quantity of the order, an order storage quantity, and/or a volume of all goods in the order.

In some optional embodiments, the collective order processing module 303 includes a similarity calculation module and an order merge module. among them:

The similarity calculation module may be configured to calculate a similarity between the order in the order pool and the basic order; the order merge module may be configured to sequentially add the order in the order pool to the order according to the order of similarity from high to low Collection list.

In some optional embodiments, the similarity calculation module is further configured to calculate an order in the order pool according to a storage location of the goods in the order, a similar storage location of the goods in the order, cargo owner information, and/or other information. The similarity between the orders with the highest priority.

In some optional embodiments, the collective processing module 303 further includes:

a term management module, configured to divide the current scheduling wave into at least two intervals according to a preset time point; and divide an order in the order pool and the current scheduling wave into the interval according to an order release time And when used to select an order that satisfies a preset similarity condition between the order pool and the base order, preferentially selecting an order that is in the same section as the base order.

According to the apparatus for optimizing the group list according to the embodiment of the present invention, the prior art is solved because the base order is selected by priority and the other similar orders are combined to form a collection order based on the basic order for scheduling subsequent picking. It is unable to adapt to the problems of large-scale storage and can not effectively improve production efficiency, and achieves the technical effect of improving the efficiency of storage and scheduling. By adopting the appropriate priority calculation method, the order with higher priority is used as the base order, and when other orders are selected, the similarity between other orders and the basic order will be higher, and the position of the goods in the two orders will be more easily matched. It can effectively reduce the walking distance of staff and improve the efficiency of picking. By adding time conditions to the group order, it is better able to adapt to the time limit requirements of large-scale storage, effectively avoiding order delays and improving the user experience.

According to an embodiment of the invention, the invention also provides an electronic device and a readable storage medium.

Referring now to Figure 4, there is shown a block diagram of a computer system 400 suitable for use in implementing a terminal device in accordance with an embodiment of the present invention. The terminal device shown in FIG. 4 is merely an example and should not impose any limitation on the function and scope of use of the embodiments of the present invention.

As shown in FIG. 4, computer system 400 includes a central processing unit (CPU) 401 that can be loaded into a program in random access memory (RAM) 403 according to a program stored in read only memory (ROM) 402 or from storage portion 408. And perform various appropriate actions and processes. In the RAM 403, various programs and data required for the operation of the system 400 are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input/output (I/O) interface 405 is also coupled to bus 404.

The following components are connected to the I/O interface 405: an input portion 406 including a keyboard, a mouse, etc.; an output portion 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a storage portion 408 including a hard disk or the like. And a communication portion 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the Internet. Driver 410 is also coupled to I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like is mounted on the drive 410 as needed so that a computer program read therefrom is installed into the storage portion 408 as needed.

In particular, the process described in the schematic diagram of the main steps above may be implemented as a computer software program in accordance with an embodiment of the present invention. For example, an embodiment of the invention includes a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the method of the main steps. In such an embodiment, the computer program can be downloaded and installed from the network via the communication portion 409, and/or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, the above-described functions defined in the system of the present invention are performed.

It should be noted that the computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in connection with an instruction execution system, apparatus or device. In the present invention, a computer readable signal medium may include a data signal that is propagated in the baseband or as part of a carrier, in which computer readable program code is carried. Such propagated data signals can take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer readable signal medium can also be any computer readable medium other than a computer readable storage medium, which can transmit, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device. . Program code embodied on a computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality and operation of possible implementations of systems, methods and computer program products in accordance with various embodiments of the invention. In this regard, each block of the flowchart or block diagrams can represent a module, a program segment, or a portion of code that includes one or more Executable instructions. It should also be noted that in some alternative implementations, the functions noted in the blocks may also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams or flowcharts, and combinations of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be used A combination of dedicated hardware and computer instructions is implemented.

The modules involved in the embodiments of the present invention may be implemented by software or by hardware. The described modules may also be disposed in the processor. For example, the description may be as follows: A processor includes a priority calculation module, a collection order generation module, a collection single processing module, and a group single module. The name of these units does not constitute a limitation on the unit itself in some cases. For example, the group module can also be described as “a module that completes the group when the collection list satisfies the preset collection condition”. .

In another aspect, the present invention also provides a computer readable medium, which may be included in the apparatus described in the above embodiments, or may be separately present and not incorporated in the apparatus. The computer readable medium carries one or more programs, and when the one or more programs are executed by the device, the device includes:

After the order enters the order pool, determining the priority of the order; setting the order with the highest priority among the current wave of the order pool as the base order, and establishing the collection order based on the basic order; selecting the order The order that meets the preset similarity condition between the pool and the basic order is added to the collection list; when the collection order satisfies the preset collection condition, the group order is completed.

According to the technical solution of the embodiment of the present invention, since the basic order is selected by using the priority order and searching for other similar orders to form a collection order based on the basic order for scheduling subsequent picking, the prior art cannot be adapted to the large-scale storage requirement. The problem of not being able to effectively improve production efficiency has reached the technical effect of improving the efficiency of storage and scheduling. By adopting the appropriate priority calculation method, the order with higher priority is used as the base order, and when other orders are selected, the similarity between other orders and the basic order will be higher, and the position of the goods in the two orders will be more easily matched. It can effectively reduce the walking distance of staff and improve the efficiency of picking. By adding time conditions to the group order, it is better able to adapt to the time limit requirements of large-scale storage, effectively avoiding order delays and improving the user experience.

The above specific embodiments do not constitute a limitation of the scope of the present invention. Those skilled in the art will appreciate that a wide variety of modifications, combinations, sub-combinations and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims

A method for optimizing a group list, comprising:

After the order enters the order pool, the priority of the order is determined;

Setting an order with the highest priority among the current wave of the order pool as a basic order, and establishing a collection order based on the basic order;

And selecting an order in the order pool and the basic order that meets a preset similarity condition, and adding the order;

When the collection list satisfies the preset collection condition, the group order is completed.
The method of claim 1 wherein said determining a priority of said order comprises:

The priority of the order is calculated based on the quantity of the order, the quantity of the order storage, and/or the volume of all the goods in the order.
The method according to claim 1, wherein the order that meets the preset similarity condition between the order pool and the basic order is selected, and the order is added, including:

Determining a similarity between the order in the order pool and the base order;

Orders in the order pool are sequentially added to the collection order in order of high to low similarity.
The method of claim 3, wherein the determining the similarity between the order in the order pool and the base order comprises:

The similarity between the order in the order pool and the base order is determined based on the storage location of the goods in the order, the similar storage location of the goods in the order, the owner information, and/or other information.
The method of claim 1, wherein selecting an order between the order pool and the base order that satisfies a preset similarity condition further comprises:

According to the preset time point, the order in the current wave of the current order pool is divided into at least two intervals;

The orders in the order pool are divided into corresponding intervals according to the order release time;

When an order that satisfies a preset similarity condition is selected between the order pool and the base order, an order that is in the same section as the base order is preferentially selected.
An apparatus for optimizing a group list, comprising:

a priority calculation module, configured to determine a priority of the order after the order enters the order pool;

a collection order generating module, configured to set an order with the highest priority among the current wave of the current order pool as a basic order, and establish a collection order based on the basic order;

a collection order processing module, configured to select an order that meets a preset similarity condition between the order pool and the basic order, and join the collection order;

The group single module is configured to complete the group list when the collection list satisfies a preset collection condition.
The apparatus according to claim 6, wherein the priority calculation module is further configured to calculate the priority of the order according to the quantity of the order, the quantity of the order storage, and/or the volume of all the goods in the order. level.
The apparatus according to claim 6, wherein the collective processing module comprises:

a similarity calculation module, configured to determine a similarity between an order in the order pool and the basic order;

The order merging module is configured to sequentially add the orders in the order pool to the collection order in order of high to low similarity.
The apparatus according to claim 8, wherein the similarity calculation module is further configured to determine the order according to a storage location of the goods in the order, a similar storage location of the goods in the order, cargo owner information, and/or other information. The similarity between the order in the pool and the underlying order.
The apparatus according to claim 6, wherein the collective processing module further comprises:

a term management module, configured to divide an order in the current wave of the current order pool into at least two intervals according to a preset time point; and place an order in the order pool and the current scheduling wave according to an order release time And dividing into the corresponding interval; and when selecting an order that satisfies a preset similarity condition between the order pool and the basic order, preferentially selecting an order that is in the same section as the basic order.
An electronic device for optimizing a group list, comprising:

One or more processors;

a storage device for storing one or more programs,

The one or more programs are executed by the one or more processors such that the one or more processors implement the method of any of claim 1.5.
A computer readable medium having stored thereon a computer program, wherein the program is executed by a processor to implement the method of any of claims 1.5.