Goods delivery method and related products thereof

Abstract

The invention provides a goods ex-warehouse method and a related product thereof. The method comprises the following steps: predicting the time consumption duration corresponding to each wave in each operation link of a goods ex-warehouse process; determining the capacity consumption duration of each wave in each operation link based on the warehouse capacity information; on the basis of the time consumption duration corresponding to each wave in each operation link and the capacity consumption duration of each wave in each operation link, determining the picking start time of each wave in each picking warehouse; and carrying out order picking scheduling on each wave based on the order picking starting time of each wave in each order picking warehouse. By utilizing the scheme provided by the invention, the capacity of each link of order delivery can be fully utilized, the waste of delivery capacity is avoided, the overstocked quantity of delivered goods is reduced, and the order delivery efficiency is improved.

Description

Technical Field

[0001] The present application generally relates to the field of warehousing logistics technology. More specifically, the present application relates to a method for removing goods from a warehouse and related products. Background Art

[0002] E-commerce companies have a wide variety of goods and a large number of outbound orders. In order to reduce picking costs and ensure the timeliness of goods out of the warehouse, wave picking is usually used to centrally pick goods for multiple orders. When using the wave picking method, orders need to go through a series of links such as picking, gathering, sowing, and packaging. For large cross-border e-commerce companies, goods usually need to be stored in multiple warehouses, and goods for one order often need to be picked in multiple warehouses. In order to reduce transportation costs, the goods that need to be picked in each warehouse in the same wave need to be gathered, and then sown and packaged before being shipped out of the warehouse.

[0003] However, since the picking time of each warehouse is different, and the time taken for different picking warehouses to transport to the collection warehouse is also different, if each warehouse starts picking orders for a wave of orders at the same time, the goods that have been picked will arrive at the collection warehouse one after another, and the collection warehouse needs to wait until all the goods of that wave have arrived before it can start sowing and packaging. If the waiting time of the collection warehouse is too long or unstable, it cannot be guaranteed that there are enough goods for sowing and packaging, which can easily cause the staff in the subsequent sowing, packaging and other links to be idle, wasting production capacity and occupying a large amount of the collection warehouse area.

[0004] In order to solve the above problems, when scheduling the outbound delivery of ordered goods, after obtaining the expected collection time of the current wave, the time consumed for picking and transporting in different picking warehouses of the current wave can be predicted. With the goal of the goods of each picking warehouse in the same wave arriving at the collection warehouse at the same time, the picking start time of the current wave in different picking warehouses can be reversed, and then the picking scheduling of different waves in each picking warehouse can be prioritized according to the picking start time. However, due to the capacity limitation of each link in the warehouse, when multiple waves are operating at the same time, the operation time of each link may be deviated. Therefore, it is difficult for the existing scheduling method to achieve the collection of goods picked in different picking warehouses of the same wave at the collection warehouse at the same time. There are still problems such as unstable collection time, occupying a large area of ​​the collection warehouse, and easy to cause waste of subsequent outbound delivery capacity.

[0005] In view of this, there is an urgent need to provide a method for goods outbound, so as to make full use of the production capacity of each link of order outbound, avoid the waste of outbound capacity, reduce the backlog of outbound goods, and improve the efficiency of order outbound. Summary of the invention

[0006] In order to at least solve one or more of the technical problems mentioned above, the present application proposes a method for outbound delivery of goods and related products in multiple aspects. The method for outbound delivery of goods can fully utilize the production capacity of each link of outbound delivery of orders, avoid waste of outbound delivery capacity, reduce the backlog of outbound goods, and improve the efficiency of outbound delivery of orders.

[0007] In a first aspect, the present application provides a method for goods out of the warehouse, including: predicting the time consumption corresponding to each wave in each operation link of the goods out of the warehouse process; determining the capacity consumption time of each wave in each operation link based on the warehouse capacity information; determining the picking start time of each wave in each picking warehouse based on the time consumption corresponding to each wave in each operation link and the capacity consumption time of each wave in each operation link; and performing picking scheduling for each wave based on the picking start time of each wave in each picking warehouse.

[0008] In some embodiments, the operation links include picking link, transfer link and sowing link; wherein, predicting the time consumed by each wave in each operation link of the goods outbound process includes: predicting the picking time consumed by each wave in each picking warehouse in the picking link; predicting the transfer time consumed by each picking warehouse to the collection warehouse in the transfer link; and predicting the sowing time consumed by each wave in the sowing link.

[0009] In some embodiments, predicting the picking time of each wave in each picking warehouse in the picking process includes: obtaining the historical picking time of each wave in each picking warehouse in the first historical time; obtaining the historical picking quantity of each wave in each picking warehouse in the first historical time; and determining the picking time of each wave in each picking warehouse based on the quantity of items to be picked in each picking warehouse, the historical picking time of each wave in each picking warehouse, and the historical picking quantity of each wave in each picking warehouse.

[0010] In some embodiments, predicting the transfer time of each wave from each picking warehouse to the collection warehouse in the transfer link includes: respectively obtaining the transfer distance and transfer speed between each picking warehouse and the collection warehouse; and determining the transfer time of each wave from each picking warehouse to the collection warehouse based on the transfer distance and transfer speed between each picking warehouse and the collection warehouse.

[0011] In some embodiments, predicting the sowing time of each wave in the sowing stage includes: obtaining the historical sowing time corresponding to each wave in the second historical time; obtaining the historical sowing quantity corresponding to each wave in the second historical time; and determining the sowing time corresponding to each wave based on the quantity to be sown corresponding to each wave, the historical sowing time corresponding to each wave, and the historical sowing quantity corresponding to each wave.

[0012] In some embodiments, warehouse capacity information includes picking capacity information and sowing capacity information; wherein, determining the capacity consumption time of each wave in each operation link based on the warehouse capacity information includes: determining the picking capacity consumption time of each wave in each picking warehouse based on the number of items to be picked and the picking capacity information of each wave; and determining the sowing capacity consumption time corresponding to each wave based on the number of items to be sown and the sowing capacity information corresponding to each wave.

[0013] In some embodiments, determining the picking start time of each wave in each picking warehouse based on the corresponding time consumed by each wave in each operation link and the capacity consumption time of each wave in each operation link includes: setting constant parameters based on the corresponding time consumed by each wave in each operation link and the capacity consumption time of each wave in each operation link; setting variable parameters, the variable parameters including the picking start time of each wave in each picking warehouse; setting target parameters and constraints based on the constant parameters and variable parameters; constructing a target mathematical model based on the constant parameters, variable parameters, target parameters and constraints; and determining the picking start time of each wave in each picking warehouse through the target mathematical model.

[0014] In some embodiments, picking scheduling for each wave based on the picking start time of each wave in each picking warehouse includes: determining the priority sequence of each wave in each picking warehouse based on the picking start time of each wave in each picking warehouse; and picking scheduling for each wave based on the priority sequence of each wave in each picking warehouse.

[0015] In a second aspect, the present application provides a device for goods outbound, comprising: a memory; and at least one processor, configured to: predict the time corresponding to each wave in each operation link of the goods outbound process; determine the capacity consumption time of each wave in each operation link based on warehouse capacity information; determine the picking start time of each wave in each picking warehouse based on the time corresponding to each wave in each operation link and the capacity consumption time of each wave in each operation link; and perform picking scheduling for each wave based on the picking start time of each wave in each picking warehouse.

[0016] In a third aspect, the present application provides a non-volatile machine-readable medium having program code stored thereon for goods outbound. When the program code is executed by at least one processor, the code guides the execution operation of the at least one processor, and the program code includes: predicting the corresponding time duration of each wave in each operation link of the goods outbound process; determining the capacity consumption time of each wave in each operation link based on the warehouse capacity information; determining the picking start time of each wave in each picking warehouse based on the corresponding time duration of each wave in each operation link and the capacity consumption time of each wave in each operation link; and performing picking scheduling for each wave based on the picking start time of each wave in each picking warehouse.

[0017] The technical solution provided by this application may have the following beneficial effects:

[0018] The goods outbound method and related products provided in the present application predict the corresponding time consumption of each wave in each operation link of the goods outbound process, and determine the capacity consumption time of each wave in each operation link based on the warehouse capacity information, so as to obtain the predicted time consumption information of each operation link in the goods outbound process and the actual capacity consumption time information of each operation link.

[0019] Furthermore, the embodiment of the present application can determine the picking start time of each wave in each picking warehouse based on the corresponding time consumption of each wave in each operation link and the capacity consumption time of each wave in each operation link, and then perform picking scheduling for each wave based on the picking start time of each wave in each picking warehouse. Thus, the predicted time consumption information of each operation link in the goods outbound process and the actual capacity consumption information of each operation link can be considered at the same time to infer the picking start time of each wave in each picking warehouse, thereby improving the accuracy of the inference of the picking start time, and being able to maximize the guarantee that the goods picked in different picking warehouses of the same wave are collected at the same time in the collection warehouse, thereby improving the stability of the collection time, avoiding long-term and large-scale occupation of the site area of ​​the collection warehouse, and solving the problem of subsequent outbound capacity waste.

[0020] In general, this application can make full use of the production capacity of each link of order outbound delivery, avoid the waste of outbound delivery capacity, reduce the backlog of outbound goods, and improve the efficiency of order outbound delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easy to understand. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0022] Figure 1 An exemplary flow chart showing a method for shipping goods out of a warehouse in some embodiments of the present application is shown;

[0023] Figure 2 An exemplary flow chart showing a method for removing goods from a warehouse in accordance with other embodiments of the present application is shown;

[0024] Figure 3 An exemplary flow chart showing a method for removing goods from a warehouse in some other embodiments of the present application is shown;

[0025] Figure 4 A schematic diagram of the structure of a device for goods shipment shown in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. For the simplicity and clarity of the description, the figure marks may be repeated in the drawings to indicate corresponding or similar elements when deemed appropriate. In addition, the present application sets forth many specific details in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein may be practiced without these specific details. In other cases, well-known methods, processes, and components are not described in detail to avoid blurring the embodiments described herein. Moreover, the description should not be regarded as limiting the scope of the embodiments described herein. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0027] It should be understood that the possible terms "first" or "second" etc. in the claims, specifications and drawings disclosed in this application are used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of this application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0028] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this application specification and claims, unless the context clearly indicates otherwise, the singular forms of "a", "an" and "the" are intended to include plural forms. It should also be further understood that the term "and / or" used in this application specification and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0029] As used in this specification and claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0030] For large cross-border e-commerce companies, in order to reduce transportation costs, it is necessary to collect the goods that need to be picked up in each warehouse in the same wave, and then send them out after sowing and packaging. However, since the picking time of each warehouse is different, the time taken for different picking warehouses to transport to the collection warehouse is also different, causing the collection warehouse to wait for the arrival of all the goods of the wave before starting sowing and packaging. If there is no guarantee that there are enough goods to be sown and packaged, it is easy to cause the staff in the subsequent sowing, packaging and other links to be idle, wasting production capacity and occupying a large amount of space in the collection warehouse. The existing solution is that when scheduling the outbound delivery of order goods, after obtaining the expected collection time of the current wave, the time taken for picking and transportation of the current wave in different picking warehouses can be predicted, and the picking start time of the current wave in different picking warehouses can be reversed to obtain, and then the picking scheduling of different waves in each picking warehouse can be prioritized according to the picking start time. However, due to the production capacity limitations of each link in the warehouse, when multiple waves operate at the same time, the operation time of each link may be deviated, which still cannot solve the above problem well.

[0031] In view of this, there is an urgent need to provide a method for goods outbound, so as to make full use of the production capacity of each link of order outbound, avoid the waste of outbound capacity, reduce the backlog of outbound goods, and improve the efficiency of order outbound.

[0032] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0033] Figure 1 An exemplary flowchart 100 showing a method for removing goods from a warehouse in some embodiments of the present application is shown in FIG. Figure 1 The method for removing goods from the warehouse shown in the embodiment of the present application may include:

[0034] In step S101, the time consumption of each wave in each operation link of the goods outbound process is predicted. The aforementioned wave is a concept in logistics and warehousing management, which mainly refers to merging multiple orders into a batch according to certain standards for centralized processing to improve the efficiency of picking and shipping. In addition, each operation link of the goods outbound process may refer to a series of links such as picking, gathering, sowing, and packaging that the goods need to go through before they can be shipped out. It can be understood that a certain amount of time will be consumed in each link to complete the corresponding operation, and the consumed time can be predicted, for example, based on historical data, so that the corresponding time consumption in each operation link can be estimated.

[0035] In step S102, the capacity consumption time of each wave in each operation link is determined based on the warehouse capacity information. In an embodiment of the present application, the aforementioned warehouse capacity information may refer to the capacity efficiency information of each warehouse, such as the hourly picking capacity of each picking warehouse and the hourly sowing capacity of the collection warehouse, etc., which can be obtained by counting previous capacity data, for example, by the number of picking pieces in a certain period of time and the number of hours in that period of time, so as to obtain the hourly picking capacity of the picking warehouse. The aforementioned capacity consumption time may refer to the capacity time occupied by each wave in each operation link under the current capacity efficiency, which can be used to judge the capacity load of each operation link.

[0036] In step S103, the picking start time of each wave in each picking warehouse is determined based on the time consumption corresponding to each wave in each operation link and the capacity consumption time of each wave in each operation link. In the embodiment of the present application, a mathematical model can be constructed based on the time consumption corresponding to each wave in each operation link and the capacity consumption time of each wave in each operation link to solve the picking start time of each wave in each picking warehouse, so that the predicted time consumption information of each operation link of the goods outbound process and the actual capacity consumption time consumption information of each operation link can be considered at the same time, avoiding the problem of inaccurate estimation of the picking start time due to ignoring the warehouse capacity limitation factors in each operation link, and improving the estimation accuracy of the picking start time of each wave in each picking warehouse.

[0037] In step S104, picking is scheduled for each wave based on the picking start time of each wave in each picking warehouse. In the embodiment of the present application, the picking schedule of each wave can be arranged by sorting the picking start time of each wave in each picking warehouse, so as to ensure that the goods picked in different picking warehouses of the same wave are collected at the collection warehouse at the same time.

[0038] This application predicts the time consumption corresponding to each wave in each operation link of the goods outbound process, and determines the capacity consumption time of each wave in each operation link based on the warehouse capacity information, so as to obtain the predicted time consumption information of each operation link in the goods outbound process and the actual capacity consumption time information of each operation link. Furthermore, the embodiment of the present application can determine the picking start time of each wave in each picking warehouse based on the time consumption corresponding to each wave in each operation link and the capacity consumption time of each wave in each operation link, and then perform picking scheduling for each wave based on the picking start time of each wave in each picking warehouse. In this way, the predicted time consumption information of each operation link in the goods outbound process and the actual capacity time consumption information of each operation link can be taken into account at the same time to estimate the picking start time of each wave in each picking warehouse, thereby improving the estimation accuracy of the picking start time, and being able to ensure to the greatest extent that the goods picked in different picking warehouses of the same wave are gathered in the gathering warehouse at the same time, thereby improving the stability of the gathering time, avoiding long-term and large-scale occupation of the site area of ​​the gathering warehouse, and solving the problem of waste of subsequent outbound capacity.

[0039] In general, this application can make full use of the production capacity of each link of order outbound delivery, avoid the waste of outbound delivery capacity, reduce the backlog of outbound goods, and improve the efficiency of order outbound delivery.

[0040] In some embodiments, the operation links may include but are not limited to the picking link, the transport link and the sowing link, and the time consumption of the aforementioned links may be determined respectively. In addition, the warehouse capacity information may include but is not limited to the picking capacity information and the sowing capacity information, and the aforementioned capacity information may be used to determine the capacity consumption time of the picking link and the sowing link. Figure 2 The following will explain in detail the process of determining the time consumption corresponding to the above links and the process of determining the production capacity consumption time of the picking link and the sowing link. Figure 2 An exemplary flowchart 200 showing a method for removing goods from a warehouse in some other embodiments of the present application is shown in FIG. Figure 2 The method for removing goods from the warehouse shown in the embodiment of the present application may include:

[0041] In step S201, the picking time of each wave in each picking warehouse of the picking link is predicted. In an embodiment of the present application, the historical picking time of each wave in each picking warehouse in the first historical time period can be first obtained, and the historical picking quantity of each wave in each picking warehouse in the first historical time period can be obtained, and then the picking time of each wave in each picking warehouse can be determined based on the number of items to be picked in each picking warehouse, the historical picking time of each wave in each picking warehouse, and the historical picking quantity of each wave in each picking warehouse. Exemplarily, the picking time of each wave in each picking warehouse of the picking link can be predicted by the following formula 1:

[0042]

[0043] Among them, tp i,w is the picking time of the i-th wave in the w-th picking warehouse, tph i,w is the historical picking time of the i-th wave in the w-th picking warehouse in the first historical time, nph i,w is the historical picking quantity of the i-th wave in the w-th picking warehouse in the first historical period, np i,w is the number of items to be picked in the wth picking warehouse of the i-th wave. In the embodiment of the present application, the first historical duration can be set as 24 hours for example. It is understandable that in actual applications, the prediction method of the picking time in each picking warehouse of each wave in the picking link can be determined according to the actual application situation, and the setting value of the first historical duration can be determined according to the actual application situation, and the present application does not impose any restrictions in this regard.

[0044] In step S202, the transit time from each picking warehouse to the collection warehouse in the transit link is predicted. In the embodiment of the present application, the transit distance and transit speed between each picking warehouse and the collection warehouse can be obtained respectively, wherein the transit distance between each picking warehouse and the collection warehouse can be obtained by measuring the distance of the transportation route between each picking warehouse and the collection warehouse. In addition, the transit speed between each picking warehouse and the collection warehouse can be obtained by first calculating the average transit time by counting the transit time between each picking warehouse and the collection warehouse, and then dividing the measured distance by the average transit time to obtain the transit speed of each picking warehouse to the collection warehouse.

[0045] Furthermore, the transit time of each wave from each picking warehouse to the collection warehouse can be determined based on the transit distance and transit speed between each picking warehouse and the collection warehouse. For example, the transit time of each wave from each picking warehouse to the collection warehouse can be predicted by the following formula 2:

[0046]

[0047] Among them, tt i,w is the time taken to transfer from the wth picking warehouse to the collection warehouse in the i-th wave, l w is the transfer distance between the wth picking warehouse and the collection warehouse, v w is the transfer speed between the w-th picking warehouse and the collection warehouse.

[0048] It is understandable that in actual applications, a method for predicting the time required for each wave of transportation from each picking warehouse to the collection warehouse can be determined based on actual application conditions, and the present application does not impose any restrictions in this regard.

[0049] In step S203, the sowing time of each wave in the sowing link is predicted. In the embodiment of the present application, the sowing link can be carried out in the collection warehouse, and the historical sowing time corresponding to each wave in the second historical time can be obtained first, and the historical sowing quantity corresponding to each wave in the second historical time can be obtained, and then the sowing time corresponding to each wave is determined based on the number of seeds to be sown corresponding to each wave, the historical sowing time corresponding to each wave, and the historical sowing quantity corresponding to each wave. Exemplarily, the sowing time corresponding to each wave can be predicted by the following formula three:

[0050]

[0051] Among them, ts i is the seeding time corresponding to the i-th wave, tsh i is the historical seeding duration corresponding to the i-th wave in the second historical duration, nsh i is the number of historical seedings corresponding to the i-th wave in the second historical period, ns i is the number of seeds to be sown corresponding to the i-th wave. In the embodiment of the present application, the second historical duration can be exemplarily set to 24 hours. It is understandable that in practical applications, the prediction method of the sowing time duration corresponding to each wave can be determined according to the actual application situation, and the setting value of the second historical duration can be determined according to the actual application situation, and the present application does not impose any restrictions in this regard.

[0052] It is understandable that the above steps S201 to S203 can be executed sequentially or simultaneously without strict timing restrictions. It depends on the actual application situation. This application does not make any restrictions in this regard.

[0053] In step S204, the picking capacity consumption time of each wave in each picking warehouse is determined based on the number of items to be picked in each picking warehouse and the picking capacity information of each wave. For example, the picking capacity consumption time of each wave in each picking warehouse can be determined by the following formula 4:

[0054]

[0055] Among them, tcp i,w is the picking capacity consumption time of the i-th wave in the w-th picking warehouse, np i,w is the number of items to be picked in the wth picking warehouse in the i-th wave, cp i,w is the picking capacity information in the w-th picking warehouse, for example, the hourly picking capacity of the w-th picking warehouse.

[0056] In step S205, the sowing capacity consumption time corresponding to each wave is determined based on the number of seeds to be sown and the sowing capacity information corresponding to each wave. For example, the sowing capacity consumption time corresponding to each wave can be determined by the following formula 5:

[0057]

[0058] Among them, tcs i is the seeding capacity consumption time corresponding to the i-th wave, ns i is the number of seeds to be sown corresponding to the i-th wave, and cs is the sowing capacity information, for example, the hourly sowing capacity of the collection warehouse.

[0059] It is understandable that the above steps S204 to S205 can be executed sequentially or simultaneously without strict timing restrictions. It depends on the actual application situation. This application does not make any restrictions in this regard.

[0060] In some embodiments, the picking start time of each wave in each picking warehouse can be determined by constructing a target mathematical model, so that each wave can be picked and scheduled based on the picking start time of each wave in each picking warehouse. Figure 3 Let's explain in detail the process of determining the picking start time. Figure 3 An exemplary flowchart 300 of a method for removing goods from a warehouse in some other embodiments of the present application is shown. Figure 3 The method for removing goods from the warehouse shown in the embodiment of the present application may include:

[0061] In step S301, constant parameters are set based on the time taken by each wave in each operation link and the capacity consumption time of each wave in each operation link. Specifically, the time taken by the i-th wave in the w-th picking warehouse described above, tp i,w 、The picking capacity consumption time of the i-th wave in the w-th picking warehouse is tcp i,w , the time tt for the i-th wave to be transferred from the w-th picking warehouse to the collection warehouse i,w , the sowing time corresponding to the i-th wave is ts i , the sowing capacity consumption time corresponding to the i-th wave tcs i And a sufficiently large positive constant M is set as the constant parameter.

[0062] In step S302, variable parameters are set. The variable parameters include the picking start time of each wave in each picking warehouse. Specifically, the picking start time xp of the i-th wave in the w-th picking warehouse can be i,w , the sowing start time xs corresponding to the i-th wave i , the latest time of seeding completion of all waves C max , the judgment result Zp of whether the i-th wave is picked before the j-th wave in the w-th picking warehouse i,j,w (If so, Zp i,j,w The value of is 1, otherwise, Zp i,j,w The value is 0), the judgment result Zs of whether the i-th wave is sown before the j-th wave i,j (If yes, Zs i,j The value of is 1, otherwise, Zs i,j The value is 0) and the time d that the i-th wave starts sowing later than the i+1-th wave i Set as variable parameter.

[0063] In step S303, target parameters and constraints are set based on constant parameters and variable parameters. In the embodiment of the present application, three target parameters can be set, namely:

[0064] ①MinimizeC max (Minimize the latest time for all waves to complete seeding);

[0065] ②Minimize∑xs i +ts i -xp i,w (Minimize the time from the start of picking to the completion of sowing for each wave);

[0066] ③Minimize∑d i(Minimize the sum of the sowing intervals between each wave).

[0067] In addition, in the embodiment of the present application, five constraints can be set, namely:

[0068] ①xp i,w +tt i,w +tp i,w ≤xs i (The sowing starts after picking is completed and the transfer arrives)

[0069]

[0070] ④C max ≥xs i +ts i (The latest time to complete sowing is greater than or equal to the time to complete sowing in each wave)

[0071] ⑤xs i -d i ≤xs i+1 (The seeding time of the first created wave i is less than or equal to the seeding time of the later created wave i+1)

[0072] In step S304, a target mathematical model is constructed based on constant parameters, variable parameters, target parameters and constraints. In the embodiment of the present application, the target mathematical model can be constructed by combining the above constant parameters, variable parameters, target parameters and constraints.

[0073] In step S305, the picking start time of each wave in each picking warehouse is determined by the target mathematical model. In the embodiment of the present application, the solution of the target mathematical model is a mixed integer programming (MIP) problem, which can be solved by a mathematical programming solver or by a heuristic or meta-heuristic algorithm to determine the most appropriate picking start time of each wave in each picking warehouse.

[0074] In step S306, picking is scheduled for each wave based on the picking start time of each wave in each picking warehouse. In an embodiment of the present application, the priority sequence of each wave in each picking warehouse can be determined based on the picking start time of each wave in each picking warehouse, and then picking is scheduled for each wave based on the priority sequence of each wave in each picking warehouse. This can ensure to the greatest extent that the goods picked in different picking warehouses of the same wave are collected at the collection warehouse at the same time, improve the stability of the collection time, avoid occupying the site area of ​​the collection warehouse for a long time and over a large area, and solve the problem of waste of subsequent outbound production capacity.

[0075] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides a device for goods shipment and corresponding embodiments.

[0076] Figure 4 FIG. 4 is a schematic diagram showing a structure of a device 400 for goods out of a warehouse according to an embodiment of the present application. Figure 4 As shown, the device 400 for goods out of the warehouse may include a processor 410 and a memory 420. Figure 4 In the device 400 for goods out of the warehouse, only the components related to this embodiment are shown. Therefore, it is obvious to those skilled in the art that the device 400 for goods out of the warehouse may also include components related to Figure 4 The components shown in the figure are different from the common components. For example: fixed-point arithmetic units.

[0077] The device 400 for goods out of the warehouse may correspond to a computing device having various processing functions, for example, a function for generating a neural network, training or learning a neural network, quantizing a floating-point neural network to a fixed-point neural network, or retraining a neural network. For example, the device 400 for goods out of the warehouse may be implemented as various types of devices, such as a personal computer (PC), a server device, a mobile device, etc.

[0078] The processor 410 controls all functions of the device 400 for goods out of the warehouse. For example, the processor 410 controls all functions of the device 400 for goods out of the warehouse by executing a program stored in the memory 420 on the device 400 for goods out of the warehouse. The processor 410 can be implemented by a central processing unit (CPU), a graphics processing unit (GPU), an application processor (AP), an artificial intelligence processor chip (IPU), etc. provided in the device 400 for goods out of the warehouse. However, the present application is not limited thereto.

[0079] In some embodiments, the processor 410 may include an input / output (I / O) unit 411 and a computing unit 412. The I / O unit 411 may be used to receive various data. Exemplarily, the computing unit 412 may be used to predict the time consumption corresponding to each wave in each operation link of the goods outbound process; determine the capacity consumption time of each wave in each operation link based on the warehouse capacity information; determine the picking start time of each wave in each picking warehouse based on the time consumption corresponding to each wave in each operation link and the capacity consumption time of each wave in each operation link; and perform picking scheduling for each wave based on the picking start time of each wave in each picking warehouse. The picking start time of each wave in each picking warehouse may be output by the I / O unit 411, for example. The output data may be provided to the memory 420 for reading and use by other devices (not shown), or may be directly provided to other devices for use.

[0080] The memory 420 is hardware for storing various data processed in the device 400 for goods out of the warehouse. For example, the memory 420 can store processed data and data to be processed in the device 400 for goods out of the warehouse. The memory 420 can store data sets involved in the goods out of the warehouse method process that has been processed or is to be processed by the processor 410, for example, the corresponding time duration of each wave in each operation link of the goods out of the warehouse process, etc. In addition, the memory 420 can store applications, drivers, etc. to be driven by the device 400 for goods out of the warehouse. For example: the memory 420 can store various programs related to the goods out of the warehouse method to be executed by the processor 410. The memory 420 can be a DRAM, but the present application is not limited to this. The memory 420 may include at least one of a volatile memory or a non-volatile memory. The non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a phase change RAM (PRAM), a magnetic RAM (MRAM), a resistive RAM (RRAM), a ferroelectric RAM (FRAM), etc. The volatile memory may include a dynamic RAM (DRAM), a static RAM (SRAM), a synchronous DRAM (SDRAM), a PRAM, an MRAM, an RRAM, a ferroelectric RAM (FeRAM), etc. In an embodiment, the memory 420 may include at least one of a hard disk drive (HDD), a solid state drive (SSD), a high-density flash memory (CF), a secure digital (SD) card, a micro secure digital (Micro-SD) card, a mini secure digital (Mini-SD) card, an extreme digital (xD) card, caches, or a memory stick.

[0081] In summary, the specific functions implemented by the memory 420 and the processor 410 of the device 400 for goods outbound provided in the implementation manner of this specification can be explained in comparison with the aforementioned implementation manner in this specification, and can achieve the technical effects of the aforementioned implementation manner, and will not be repeated here.

[0082] In this embodiment, the processor 410 can be implemented in any suitable manner. For example, the processor 410 can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc.

[0083] It should also be understood that any module, unit, component, server, computer, terminal or device that executes instructions exemplified herein may include or otherwise access computer-readable media, such as storage media, computer storage media or data storage devices (removable and / or non-removable) such as disks, optical disks or tapes. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules or other data.

[0084] The foregoing content can be better understood in accordance with the following terms:

[0085] Clause A1, a method for goods outbound, characterized in that it includes: predicting the time consumption corresponding to each wave in each operation link of the goods outbound process; determining the capacity consumption time of each wave in each operation link based on the warehouse capacity information; determining the picking start time of each wave in each picking warehouse based on the time consumption corresponding to each wave in each operation link and the capacity consumption time of each wave in each operation link; and picking scheduling for each wave based on the picking start time of each wave in each picking warehouse.

[0086] Clause A2. The method according to Clause A1, wherein the operation links include picking link, transfer link and sowing link; wherein the predicted time consumption of each wave in each operation link of the goods outbound process includes: predicting the picking time consumption of each wave in each picking warehouse in the picking link; predicting the transfer time consumption of each wave from each picking warehouse to the collection warehouse in the transfer link; and predicting the sowing time consumption of each wave in the sowing link.

[0087] Clause A3. A method according to clause A2, wherein the prediction of the picking time for each wave in each picking warehouse in the picking link includes: obtaining the historical picking time for each wave in each picking warehouse in the first historical time period; obtaining the historical picking quantity for each wave in each picking warehouse in the first historical time period; and determining the picking time for each wave in each picking warehouse based on the quantity of items to be picked in each picking warehouse, the historical picking time for each wave in each picking warehouse, and the historical picking quantity for each wave in each picking warehouse.

[0088] Clause A4. The method according to clause A2, wherein the prediction of the transfer time for each wave from each picking warehouse to the collecting warehouse in the transfer link includes: respectively obtaining the transfer distance and transfer speed between each picking warehouse and the collecting warehouse; and determining the transfer time for each wave from each picking warehouse to the collecting warehouse based on the transfer distance and transfer speed between each picking warehouse and the collecting warehouse.

[0089] Clause A5. The method according to clause A2, wherein the prediction of the sowing time required for each wave in the sowing stage includes: obtaining the historical sowing time corresponding to each wave in the second historical time; obtaining the historical sowing quantity corresponding to each wave in the second historical time; and determining the sowing time required for each wave based on the quantity to be sown corresponding to each wave, the historical sowing time corresponding to each wave and the historical sowing quantity corresponding to each wave.

[0090] Clause A6. The method described in Clause A1, wherein the warehouse capacity information includes picking capacity information and sowing capacity information; wherein the method of determining the capacity consumption time of each wave in each operation link based on the warehouse capacity information includes: determining the picking capacity consumption time of each wave in each picking warehouse based on the number of items to be picked in each wave and the picking capacity information; and determining the sowing capacity consumption time corresponding to each wave based on the number of items to be sown corresponding to each wave and the sowing capacity information.

[0091] Clause A7. The method according to Clause A1, wherein the determination of the picking start time of each wave in each picking warehouse based on the corresponding time consumed by each wave in each operation link and the capacity consumption time of each wave in each operation link includes: setting constant parameters based on the corresponding time consumed by each wave in each operation link and the capacity consumption time of each wave in each operation link; setting variable parameters, the variable parameters including the picking start time of each wave in each picking warehouse; setting target parameters and constraints based on the constant parameters and the variable parameters; constructing a target mathematical model based on the constant parameters, the variable parameters, the target parameters and the constraints; and determining the picking start time of each wave in each picking warehouse through the target mathematical model.

[0092] Clause A8. A method according to clause A1, wherein the picking scheduling for each wave based on the picking start time of each wave in each picking warehouse includes: determining the priority sequence of each wave in each picking warehouse based on the picking start time of each wave in each picking warehouse; and picking scheduling for each wave based on the priority sequence of each wave in each picking warehouse.

[0093] Item A9, a device for goods outbound, characterized in that it includes: a memory; and at least one processor, which is configured to: predict the corresponding time consumption of each wave in each operation link of the goods outbound process; determine the capacity consumption time of each wave in each operation link based on the warehouse capacity information; determine the picking start time of each wave in each picking warehouse based on the corresponding time consumption of each wave in each operation link and the capacity consumption time of each wave in each operation link; and perform picking scheduling for each wave based on the picking start time of each wave in each picking warehouse.

[0094] Item A9, a non-volatile machine-readable medium having program code stored thereon for goods outbound. When the program code is executed by at least one processor, the code guides the execution operation of the at least one processor. The program code includes: predicting the corresponding time duration of each wave in each operation link of the goods outbound process; determining the capacity consumption time of each wave in each operation link based on the warehouse capacity information; determining the picking start time of each wave in each picking warehouse based on the corresponding time duration of each wave in each operation link and the capacity consumption time of each wave in each operation link; and performing picking scheduling for each wave based on the picking start time of each wave in each picking warehouse.

[0095] Although multiple embodiments of the present application have been shown and described herein, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art can think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein can be adopted. The attached claims are intended to limit the scope of protection of the present application, and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for removing goods from warehouse, characterized in that: include: Predict the time required for each wave of goods in each operation link of the goods outbound process; Determine the capacity consumption time of each wave in each operation link based on warehouse capacity information; Determine the picking start time of each wave in each picking warehouse based on the corresponding time consumption of each wave in each operation link and the capacity consumption time of each wave in each operation link; as well as Picking is scheduled for each wave based on the picking start time of each wave in each picking warehouse.

2. The method for removing goods from warehouse according to claim 1, characterized in that: The operation links include picking link, transfer link and sowing link; wherein, the predicted time consumption of each wave in each operation link of the goods outbound process includes: Predicting the picking time of each wave in each picking warehouse in the picking process; Predicting the time taken for each wave of goods to be transferred from each picking warehouse to the collection warehouse in the transfer link; and Predict the time required for sowing each wave in the sowing stage.

3. The method for removing goods from warehouse according to claim 2, characterized in that: The predicted picking time of each wave in each picking warehouse of the picking process includes: Obtain the historical picking duration of each wave in each picking warehouse during the first historical duration; Obtaining the historical picking quantity of each wave in each picking warehouse during the first historical period; and The picking time of each wave in each picking warehouse is determined based on the number of items to be picked in each picking warehouse, the historical picking time of each wave in each picking warehouse and the historical picking number of each wave in each picking warehouse.

4. The method for removing goods from warehouse according to claim 2, characterized in that: The predicted time taken for each wave of transportation from each picking warehouse to the collection warehouse in the transportation link includes: respectively obtaining the transfer distance and transfer speed between each picking warehouse and the collection warehouse; and The transfer time of each wave from each picking warehouse to the collecting warehouse is determined based on the transfer distance and transfer speed between each picking warehouse and the collecting warehouse.

5. The method for removing goods from warehouse according to claim 2, characterized in that: The predicted sowing time of each wave in the sowing stage includes: Obtain the historical seeding duration corresponding to each wave in the second historical duration; Obtaining the historical seeding quantity corresponding to each wave in the second historical time period; and The sowing time corresponding to each wave is determined based on the number of seeds to be sown corresponding to each wave, the historical sowing time corresponding to each wave, and the historical sowing number corresponding to each wave.

6. The method for removing goods from warehouse according to claim 1, characterized in that: The warehouse capacity information includes picking capacity information and sowing capacity information; wherein, determining the capacity consumption time of each wave in each operation link based on the warehouse capacity information includes: Determine the picking capacity consumption time of each wave in each picking warehouse based on the number of items to be picked in each wave in each picking warehouse and the picking capacity information; and The sowing capacity consumption time corresponding to each wave is determined based on the number of seeds to be sown corresponding to each wave and the sowing capacity information.

7. The method for removing goods from warehouse according to claim 1, characterized in that: The method of determining the picking start time of each wave in each picking warehouse based on the corresponding time duration of each wave in each operation link and the capacity consumption time of each wave in each operation link includes: Set constant parameters based on the time consumed by each wave in each operation link and the capacity consumption time consumed by each wave in each operation link; Setting variable parameters, wherein the variable parameters include the picking start time of each wave in each picking warehouse; Setting target parameters and constraint conditions based on the constant parameters and the variable parameters; Constructing a target mathematical model based on the constant parameters, the variable parameters, the target parameters and the constraint conditions; The target mathematical model is used to determine the picking start time of each wave in each picking warehouse.

8. The method for removing goods from warehouse according to claim 1, characterized in that: The picking scheduling for each wave based on the picking start time of each wave in each picking warehouse includes: Determining a priority sequence of each wave in each picking warehouse based on the picking start time of each wave in each picking warehouse; and Picking scheduling is performed for each wave based on the priority sequence of each wave in each picking warehouse.

9. A device for goods out of warehouse, characterized in that: include: Memory; as well as at least one processor configured to: Predict the time required for each wave of goods in each operation link of the goods outbound process; Determine the capacity consumption time of each wave in each operation link based on warehouse capacity information; Determine the picking start time of each wave in each picking warehouse based on the corresponding time consumption of each wave in each operation link and the capacity consumption time of each wave in each operation link; as well as Picking is scheduled for each wave based on the picking start time of each wave in each picking warehouse.

10. A non-transitory machine-readable medium having stored thereon a program code for goods out of a warehouse, wherein when the program code is executed by at least one processor, the code guides the execution operation of the at least one processor, and the program code comprises: Predict the time required for each wave of goods in each operation link of the goods outbound process; Determine the capacity consumption time of each wave in each operation link based on warehouse capacity information; Determine the picking start time of each wave in each picking warehouse based on the corresponding time consumption of each wave in each operation link and the capacity consumption time of each wave in each operation link; as well as Picking is scheduled for each wave based on the picking start time of each wave in each picking warehouse.

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