CN110871978B - Resource allocation method and device for stereoscopic warehouse - Google Patents
Resource allocation method and device for stereoscopic warehouse Download PDFInfo
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- CN110871978B CN110871978B CN201811020512.9A CN201811020512A CN110871978B CN 110871978 B CN110871978 B CN 110871978B CN 201811020512 A CN201811020512 A CN 201811020512A CN 110871978 B CN110871978 B CN 110871978B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
- B65G1/0492—Storage devices mechanical with cars adapted to travel in storage aisles
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The embodiment of the application discloses a resource allocation method and device for a stereoscopic warehouse. One embodiment of the method comprises: detecting the picking efficiency of a workstation in a stereoscopic warehouse; determining a corresponding relation between the number of the workstations and the utilization rate of the transportation equipment based on the detected picking efficiency of the workstations and a predetermined resource configuration model, wherein the resource configuration model is used for representing the corresponding relation between the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment; determining the number of workstations required when the utilization rate of the transportation equipment is greater than or equal to a utilization rate threshold value based on the corresponding relation between the number of workstations and the utilization rate of the transportation equipment; the determined number of workstations is started from at least two workstations. The utilization rate of the transportation equipment in the stereoscopic warehouse of the embodiment.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a resource allocation method and device for a stereoscopic warehouse.
Background
The automatic stereoscopic warehouse can automatically store and take the containers through automatic equipment to improve the utilization rate of the high layer of the warehouse. Such high storage density stereoscopic warehouses may typically include vertically operating elevators, horizontally operating conveyor rails and shuttle cars, and workstations for picking out warehouse items.
The number of the workstations is a key factor influencing the operation efficiency of the stereoscopic warehouse. Therefore, the warehouse field operators need to allocate the number of online workstations in real time to increase the load rate of the elevator and the shuttle car.
In practice, the workstation resources are typically allocated empirically. For example, the demand for opening several workstations is derived from the number of orders received, the number of picks per hour.
Disclosure of Invention
The embodiment of the application provides a resource allocation method and device for a stereoscopic warehouse.
In a first aspect, an embodiment of the present application provides a resource configuration method for a stereoscopic warehouse, the stereoscopic warehouse including a transportation device configured to transport an item and at least two workstations configured to pick the item, the method including: detecting the picking efficiency of a workstation in a stereoscopic warehouse; determining a corresponding relation between the number of the workstations and the utilization rate of the transportation equipment based on the detected picking efficiency of the workstations and a predetermined resource configuration model, wherein the resource configuration model is used for representing the corresponding relation between the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment; determining the number of the workstations required when the utilization rate of the transportation equipment is greater than or equal to a utilization rate threshold value based on the corresponding relation between the number of the workstations and the utilization rate of the transportation equipment; the determined number of workstations is started from at least two workstations.
In some embodiments, the resource configuration model is determined by: the method comprises the steps that article picking data of a stereoscopic warehouse in a preset time period are obtained, wherein the preset time period comprises a plurality of sub time periods, and the article picking data comprise the number of articles picked by a workstation of at least two workstations in the sub time periods and the running time of a transportation device in the sub time periods; generating a data sample set based on item picking data, wherein data samples in the data sample set comprise the number of workstations, picking efficiencies of the workstations, and utilization rates of transportation equipment corresponding to the number of workstations and the picking efficiencies of the workstations; and determining parameters of a preset production function based on the data sample set, and determining the production function after the parameters are determined as a resource configuration model, wherein the production function is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment.
In some embodiments, generating a set of data samples based on item picking data comprises: for a sub-time period in the plurality of sub-time periods, extracting the number of work stations started in the sub-time period, the number of picked items and the running time of the transportation equipment from the item picking data; determining a picking efficiency for the workstation during the sub-period of time based on the number of workstations activated and the number of items picked; determining the utilization rate of the transportation equipment based on the operation time length of the transportation equipment; generating data samples based on the number of the started workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment; based on the generated data samples, a set of data samples is generated.
In some embodiments, the picking efficiency of the workstation comprises one of: a mean, a quarter-decile, a median, a three-quarter median of the number of items picked during the sub-period.
In some embodiments, the preset production function comprises a kombus production function or a fixed surrogate elasticity generating function.
In some embodiments, determining parameters of a preset production function based on the data sample set, and determining the production function after determining the parameters as a resource allocation model includes: performing linear transformation on the preset production function to obtain a linear regression model of the preset production function; based on the data sample set, performing parameter estimation on the linear regression model by using a least square method to determine parameters of a preset production function; and determining the production function with the determined parameters as a resource allocation model.
In some embodiments, the transport equipment includes a lift that transports the item vertically and/or a shuttle that transports the item horizontally.
In a second aspect, an embodiment of the present application provides a resource configuration apparatus for a stereoscopic warehouse, the stereoscopic warehouse including a transportation device configured to transport items and at least two workstations configured to pick the items, the apparatus including: an efficiency detection unit configured to detect a picking efficiency of a workstation in a stereoscopic warehouse; a relation determining unit configured to determine a corresponding relation between the number of workstations and a utilization rate of the transportation device based on the detected picking efficiency of the workstations and a predetermined resource configuration model, wherein the resource configuration model is used for representing the corresponding relation between the number of workstations, the picking efficiency of the workstations and the utilization rate of the transportation device; a number determination unit configured to determine the number of workstations required when the utilization rate of the transportation device is greater than or equal to a utilization rate threshold value, based on a correspondence between the number of workstations and the utilization rate of the transportation device; an activation unit configured to activate the determined number of workstations from the at least two workstations.
In some embodiments, the resource configuration model is determined by: the method comprises the steps that article picking data of a stereoscopic warehouse in a preset time period are obtained, wherein the preset time period comprises a plurality of sub time periods, and the article picking data comprise the number of articles picked by a workstation of at least two workstations in the sub time periods and the running time of a transportation device in the sub time periods; generating a data sample set based on item picking data, wherein data samples in the data sample set comprise the number of workstations, picking efficiencies of the workstations, and utilization rates of transportation equipment corresponding to the number of workstations and the picking efficiencies of the workstations; and determining parameters of a preset production function based on the data sample set, and determining the production function after the parameters are determined as a resource configuration model, wherein the production function is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment.
In some embodiments, generating a set of data samples based on item picking data comprises: for a sub-time period in the plurality of sub-time periods, extracting the number of work stations started in the sub-time period, the number of items picked and the running time of the transportation equipment from the item picking data; determining a picking efficiency for the workstation during the sub-period of time based on the number of workstations activated and the number of items picked; determining the utilization rate of the transportation equipment based on the operation time length of the transportation equipment; generating data samples based on the number of the started workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment; based on the generated data samples, a set of data samples is generated.
In some embodiments, the picking efficiency of the workstation comprises one of: a mean, a quarter-decile, a median, a three-quarter median of the number of items picked during the sub-period.
In some embodiments, the preset production function comprises a kombus production function or a fixed surrogate elasticity generating function.
In some embodiments, determining parameters of a preset production function based on the data sample set, and determining the production function after determining the parameters as a resource allocation model includes: performing linear transformation on the preset production function to obtain a linear regression model of the preset production function; based on the data sample set, performing parameter estimation on the linear regression model by using a least square method to determine parameters of a preset production function; and determining the production function with the determined parameters as a resource configuration model.
In some embodiments, the transport apparatus includes a hoist that transports the item vertically and/or a shuttle that transports the item horizontally.
In a third aspect, an embodiment of the present application provides an electronic device, including: a controller comprising one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the controller, the controller is caused to implement the method as described in any implementation of the first aspect.
In a fourth aspect, the present application provides a computer readable medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the resource allocation method and device for the stereoscopic warehouse, the picking efficiency of the workstations in the stereoscopic warehouse is detected, the corresponding relation between the number of the workstations and the utilization rate of the transportation equipment is determined based on the picking efficiency and the predetermined resource allocation model, the number of the workstations which need to be started when the utilization rate of the transportation equipment is about or equal to the utilization rate threshold value is determined according to the corresponding relation between the number of the workstations and the utilization rate threshold value, and finally the determined number of the workstations are started, so that the utilization rate of the transportation equipment in the stereoscopic warehouse is improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
FIG. 1 is an exemplary system architecture diagram to which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a resource configuration method for a stereoscopic warehouse according to the present application;
FIG. 3 is a schematic flow diagram of determining a resource allocation model in the embodiment shown in FIG. 2;
fig. 4 is a schematic diagram of an application scenario of a resource configuration method for a stereoscopic warehouse according to the present application;
fig. 5 is a schematic structural diagram of an embodiment of a resource allocation apparatus for a stereoscopic warehouse according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which an embodiment of the resource configuration method for a stereoscopic warehouse or the resource configuration apparatus for a stereoscopic warehouse of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include an stereoscopic warehouse 101 and a controller 102.
The stereoscopic warehouse 101 may interact with the controller 102 to receive or send messages. The stereoscopic warehouse 101 may include transportation equipment such as hoists and shuttle cars, and a plurality of workstations. Wherein the elevator can run on a vertical track to transport items vertically. The shuttle car can run on a horizontal rail to horizontally transport the goods. And the workstations may pick items based on, for example, order quantity.
The controller 102 may or may not be provided inside the stereoscopic warehouse 101. The controller 102 may perform various controls on the stereoscopic warehouse 101. For example, controller 102 may detect the picking efficiency of a workstation, and controller 102 may turn the workstation on or off as well.
It should be noted that the resource allocation method for the stereoscopic warehouse provided in the embodiment of the present application is generally executed by the controller 102, and accordingly, the resource allocation apparatus for the stereoscopic warehouse is generally disposed in the controller 102.
Here, the resources may include workstations, transportation devices, and the like in the stereoscopic warehouse. Accordingly, the resource configuration may be to configure workstations, transportation devices, etc. in the stereoscopic warehouse, e.g., to turn on a workstation, turn off a workstation, etc.
The controller 102 may be hardware or software. When the controller 102 is hardware, it may be implemented as a distributed device cluster composed of a plurality of devices, or may be implemented as a single device. When the controller is software, it may be implemented as a plurality of software or software modules (for example to provide distributed services), or it may be implemented as a single software or software module. And is not particularly limited herein.
It should be understood that the number of hoists, shuttle cars, workstations, and controllers in fig. 1 are merely illustrative. There may be any suitable number of hoists, shuttle cars, workstations, and controllers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for resource allocation for a stereoscopic warehouse according to the present application is shown. The resource allocation method for the stereoscopic warehouse can comprise the following steps of:
In this embodiment, a stereoscopic warehouse (e.g., the stereoscopic warehouse 101 shown in fig. 1) may include a transport apparatus and at least two workstations. Wherein the transport apparatus is configured to transport the item and the workstation is configured to pick the item. An executive body of a resource allocation method for a stereoscopic warehouse (e.g., controller 102 shown in fig. 1) may detect the efficiency with which workstations in the stereoscopic warehouse pick items. As an example, the number of items picked by a workstation for one hour may be detected as the picking efficiency of the workstation. Here, picking efficiency may refer to the number of items that a workstation picks per unit time (e.g., an hour, a day, etc.).
In some alternative implementations of the present embodiment, the transportation device in the stereoscopic warehouse may include a hoist and/or a shuttle. Wherein the elevator may be configured to transport items vertically and the shuttle may be configured to transport items horizontally.
In this embodiment, an executing body (for example, the controller 102 shown in fig. 1) of the resource allocation method for a stereoscopic warehouse may determine the correspondence between the number of workstations and the utilization rate of the transportation device by using the picking efficiency of the workstations detected in step 201 and a predetermined resource allocation model. The resource configuration model is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment. As an example, the resource configuration model may be a correspondence table storing correspondences between the number of workstations, the picking efficiency of the workstations and the utilization of the elevator, determined based on data statistics of the number of workstations and the picking efficiency of the workstations and the utilization of the elevator.
In some optional implementations of the present embodiment, in order to measure the correspondence between the input variables (the number of workstations and the picking efficiency of the workstations) and the output variables (the utilization rate of the transportation equipment), a functional relationship between the input and the output may also be set. As an example, a person skilled in the art may use a preset production function as the initial model. The production function represents the relationship between the input amount used in production and the output amount that can be produced under the condition that the technical level is not changed in a certain period.
Next, the determination procedure of the resource allocation model according to the present embodiment will be described by taking the flowchart shown in fig. 3 as an example. Fig. 3 shows a schematic flow chart of determining a resource configuration model in the embodiment shown in fig. 2.
As shown in fig. 3, the resource allocation model may be determined by the following steps:
in step 301, item picking data of a stereoscopic warehouse within a preset time period may be obtained. Wherein the preset time period (one month, one week, one day, etc.) may include a plurality of sub-time periods (e.g., one day, one hour, etc.). The item picking data may include the number of items picked by each workstation in the stereoscopic warehouse during each sub-period and the length of time the transport equipment is operating during each sub-period.
Based on the item picking data, a set of data samples may be generated 302. Wherein each data sample in the set of data samples may include a number of workstations, a picking efficiency of the workstations, and a utilization of transport equipment corresponding to the number of workstations and the picking efficiency of the workstations.
Optionally, step 302 may specifically include:
a first step of, for each of a plurality of sub-periods:
firstly, extracting the number of workstations started in the sub-time period, the number of items to be picked and the running time of the transportation equipment from the item picking data;
then, determining the picking efficiency of the workstation in the sub-time period based on the number of the activated workstations and the number of the picked items;
then, determining the utilization rate of the transportation equipment (for example, the ratio of the operation time length to the sub-time period) based on the operation time length of the transportation equipment;
finally, data samples are generated based on the number of workstations activated, the picking efficiency of the workstations, and the utilization of the transport equipment.
And a second step of generating a data sample set based on the data samples generated in the first step.
Alternatively, the picking efficiency of the workstation may be determined by: firstly, determining the mean value, the quarter quantile, the median and the three-quarter quantile of the quantity of the articles picked by the workstation in a unit time period by utilizing the quantity of the workstations started in the sub-time period and the quantity of the picked articles; then, the average, quarter, median or three-quarters of the number of items picked by the workstation in the unit time period is determined as the picking efficiency of the workstation.
In the present application, the manner of determining the picking efficiency of the workstation is not limited to this, and other suitable manners may be adopted to determine the picking efficiency of the workstation.
In step 303, parameters of a preset production function may be determined based on the data sample set, and then the production function with the determined parameters is determined as a resource configuration model. The production function is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment.
Optionally, the preset generation Function may include a Cobb-Douglas Production Function (also referred to as a CD Production Function) or a Constant Elasticity generation Function (Constant Elasticity of subscription Production Function, also referred to as a CES generation Function).
Optionally, step 303 may specifically include: firstly, a preset production function is subjected to linear transformation to obtain a linear regression model of the preset production function. And then, based on the data sample set, performing parameter estimation on the linear regression model by using a least square method, and determining the parameters of a preset production function. And finally, determining the production function with the determined parameters as a resource configuration model.
The step 303 is specifically described below by taking the preset generation function as the CES production function as an example.
Firstly, the corresponding relation among the number of workstations sum, the picking efficiency eff of the workstations and the utilization rate y of the transport equipment is set as follows by using a CES production function:
wherein gamma is a technological progress parameter, delta 1 Optimal distribution coefficient, δ, for the number of workstations sum 2 And (3) an optimal distribution coefficient of the sorting efficiency eff of the workstation, wherein rho and v are elasticity of a production function, and u is a random disturbance term. And, 0 < δ 1 ，δ 2 ＜1，ρ＜1，v＞0，δ 1 +δ 2 ＝1。
Taking logarithm of formula (1) to obtain
Order:
f(ρ)＝ln(δ 1 ×sum -ρ +δ 2 ×eff -ρ )＝ln Q (3)
wherein, for the convenience of derivation, δ is used 1 ×sum -ρ +δ 2 ×eff -ρ Is denoted as Q.
Taylor expansion is carried out on the unitary function f (rho) of the rho at the position of rho =1 to obtain
First, since Q- ρ＝0 =1, so f (0) =0.
Secondly, because of
Where f' (ρ) is the first derivative of f (ρ). Thus, the device
f′(0)＝-δ 1 ×lnsum-δ 2 ×ln eff (7)
Thirdly, due to
Where f' (ρ) is the second derivative of f (ρ). Thus, it is possible to provide
f″(0)＝δ 1 ×δ 2 ×(ln sum-ln eff) 2 (10)
Substituting f (0), f' (0), f ″ (0) into the formula (4) to obtain
Substituting the formula (11) into the formula (2) to obtain
The above equation (12) is a linear regression model of the predetermined production function (i.e., equation (1)). Further, since u satisfies the gaussian-markov assumption, i.e., the mean is 0, the independent covariance is 0, and the uncorrelated, the parameter estimation of formula (12) can be performed by the least square method, and thus the parameters γ and δ can be obtained 1 、δ 2 Values of ρ, v.
And finally, determining the production function with the determined parameters as a resource configuration model.
Although the parameter estimation is performed on the linear regression model by the least square method in the above example, the present application is not limited thereto. It should be understood that other suitable processing approaches may be used to perform parameter estimation on the linear regression model, such as ridge regression, etc.
Although the above example describes the CES production function as the preset production function, this is merely illustrative. It will be appreciated by those skilled in the art that the resource allocation model may also be determined using, for example, a CD production function as the preset production function.
Since the resource allocation model represents the corresponding relationship between the number of workstations, the picking efficiency of the workstations, and the utilization rate of the transportation equipment, the corresponding relationship between the number of workstations and the utilization rate of the transportation equipment can be obtained by substituting the picking efficiency of the workstations detected in step 201 into the resource allocation model (for example, formula (1) after the parameters are determined).
Returning to FIG. 2, the description of flow 200 continues.
And step 203, determining the number of the workstations required when the utilization rate of the transportation equipment is greater than or equal to the utilization rate threshold value based on the corresponding relation between the number of the workstations and the utilization rate of the transportation equipment.
In this embodiment, an execution subject (for example, the controller 102 shown in fig. 1) of the resource allocation method for a stereoscopic warehouse may analyze the correspondence between the number of workstations determined in step 202 and the utilization rate of the transportation device, so that the number of workstations required when the utilization rate of the transportation device is greater than or equal to the utilization rate threshold value may be determined.
As shown in equation (1), the number of workstations and the utilization rate of the transportation device may have a non-linear relationship, that is, there may be: when the number of the workstations is less than a certain value, the utilization rate of the transportation equipment is increased along with the increase of the number of the workstations; when the number of the work stations is larger than the value, the utilization rate of the transportation equipment is reduced along with the increase of the number of the work stations (or vice versa). Therefore, the correspondence relationship (i.e., the functional expression) between the number of workstations and the utilization rate of the transportation device can be analyzed, and the range of the number of workstations in which the utilization rate of the transportation device is equal to or higher than the utilization rate threshold (e.g., 60%, 80%, etc.) can be determined.
As an example, a derivative may be made of a functional expression of the number of workstations and the utilization rate of the transport facility, and a value of the number of workstations at which the utilization rate of the transport facility is maximum may be determined. That is, the need to start several workstations maximizes the utilization of the transport equipment.
The determined number of workstations is started from at least two workstations, step 204.
In this embodiment, the execution subject (for example, the controller 102 shown in fig. 1) of the resource allocation method for the stereoscopic warehouse may select the number of workstations determined in step 203 from the workstations of the stereoscopic warehouse to start, so that the utilization rate of the transportation device of the stereoscopic warehouse is greater than or equal to the utilization rate threshold.
With continued reference to fig. 4, one application scenario 400 of a resource configuration method for a stereoscopic repository in accordance with the present application is shown. In the application scenario 400 of fig. 4, the stereoscopic warehouse includes 5 workstations, hoists (not shown), and the like. First, the controller 401 may detect the number of items that the workstation picks within an hour, i.e., the picking efficiency of the workstation is 250 items/hour. Then, the detected picking efficiency of the workstations is substituted into a predetermined resource allocation model (for example, formula (1), where y is the utilization rate of the hoist), and a functional relationship between the number of workstations and the utilization rate of the hoist is obtained. Then, the value of the number of work stations when the utilization rate of the hoisting machine reaches the maximum is determined to be 3 through derivation. Finally, the controller 401 may turn on 3 of the 5 stations, thereby ensuring that the utilization of the elevator of the stereoscopic warehouse reaches the maximum load.
According to the resource configuration method for the stereoscopic warehouse provided by the embodiment of the application, the picking efficiency of the workstations in the stereoscopic warehouse is detected, the corresponding relation between the number of the workstations and the utilization rate of the transportation equipment is determined based on the picking efficiency and the predetermined resource configuration model, the number of the workstations which need to be started when the utilization rate of the transportation equipment is about or equal to the utilization rate threshold value is determined according to the corresponding relation between the number of the workstations and the utilization rate of the transportation equipment, and finally the determined number of the workstations are started, so that the utilization rate of the transportation equipment in the stereoscopic warehouse is improved.
With further reference to fig. 5, as an implementation of the method shown in the above-mentioned figures, the present application provides an embodiment of a resource allocation apparatus for a stereoscopic warehouse, which corresponds to the embodiment of the method shown in fig. 2, and which may be specifically applied in, for example, a controller.
As shown in fig. 5, the resource configuration apparatus 500 for a stereoscopic warehouse of the present embodiment may include an efficiency detection unit 501, a relationship determination unit 502, a number determination unit 503, and an activation unit 504. Wherein the efficiency detection unit 501 is configured to detect the picking efficiency of the workstation in the stereoscopic warehouse; the relationship determining unit 502 is configured to determine a correspondence between the number of workstations and a utilization rate of the transportation device based on the detected picking efficiency of the workstations and a predetermined resource configuration model, wherein the resource configuration model is used for representing the correspondence between the number of workstations, the picking efficiency of the workstations and the utilization rate of the transportation device; the number determination unit 503 is configured to determine the number of workstations required when the utilization rate of the transportation device is greater than or equal to the utilization rate threshold value, based on the correspondence between the number of workstations and the utilization rate of the transportation device; and the activation unit 504 is configured to activate the determined number of workstations from the at least two workstations.
In this embodiment, a stereoscopic warehouse (e.g., the stereoscopic warehouse 101 shown in fig. 1) may include a transport apparatus and at least two workstations. Wherein the transport apparatus is configured to transport the item and the workstation is configured to pick the item. The above-described efficiency detection unit 501 of the resource configuration apparatus 500 for a stereoscopic warehouse may detect the efficiency with which work stations in the stereoscopic warehouse pick items. As an example, the number of items picked by a workstation for one hour may be detected as the picking efficiency of the workstation. Here, picking efficiency may refer to the number of items that a workstation picks in a unit of time (e.g., an hour, a day, etc.).
In some alternative implementations of the present embodiment, the transportation device in the stereoscopic warehouse may include a hoist and/or a shuttle. Wherein the elevator may be configured to transport items vertically and the shuttle may be configured to transport items horizontally.
In this embodiment, the relationship determination unit 502 may determine the correspondence relationship between the number of workstations and the utilization rate of the transportation device by using the picking efficiency of the workstations detected by the efficiency detection unit 501 and a predetermined resource allocation model. The resource configuration model is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment.
In some optional implementations of this embodiment, the resource allocation model may be determined by:
the method comprises the steps of firstly, acquiring article picking data of a stereoscopic warehouse in a preset time period. Wherein the preset time period (one month, one week, one day, etc.) may include a plurality of sub-time periods (e.g., one day, one hour, etc.). The item picking data may include the number of items picked by each workstation in the stereoscopic warehouse during each sub-period and the length of time the transport equipment is operating during each sub-period.
And secondly, generating a data sample set based on the item picking data. Wherein each data sample in the set of data samples may include a number of workstations, a picking efficiency of the workstations, and a utilization of transport equipment corresponding to the number of workstations and the picking efficiency of the workstations.
And thirdly, determining the parameters of a preset production function based on the data sample set, and then determining the production function with the determined parameters as a resource configuration model. The production function is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment.
In some optional implementations of this embodiment, the data sample set may be generated by: for each sub-time period in a plurality of sub-time periods, extracting the number of work stations started in the sub-time period, the number of picked items and the running time of the transportation equipment from the item picking data; determining a picking efficiency for the workstation during the sub-period of time based on the number of workstations activated and the number of items picked; determining the utilization rate of the transportation equipment based on the operation duration of the transportation equipment; data samples are generated based on the number of workstations activated, picking efficiency of the workstations, and utilization of the transport equipment. Based on the generated data samples, a set of data samples is generated.
In some alternative implementations of the present embodiment, the picking efficiency of the workstation includes one of: a mean, a quarter quartile, a median, a three-quarter median of the number of items picked during the sub-time period.
In some optional implementations of this embodiment, the preset generation Function may include a Cobb-Douglas Production Function (also referred to as a CD Production Function) or a Constant Elasticity of stimulation (also referred to as a CES generation Function).
In some optional implementations of this embodiment, the resource configuration model may be determined by: firstly, a linear transformation is performed on a preset production function to obtain a linear regression model of the preset production function. And then, based on the data sample set, performing parameter estimation on the linear regression model by using a least square method, and determining the parameters of a preset production function. And finally, determining the production function with the determined parameters as a resource configuration model.
In this embodiment, the number determination unit 503 may analyze the correspondence between the number of workstations determined by the relationship determination unit 502 and the utilization rate of the transportation device, so as to determine the number of workstations required when the utilization rate of the transportation device is greater than or equal to the utilization rate threshold.
In this embodiment, the starting unit 504 may select the number of the workstations determined in step 203 from the workstations of the stereoscopic warehouse to start, so that the utilization rate of the transportation equipment of the stereoscopic warehouse is greater than or equal to the threshold utilization rate.
The resource allocation device for the stereoscopic warehouse provided by the above embodiment of the application detects the picking efficiency of the workstations in the stereoscopic warehouse, then determines the corresponding relationship between the number of the workstations and the utilization rate of the transportation equipment based on the picking efficiency and the predetermined resource allocation model, then determines the number of the workstations to be started when the utilization rate of the transportation equipment is about or equal to the utilization rate threshold value according to the corresponding relationship between the number of the workstations and the utilization rate of the transportation equipment in the stereoscopic warehouse, and finally starts the determined number of the workstations, thereby improving the utilization rate of the transportation equipment in the stereoscopic warehouse.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use in implementing an electronic device (e.g., controller 102 of FIG. 1) according to embodiments of the present application is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a controller 601, and the controller 601 includes one or more Central Processing Units (CPUs) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The controller 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as an Organic Light Emitting Diode (OLED) display, a Liquid Crystal Display (LCD), and the like, and a speaker and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that the computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609 and/or installed from the removable medium 611. The computer program, when executed by the controller 601, performs the above-described functions defined in the method of the present application.
It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a controller includes an efficiency detection unit, a relationship determination unit, a number determination unit, and a start unit. The names of these units do not in some cases constitute a limitation on the units themselves, for example, the efficiency detection unit may also be described as a "unit that detects the picking efficiency of a workstation in a stereoscopic warehouse".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: detecting the picking efficiency of a workstation in a stereoscopic warehouse; determining a corresponding relation between the number of the workstations and the utilization rate of the transportation equipment based on the detected picking efficiency of the workstations and a predetermined resource configuration model, wherein the resource configuration model is used for representing the corresponding relation between the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment; determining the number of the workstations required when the utilization rate of the transportation equipment is greater than or equal to a utilization rate threshold value based on the corresponding relation between the number of the workstations and the utilization rate of the transportation equipment; the determined number of workstations is started from at least two workstations.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (14)
1. A resource configuration method for a stereoscopic warehouse comprising a transportation device configured to transport items and at least two workstations configured to pick items, the method comprising:
detecting the picking efficiency of a workstation in the stereoscopic warehouse;
determining a corresponding relation between the number of the workstations and the utilization rate of the transportation equipment based on the detected picking efficiency of the workstations and a predetermined resource configuration model, wherein the resource configuration model is used for representing the corresponding relation between the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment;
determining the number of workstations required when the utilization rate of the transportation equipment is greater than or equal to a utilization rate threshold value based on the corresponding relation between the number of workstations and the utilization rate of the transportation equipment;
initiating a determined number of workstations from the at least two workstations;
wherein the resource allocation model is determined by:
acquiring item picking data of the stereoscopic warehouse in a preset time period, wherein the preset time period comprises a plurality of sub-time periods, and the item picking data comprises the number of items picked by a workstation of the at least two workstations in the sub-time periods of the plurality of sub-time periods and the running time of the transportation equipment in the sub-time period of the plurality of sub-time periods;
generating a data sample set based on the item picking data, wherein data samples in the data sample set comprise the number of workstations, picking efficiencies of the workstations, and utilization rates of transportation equipment corresponding to the number of workstations and the picking efficiencies of the workstations;
and determining parameters of a preset production function based on the data sample set, and determining the production function after the parameters are determined as the resource configuration model, wherein the production function is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment.
2. The method of claim 1, wherein said generating a set of data samples based on said item picking data comprises:
for a sub-time period of the plurality of sub-time periods, extracting the number of workstations started in the sub-time period, the number of items picked and the running time of the transportation equipment from the item picking data; determining a picking efficiency for the workstation during the sub-period of time based on the number of workstations activated and the number of items picked; determining the utilization rate of the transportation equipment based on the running time of the transportation equipment; generating data samples based on the number of workstations started, the picking efficiency of the workstations and the utilization rate of the transportation equipment;
based on the generated data samples, a set of data samples is generated.
3. The method of claim 2, wherein the picking efficiency of the workstation comprises one of: a mean, a quarter-decile, a median, a three-quarter median of the number of items picked during the sub-period.
4. The method of claim 1, wherein the preset production function comprises a kombus production function or a fixed surrogate elasticity generating function.
5. The method of claim 4, wherein the determining parameters of a preset production function based on the data sample set, and the determining the parameterized production function as the resource allocation model comprises:
performing linear transformation on the preset production function to obtain a linear regression model of the preset production function;
based on the data sample set, performing parameter estimation on a linear regression model by using a least square method to determine parameters of the preset production function;
and determining the production function with the determined parameters as the resource allocation model.
6. The method of claim 1, wherein the transport equipment comprises a hoist that transports items vertically and/or a shuttle that transports items horizontally.
7. A resource configuration apparatus for a stereoscopic warehouse comprising transport equipment configured to transport items and at least two workstations configured to pick items, the apparatus comprising:
an efficiency detection unit configured to detect a picking efficiency of a workstation in the stereoscopic warehouse;
a relation determining unit configured to determine a correspondence between the number of workstations and a utilization rate of the transportation device based on the detected picking efficiency of the workstations and a predetermined resource configuration model, wherein the resource configuration model is used for representing the correspondence between the number of workstations, the picking efficiency of the workstations and the utilization rate of the transportation device;
a number determination unit configured to determine the number of workstations required when the utilization rate of the transportation device is greater than or equal to a utilization rate threshold value, based on a correspondence between the number of workstations and the utilization rate of the transportation device;
a start-up unit configured to start up the determined number of workstations from the at least two workstations;
wherein the resource allocation model is determined by:
acquiring item picking data of the stereoscopic warehouse in a preset time period, wherein the preset time period comprises a plurality of sub-time periods, and the item picking data comprises the number of items picked by a workstation of the at least two workstations in the sub-time periods of the plurality of sub-time periods and the running time of the transportation equipment in the sub-time period of the plurality of sub-time periods;
generating a data sample set based on the item picking data, wherein data samples in the data sample set comprise the number of workstations, picking efficiencies of the workstations, and utilization rates of transportation equipment corresponding to the number of workstations and the picking efficiencies of the workstations;
and determining parameters of a preset production function based on the data sample set, and determining the production function after the parameters are determined as the resource configuration model, wherein the production function is used for representing the corresponding relation among the number of the workstations, the picking efficiency of the workstations and the utilization rate of the transportation equipment.
8. The apparatus of claim 7, wherein said generating a set of data samples based on said item picking data comprises:
for a sub-time period of the plurality of sub-time periods, extracting the number of workstations started in the sub-time period, the number of items picked and the running time of the transportation equipment from the item picking data; determining a picking efficiency for the workstation during the sub-period of time based on the number of workstations activated and the number of items picked; determining the utilization rate of the transportation equipment based on the running time of the transportation equipment; generating data samples based on the number of workstations started, the picking efficiency of the workstations and the utilization rate of the transportation equipment;
based on the generated data samples, a set of data samples is generated.
9. The apparatus of claim 8, wherein the picking efficiency of the workstation comprises one of: a mean, a quarter-decile, a median, a three-quarter median of the number of items picked during the sub-period.
10. The apparatus of claim 7, wherein the preset production function comprises a cobb-dag-grulas production function or a fixed surrogate elasticity generating function.
11. The apparatus of claim 10, wherein the determining parameters of a preset production function based on the data sample set, and the determining a parameterized production function as the resource configuration model comprises:
performing linear transformation on the preset production function to obtain a linear regression model of the preset production function;
based on the data sample set, performing parameter estimation on a linear regression model by using a least square method to determine parameters of the preset production function;
and determining the production function with the determined parameters as the resource configuration model.
12. The apparatus of claim 7, wherein the transport device comprises a hoist that transports items vertically and/or a shuttle that transports items horizontally.
13. An electronic device, comprising:
a controller comprising one or more processors;
a storage device having one or more programs stored thereon,
when executed by the controller, cause the controller to implement the method of any one of claims 1-6.
14. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.
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| CN113071839A (en) * | 2021-03-31 | 2021-07-06 | 深圳市库宝软件有限公司 | Robot configuration method and device, control terminal and warehousing system |
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