CN110633880A - Method and device for determining configuration number of automatic guided vehicles - Google Patents

Method and device for determining configuration number of automatic guided vehicles Download PDF

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CN110633880A
CN110633880A CN201810653105.5A CN201810653105A CN110633880A CN 110633880 A CN110633880 A CN 110633880A CN 201810653105 A CN201810653105 A CN 201810653105A CN 110633880 A CN110633880 A CN 110633880A
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data
model
sample data
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刘小庆
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Beijing Jingdong Qianshi Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention discloses a method and a device for determining the configuration number of automatic guided vehicles, and relates to the technical field of computers. One embodiment of the method comprises: acquiring factor data according to the created model, wherein the factor data at least comprises the number of workstations and the number of pieces to be picked; determining the initial configuration quantity of the automatic guided vehicles according to the acquired factor data and the model; and adjusting the initial configuration data according to the caching digits of the workstation. According to the method, the reasonable configuration number of the AGV is calculated through the created model, the real data is utilized, the current technical efficiency problem can be considered, the resource configuration is reasonable, and the problems of order hanging and the like are avoided.

Description

Method and device for determining configuration number of automatic guided vehicles
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for determining configuration quantity of automatic guided vehicles.
Background
At present, when the number of AGVs is set in an unmanned storehouse, it is usually determined by using human historical experience. Namely, after the number of pieces to be picked is determined according to the order quantity, the configuration quantity of the AGV is artificially determined through comparison with historical data. The method is completely inferred by human experience, so that the conditions of order hanging and the like caused by unreasonable resource utilization or insufficient AGV configuration quantity exist, and the implementation is limited when workers flow.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for determining a configuration quantity of AGVs, which can calculate a reasonable configuration quantity of AGVs through a created model, and utilize real data, so as to consider the current technical efficiency problem, ensure rationalization of resource configuration, and avoid the problems of hang-up and the like.
To achieve the above objects, according to one aspect of the embodiments of the present invention, there is provided a method of determining a configuration number of an automated guided vehicle.
The method for determining the configuration number of the automatic guided vehicles in the embodiment of the invention comprises the following steps: acquiring factor data according to the created model, wherein the factor data at least comprises the number of workstations and the number of pieces to be picked; determining the initial configuration quantity of the automatic guided vehicles according to the acquired factor data and the model; and adjusting the initial configuration data according to the caching digits of the workstation.
Optionally, before obtaining the factor data according to the created model, further comprising:
designing a model according to a kobuki-douglas production function, the model being described as:
y is the number of picks, car is the number of automated guided vehicles configured, station is the number of workstations, beta0，β1And beta2Is a parameter, and 0<β1,β2<1；
According to the obtained sample data pairParameter beta0，β1And beta2And (6) estimating.
Optionally, the parameter β is set according to the acquired sample data0，β1And beta2The step of performing the estimation comprises:
standardizing the acquired sample data;
from the normalized sample data, and fitting the parameter β by ridge regression0，β1And beta2And (6) estimating.
Optionally, the parameter β is set according to the acquired sample data0，β1And beta2Before the estimation, the method further comprises the following steps:
acquiring more than one group of sample data, wherein each group of sample data comprises the historical configuration number of the automatic guided vehicle, the number of the workstations and the number of the picked items;
judging whether the historical configuration quantity, the work station quantity or the number of the pickers in the sample data of each group is zero or not; if yes, removing the sample data;
judging whether the number of the pickers in the sample data is larger than a preset threshold value or not; if yes, the sample data is removed.
Optionally, before determining the initial configuration number of the automated guided vehicle according to the acquired factor data and the model, the method further includes: obtaining model adjustment data according to a warehouse to which the automatic guided transporting vehicle belongs; adjusting the created model according to the model adjustment data;
and then, determining the initial configuration quantity of the automatic guided vehicle according to the acquired factor data and the adjusted model.
To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided an apparatus for determining the number of configurations of an automated guided vehicle.
The device for determining the configuration number of the automatic guided vehicles in the embodiment of the invention comprises the following components:
the factor data acquisition module is used for acquiring factor data according to the created model, wherein the factor data at least comprises the number of workstations and the number of pieces to be picked;
the initial configuration data determining module is used for determining the initial configuration quantity of the automatic guided vehicles according to the acquired factor data and the model;
and the configuration data adjusting module is used for adjusting the initial configuration data according to the caching digits of the workstation.
Optionally, the apparatus for determining the configuration number of the automated guided vehicle according to the embodiment of the present invention further includes a model creation module, configured to design a model according to a kobu-douglas production function, where the model is described as:
Figure BDA0001704645180000031
y is the number of picks, car is the number of automated guided vehicles configured, station is the number of workstations, beta0，β1And beta2Is a parameter, and 0<β1,β2<1；
And, according to the sample data obtained, the parameter beta0，β1And beta2And (6) estimating.
Optionally, the model creating module is further configured to normalize the acquired sample data; and, from said normalized sample data, and by ridge regression on the parameter β0，β1And beta2And (6) estimating.
Optionally, the model creating module is further configured to obtain more than one group of sample data, where each group of sample data includes the historical configuration number of the automated guided vehicle, the number of the workstations, and the number of the pickers; and judging whether the historical configuration number, the work station number or the number of the pickers in the sample data of each group is zero; if yes, removing the sample data; judging whether the number of the pickers in the sample data is larger than a preset threshold value or not; if yes, the sample data is removed.
Optionally, the apparatus for determining the configuration number of the automated guided vehicles according to the embodiment of the present invention further includes a model adjustment module, configured to obtain model adjustment data according to a warehouse to which the automated guided vehicle belongs; adjusting the created model according to the model adjustment data;
and then, the initial configuration data determining module determines the initial configuration quantity of the automatic guided vehicle according to the acquired factor data and the adjusted model.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic device for determining the number of configurations of an automated guided vehicle.
The electronic equipment for determining the configuration number of the automatic guided vehicle comprises: one or more processors; a storage device to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement any of the above methods of determining a configured number of automated guided vehicles.
To achieve the above object, according to a further aspect of the embodiments of the present invention, there is provided a computer readable medium having a computer program stored thereon, wherein the program is executed by a processor to implement any one of the above methods for determining the number of configured automated guided vehicles.
One embodiment of the above invention has the following advantages or benefits: according to the method and the device, the AGV initial configuration data are determined based on the real quantity according to the created model, the current technical efficiency problem is considered, and the problems of order hanging and the like are avoided. And after the initial configuration data is determined through the model, the initial configuration data is adjusted according to the caching digits of the workstation, and the final AGV configuration quantity is calculated.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic view of a main flow of a method of determining a configuration number of automated guided vehicles according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a ridge estimation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the major modules of an apparatus for determining the number of configured automated guided vehicles according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a method for automatically configuring a number of vehicles according to an embodiment of the present invention, and as shown in fig. 1, the method for automatically configuring a number of vehicles according to an embodiment of the present invention mainly includes:
step S101: factor data is obtained according to the created model, and the factor data at least comprises the number of workstations and the number of pieces to be picked. Wherein the model may be created according to a Cobb-Douglas production function (C-D production function). In order to measure the relationship between input (the number of workstations and the number of automated guided vehicles) and output (the number of pickers), it is necessary to set a functional relationship between input and output, which is called a production function, and which represents the relationship between the number of various production elements (i.e., the input) used in production and the maximum output that can be produced, without changing the technical level, over a certain period of time. The nature of the production function reflects the state of the art, and in the present embodiment, the production function used is a C-D production function. Because the form is concise, the method can be converted into a linear form, and parameter estimation is convenient. Furthermore, the parameters have clear economic meanings, useful information about production scale remuneration and elasticity can be obtained, and the rationality of the model can be checked. Of course, in another embodiment, the model may be designed in other functional forms, and the effect of the model may not be as accurate and convenient as the model created by the C-D production function.
Specifically, a model is designed according to the kobuk-douglas production function, and the model is described as follows:
Figure BDA0001704645180000061
y is the number of picks, car is the number of automated guided vehicles configured, station is the number of workstations, beta0，β1And beta2Is a parameter, and 0<β1,β2<1; according to the acquired sample data pair parameter beta0，β1And beta2And (6) estimating. Beta is a1And beta2Respectively, the yield flexibility of AGVs and workstations, i.e. the percentage of yield (i.e. the number of pickles) that increases when the input (AGV or workstation) for a certain production element increases by 1%. According to the economic implications of the output, 0 is generally required<β1,β2<1. Considering random factors, taking the natural logarithm of the formula and converting into:
lny＝lnβ0+β1lncar+β2lnstation+ε
where ε is assumed to obey the Gaussian-Markov assumption, i.e., ε mean is 0, independent covariance, uncorrelated. Estimating beta through input and output sample data0，β1And beta2Then, the specific quantity relation between the number of the pickers, the AGV configuration quantity and the number of the workstations is found out. Further, when the number of new pickers and the number of workstations are known, the number can be measuredThe number of AGVs required.
After the basic form of the model is constructed through the process, parameters in the model are estimated by using the acquired sample data. Wherein the sample data is real data. In consideration of the working hour system of the unmanned bin, the number of workstations, the configured number of AGVs and the number of finished pickers which are opened in one or more unmanned bins per hour can be extracted as sample data in units of each hour. In the acquired sample data, there may be erroneous data that does not conform to the true situation. For example, for an unmanned bin, the data is either non-working time, all three items of data are 0, or working time, all three items of data are not 0, and in the data per hour (natural hour), the value of any variable of the number of pickers, the number of workstations, and the number of AGVs, which takes 0, is determined to be an abnormal value. For another example, the number of picks per hour per work station is considered to be limited based on the real work efficiency (the specific value needs to be considered based on the real efficiency of the unmanned bin), and when the number of picks per hour per work station is high, there is a high possibility of other abnormal situations. Therefore, before parameter estimation, more than one set of sample data is acquired, and each set of sample data comprises the historical configuration number of the automatic guided vehicle, the work station number and the picking number. And for the sample data of each group, judging whether the historical configuration quantity, the workstation quantity or the number of the pickers of the automatic guided vehicle in the sample data is zero; if yes, the sample data is removed. Judging whether the number of the pickers in the sample data is larger than a preset threshold value or not; if yes, the sample data is removed. As to the above two determination sequences, the embodiment of the present invention is not limited, and only sample data in which the number of the automated guided vehicle historical dispositions, the number of the workstations, or the number of the pickers is zero, and sample data in which the number of the pickers is greater than the preset threshold value are removed.
And according to the acquired sample data, comparing the parameter beta with the parameter beta0，β1And beta2In the estimation process, standardizing the acquired sample data;from the normalized sample data, and fitting the parameter β by ridge regression0，β1And beta2And (6) estimating. When multiple collinearity exists in the independent variable, if the least square method is directly used, although the parameter estimation result is an unbiased estimation quantity, the variance of the parameter estimation result is large, the robustness of the estimation result is poor, and the coefficient result is probably opposite to the theoretical expectation. In the embodiment of the invention, the number of AGVs and the number of workstations have a high correlation relationship, namely multiple collinearity, so that a least square method is not suitable for directly estimating parameters. Ridge regression (Tikhonov regression) is a biased estimation regression method dedicated to collinear data analysis, and is essentially an improved least square estimation method, wherein unbiased property of the least square method is abandoned, so that regression coefficients obtained at the cost of losing part of information and reducing precision are more consistent with a practical and reliable regression method, and fitting of pathological data is stronger than that of the least square method. Therefore, solving multiple collinearity can use ridge regression, which is very similar to least squares, except that a penalty function is added based on the sum of squared residuals, and the estimated value of the parameter is obtained by minimizing the following formula:
Figure BDA0001704645180000081
in the above formula, λ ≧ 0 is the tuning parameter, which can be determined by cross validation.
Figure BDA0001704645180000082
For the penalty function, the parameter estimate gets closer to 0 as λ gets larger. If written in matrix form, the parameter estimates are given by:
Figure BDA0001704645180000083
the role of the lambda of the ridge estimate is to adjust the balance between bias and variance, at the expense of estimator unbiased, in exchange for a large drop in estimator variance, ultimately reducing
Figure BDA0001704645180000084
MSE (mean square error). In an embodiment of the present invention, the geometry of the ridge estimate is explained in FIG. 2, considering only the number of AGVs and the number of workstations.
Step S102: and determining the initial configuration quantity of the automatic guided vehicles according to the acquired factor data and the acquired model. For each unmanned warehouse, there is a certain difference, and in order to determine the configuration data of the AGV warehouse more accurately, before the step S102, model adjustment data may be obtained according to the warehouse (unmanned warehouse) to which the automated guided vehicle belongs. The adjustment data is historical data of the warehouse, and the created model is adjusted through the historical data, and the estimated parameters may or may not be changed through the adjustment. And then, determining the initial configuration number of the automatic guided vehicles according to the acquired factor data and the adjusted model.
Step S103: and adjusting the initial configuration data according to the caching digits of the workstation. Because each workstation is provided with a fixed buffer bit number, for example, the buffer bit number of each workstation is 6, 6 trolleys are bound by one workstation at most to carry 6 shelves, and if the initial configuration number of the AGVs is determined to be 10 under the condition that only 1 workstation works, all the 10 AGVs cannot be configured. Therefore, after the initial configuration data of the AGV is determined by the model, the initial configuration data needs to be adjusted by the number of cache bits of the workstation.
When the number of to-be-picked items is obtained, the number of to-be-picked items in a future period of time cannot be known, and the number of to-be-picked items can be determined through the historical picking data of the unmanned bin. Wherein the number of picks per hour within the historical period is obtained, the obtained data being a set of data, wherein a mean, a 75% quantile and a 95% quantile of the set of real data can be determined, each of which can be taken as the number of picks to be picked. In an embodiment of the present invention, the number of pickups determined is 240, 378 and 657 as shown in the following table.
Since the number of buffer bits per station (e.g., 6) is fixed, if the number of stations is 1, the number of pieces to be picked is 240, 378, and 657, the initial configuration number of AGVs determined by the created model is 10, 11, and 15, respectively. Since AGVs larger than 7 cannot be configured, 7 AGVs are configured at most by adjusting the initial configuration data, and the configured resources cannot meet the production requirement at this time, a hang list is generated under the current resource configuration. For example, with a number of stations of 1, the number of pieces to be sorted is 240, 378 and 657, and only a maximum of 7 AGVs can be configured, which generate a number of hangs of 29, 167 and 446 respectively. When the number of workstations is 2, 3, 4, the number of outstanding pieces is 240, 378, 657, and the adjusted AGV configuration number and hang number are shown in the following table.
Figure BDA0001704645180000091
According to the method and the device, the functional relation between the number of the picked pieces and the number of the workstations and the AGV configuration number is fitted according to the operation data, the parameters are estimated based on real sample data, and then a model is created, so that the optimal AGV configuration number under the real technical level is obtained. Because the influence of the abnormal value on the model is relatively large, the abnormal value in the sample data is removed after the sample data is acquired. And determining the reasonable AGV configuration quantity based on the number of the picked pieces, the number of the workstations and the caching digits of the workstations according to the obtained functional relation. The initial configured number of AGVs is calculated from the created model, given the number of pieces to be picked per hour and the number of workstations. For the number of pieces to be sorted, the average, 75% quantile and 95% quantile of the number of actually finished sorted pieces per hour of the warehouse can be adopted as the number of pieces to be sorted. The total number of the workstations is fixed, and the value range of the number of the workstations can be set according to the data of the number of the historical opening workstations. After the initial configuration data are determined through the model, the initial configuration data are adjusted according to the caching digits of the workstation, and therefore the configuration data are more reasonable. The model in the embodiment of the invention is simple, but the model can obtain useful information about production scale reward and elasticity, can check the reasonability of the model, and not only can determine the result accurately and effectively, but also has strong practicability.
Fig. 3 is a schematic diagram of main modules of an apparatus for determining the number of configured automated guided vehicles according to an embodiment of the present invention, and as shown in fig. 3, an apparatus 300 for determining the number of configured automated guided vehicles according to an embodiment of the present invention includes an acquisition factor data module 301, an initial configuration data determining module 302, and a configuration data adjusting module 303.
The factor data obtaining module 301 is configured to obtain factor data according to the created model, where the factor data at least includes the number of workstations and the number of pieces to be picked.
The module for determining initial configuration data 302 is used for determining the initial configuration number of the automated guided vehicle according to the acquired factor data and the model.
The configuration data adjusting module 303 is configured to adjust the initial configuration data according to the number of cache bits of the workstation.
The device for determining the configuration number of the automated guided vehicles in the embodiment of the invention further comprises a model creation module, which is used for designing a model according to a Kobuk-Douglas production function, and the model is described as follows:
Figure BDA0001704645180000111
y is the number of picks, car is the number of automated guided vehicles configured, station is the number of workstations, beta0，β1And beta2Is a parameter, and 0<β1,β2<1. And, according to the sample data obtained, the parameter beta0，β1And beta2And (6) estimating. The model creating module is also used for standardizing the acquired sample data; and, from the normalized sample data, and by ridge regression on the parameter β0，β1And beta2And (6) estimating. The model creating module is further used for acquiring more than one group of sample data, and each group of sample data comprises the historical configuration number of the automatic guided vehicle, the number of the workstations and the number of the pickers; and judging the historical configuration number, the work station number or the picking number of the automatic guided vehicles in the sample data of each groupWhether the number of the selected pieces is zero or not is judged; if yes, the sample data is removed. Judging whether the number of the pickers in the sample data is larger than a preset threshold value or not; if yes, the sample data is removed.
The device for determining the configuration quantity of the automated guided vehicles in the embodiment of the invention further comprises a model adjusting module, a model adjusting module and a model adjusting module, wherein the model adjusting module is used for acquiring model adjusting data according to a warehouse to which the automated guided vehicles belong; and adjusting the created model according to the model adjusting data. And then, the initial configuration data determining module determines the initial configuration quantity of the automatic guided vehicle according to the acquired factor data and the adjusted model.
According to the method and the device, the functional relation between the number of the picked pieces and the number of the workstations and the AGV configuration number is fitted according to the operation data, the parameters are estimated based on real sample data, and then a model is created, so that the optimal AGV configuration number under the real technical level is obtained. Because the influence of the abnormal value on the model is relatively large, the abnormal value in the sample data is removed after the sample data is acquired. And determining the reasonable AGV configuration quantity based on the number of the picked pieces, the number of the workstations and the caching digits of the workstations according to the obtained functional relation. The initial configured number of AGVs is calculated from the created model, given the number of pieces to be picked per hour and the number of workstations. For the number of pieces to be sorted, the average, 75% quantile and 95% quantile of the number of actually finished sorted pieces per hour of the warehouse can be adopted as the number of pieces to be sorted. The total number of the workstations is fixed, and the value range of the number of the workstations can be set according to the data of the number of the historical opening workstations. After the initial configuration data are determined through the model, the initial configuration data are adjusted according to the caching digits of the workstation, and therefore the configuration data are more reasonable. The model in the embodiment of the invention is simple, but the model can obtain useful information about production scale reward and elasticity, can check the reasonability of the model, and not only can determine the result accurately and effectively, but also has strong practicability.
Fig. 4 illustrates an exemplary system architecture 400 of a method of determining a configured number of automated guided vehicles or an apparatus for determining a configured number of automated guided vehicles to which embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 401, 402, 403. The background management server can analyze and process the received data such as the product information inquiry request and feed back the processing result to the terminal equipment.
It should be noted that the method for determining the configured number of the automated guided vehicle provided by the embodiment of the present invention is generally executed by the server 405, and accordingly, the device for determining the configured number of the automated guided vehicle is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments 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 in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention 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 invention, 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 the present invention, 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.
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 invention. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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 modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an obtain factor data module, a determine initial configuration data module, and a configuration data adjustment module. The names of these modules do not in some cases constitute a definition of the module itself, and for example, the get factor data module may also be described as a "module that gets factor data according to the created model".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring factor data according to the created model, wherein the factor data at least comprises the number of workstations and the number of pieces to be picked; determining the initial configuration quantity of the automatic guided vehicles according to the acquired factor data and the acquired model; and adjusting the initial configuration data according to the caching digits of the workstation.
According to the method and the device, the functional relation between the number of the picked pieces and the number of the workstations and the AGV configuration number is fitted according to the operation data, the parameters are estimated based on real sample data, and then a model is created, so that the optimal AGV configuration number under the real technical level is obtained. Because the influence of the abnormal value on the model is relatively large, the abnormal value in the sample data is removed after the sample data is acquired. And determining the reasonable AGV configuration quantity based on the number of the picked pieces, the number of the workstations and the caching digits of the workstations according to the obtained functional relation. After the initial configuration data are determined through the model, the initial configuration data are adjusted according to the caching digits of the workstation, and therefore the configuration data are more reasonable. The model in the embodiment of the invention is simple, but the model can obtain useful information about production scale reward and elasticity, can check the reasonability of the model, and not only can determine the result accurately and effectively, but also has strong practicability.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (12)

1. A method of determining an automated guided vehicle configuration number, comprising:
acquiring factor data according to the created model, wherein the factor data at least comprises the number of workstations and the number of pieces to be picked;
determining the initial configuration quantity of the automatic guided vehicles according to the acquired factor data and the model;
and adjusting the initial configuration data according to the caching digits of the workstation.
2. The method of claim 1, further comprising, prior to obtaining factor data from the created model:
designing a model according to a kobuki-douglas production function, the model being described as:
Figure FDA0001704645170000011
y number of picks and car number of auto-guideThe number of allocated transport vehicles, station, is the number of workstations, beta0，β1And beta2Is a parameter, and 0<β1,β2<1；
According to the acquired sample data pair parameter beta0，β1And beta2And (6) estimating.
3. The method of claim 2, wherein the parameter β is determined according to the acquired sample data0，β1And beta2The step of performing the estimation comprises:
standardizing the acquired sample data;
from the normalized sample data, and fitting the parameter β by ridge regression0，β1And beta2And (6) estimating.
4. The method of claim 2, wherein the parameter β is determined from the acquired sample data0，β1And beta2Before the estimation, the method further comprises the following steps:
acquiring more than one group of sample data, wherein each group of sample data comprises the historical configuration number of the automatic guided vehicle, the number of the workstations and the number of the picked items;
judging whether the historical configuration quantity, the work station quantity or the number of the pickers in the sample data of each group is zero or not; if yes, removing the sample data;
judging whether the number of the pickers in the sample data is larger than a preset threshold value or not; if yes, the sample data is removed.
5. The method of claim 1, further comprising, prior to determining the initial configured number of automated guided vehicles based on the obtained factor data and the model: obtaining model adjustment data according to a warehouse to which the automatic guided transporting vehicle belongs; adjusting the created model according to the model adjustment data;
and then, determining the initial configuration quantity of the automatic guided vehicle according to the acquired factor data and the adjusted model.
6. An apparatus for determining a configured number of automated guided vehicles, comprising:
the factor data acquisition module is used for acquiring factor data according to the created model, wherein the factor data at least comprises the number of workstations and the number of pieces to be picked;
the initial configuration data determining module is used for determining the initial configuration quantity of the automatic guided vehicles according to the acquired factor data and the model;
and the configuration data adjusting module is used for adjusting the initial configuration data according to the caching digits of the workstation.
7. The apparatus of claim 6, further comprising a model creation module for designing a model according to the Cabub-Douglas production function, the model being described as:
Figure FDA0001704645170000021
y is the number of picks, car is the number of automated guided vehicles configured, station is the number of workstations, beta0，β1And beta2Is a parameter, and 0<β1,β2<1；
And, according to the sample data obtained, the parameter beta0，β1And beta2And (6) estimating.
8. The apparatus of claim 7, wherein the model creation module is further configured to normalize the obtained sample data; and, from said normalized sample data, and by ridge regression on the parameter β0，β1And beta2And (6) estimating.
9. The apparatus of claim 7, wherein the model creation module is further configured to obtain more than one set of sample data, each set of sample data comprising a historical configuration quantity of automated guided vehicles, a quantity of workstations, and a quantity of pickers; and judging whether the historical configuration number, the work station number or the number of the pickers in the sample data of each group is zero; if yes, removing the sample data; judging whether the number of the pickers in the sample data is larger than a preset threshold value or not; if yes, the sample data is removed.
10. The apparatus of claim 6, further comprising a model adjustment module for obtaining model adjustment data according to a warehouse to which the automated guided vehicle belongs; adjusting the created model according to the model adjustment data;
and then, the initial configuration data determining module determines the initial configuration quantity of the automatic guided vehicle according to the acquired factor data and the adjusted model.
11. An electronic device for determining a configured number of automated guided vehicles, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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