CN113762563A - Warehousing goods space optimal layout method and system based on order grey correlation analysis - Google Patents

Warehousing goods space optimal layout method and system based on order grey correlation analysis Download PDF

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CN113762563A
CN113762563A CN202010651540.1A CN202010651540A CN113762563A CN 113762563 A CN113762563 A CN 113762563A CN 202010651540 A CN202010651540 A CN 202010651540A CN 113762563 A CN113762563 A CN 113762563A
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邹霞
于兴
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Abstract

The invention discloses a warehousing goods space optimal layout method and a warehousing goods space optimal layout system based on order grey correlation analysis, wherein the method comprises the following steps of: acquiring a plurality of order information, and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order top items; calculating the association degree between the items in the order by adopting the gray association degree according to the order-ex-warehouse frequency matrix; clustering the items based on the association degree among the items; and optimizing the goods location of the item according to the clustering result and the full turnover rate principle.

Description

Warehousing goods space optimal layout method and system based on order grey correlation analysis
Technical Field
The invention belongs to the technical field of automatic warehousing, and particularly relates to a warehousing goods space optimal layout method and system based on order grey correlation analysis.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In order to improve the satisfaction degree of customers and increase the viscosity of the customers, the current domestic mainstream B2C E-commerce platform continuously increases the logistics speed. By taking a exclusive meeting, Jingdong and vegetable and bird as examples, the warehousing is also basically fully automated, the traditional 'person to person' picking mode is changed, the 'person to person' picking is realized by adopting loading and unloading and carrying equipment in different forms, only a very short time is needed for issuing an order to deliver a commodity out of the warehouse, and the problems of order overstock, warehouse-out bottleneck and the like are effectively reduced. Much of the research on order picking focuses on order clustering, item clustering, and optimization of picking paths.
In the item clustering research, the commodities with high association degree tend to be gathered together, the manual sorting efficiency is improved, and the sorting path and time are shortened. However, AVS/RS (Autonomous Vehicle Storage and Retrieval System) has a completely opposite clustering analysis principle, and the commodities with high relevance are separately placed, so that the utilization rate of the machine and the order reaction speed can be improved. The method for clustering items through order grey correlation analysis is a heuristic clustering method, has the characteristics of quickness and easiness in implementation, and the existing grey correlation analysis model comprises the following steps: a Duncus grey correlation degree, a grey correlation degree based on a similarity view angle, a grey correlation degree based on a proximity view angle, and the like. However, if the method is directly applied to the B2C order, the distinguishing performance of the correlation is poor and the calculation is complicated if a dune grey correlation analysis method is adopted; if a gray relevance analysis method of a similarity visual angle or a proximity visual angle is adopted, when the ex-warehouse frequency of the product i and the product j presents a cross distribution condition, the model cannot present the real relevance of the two products. That is, the existing grey correlation analysis method is no longer suitable for the item clustering of the industry.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a warehousing goods space optimization layout method and system based on order gray correlation analysis.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
a warehousing goods space optimization layout method based on order grey correlation analysis comprises the following steps:
acquiring a plurality of order information, and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order top items;
calculating the association degree between the items in the order by adopting the gray association degree according to the order-ex-warehouse frequency matrix;
clustering the items based on the association degree among the items;
and optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
Further, constructing an order-ex-warehouse frequency matrix according to the ex-warehouse information of the plurality of order top items comprises:
constructing an order-ex-warehouse frequency matrix, wherein each row of the order-ex-warehouse frequency matrix corresponds to one order, each column corresponds to one item, and the ex-warehouse conditions of the items in all the orders are recorded;
if an order includes an item, the value of the corresponding position in the matrix is marked as 1, and if not, the value is marked as 0.
Further, calculating the association degree between the items in the order by using the gray association degree comprises the following steps:
extracting the ex-warehouse information sequence of each item based on the order-ex-warehouse frequency matrix;
for any two items i and j, the corresponding ex-warehouse information sequences are respectively recorded as: xi＝(xi(1)，xi(2)，……，xi(on))，Xj＝(xj(1)，xj(2)，……，xj(on)), wherein on denotes the amount of orders, xi(k) And xj(k) Respectively representing the ex-warehouse information of the item i and the item j in the kth order, the value is 1 or 0, k belongs to [1, 0n ]]；
The calculation formula of the association degree between the item i and the item j is as follows:
Figure BDA0002575149070000031
Figure BDA0002575149070000032
wherein, when xi(n) and xjWhen (n) is not zero at the same time, the correlation coefficient
Figure BDA0002575149070000033
When x isi(k)＝xj(k) When the content is equal to 0, the content,
Figure BDA0002575149070000034
further, a weight clustering method or a two-level clustering method is adopted for clustering the items.
Further, clustering the items by using a weight clustering method comprises:
supposing that commodities are placed on a row of shelves a in advance, each row of shelves contains b layers, each row of shelves is provided with one elevator, each layer is provided with an independent shuttle vehicle, the number of categories is placed as uniformly as possible, and the objective function is as follows:
Figure BDA0002575149070000035
wherein, wherein: g ═ a, h ═ a ═ b (b-1)/2, xi，yj≥0，xiIndicating degree of association of items between different shelves, yjIndicating the degree of item association, C, between internal layers of the shelf1Weight representing degree of association of items between shelves, C2Representing item association weight between internal layers of the shelf.
Further, clustering the items by using a two-level clustering method comprises:
supposing that commodities are placed on a row a of shelves in advance, each row of shelves contains b layers, each row of shelves is provided with a hoisting machine, each layer is provided with an independent shuttle vehicle, and the quantity of the categories is as uniform as possible;
first, the items are grouped into class a, the objective function:
Figure BDA0002575149070000036
wherein, costijRepresenting the degree of association between the i commodity and the j commodity on two different shelves in the a shelves;
then, for the items on a shelves, genetic iteration is carried out respectively, and the objective function is as follows:
Figure BDA0002575149070000041
wherein, costxij≥0，
Figure BDA0002575149070000042
i＜j，
Figure BDA0002575149070000043
And the x objective functions respectively take fuzzy optimal values.
Further, a weight clustering method or a two-level clustering method is solved based on a genetic algorithm.
One or more embodiments provide a warehousing goods space optimization layout system based on order grey correlation analysis, comprising:
the data preprocessing module is used for acquiring a plurality of order information and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order upper items;
the relevancy calculation module is used for calculating the relevancy among the items in the order by adopting gray relevancy according to the order-ex-warehouse frequency matrix;
the item clustering module is used for clustering the items based on the association degree among the items;
and the goods location optimizing module is used for optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
One or more embodiments provide an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions, when executed by the processor, implementing the warehouse freight space optimization layout method based on order gray correlation analysis.
One or more embodiments provide a computer-readable storage medium for storing computer instructions which, when executed by a processor, implement the warehouse cargo space optimization layout method based on order grey correlation analysis.
The above one or more technical solutions have the following beneficial effects:
the traditional grey correlation model is improved by analyzing the characteristics of the B2C E-commerce order, the improved grey correlation model suitable for the B2C E-commerce order is provided, the correlation of commodity items on the same order can be well reflected, the calculated correlation accords with the actual correlation, and the rationality of subsequent goods space optimization is ensured.
A clustering model and a secondary clustering model based on weight are respectively constructed, and are applied to clustering of items, and the roadway and the layer where the commodity is located are sequentially distributed, so that the equipment utilization rate is improved as much as possible, the order processing time is shortened, and the equipment energy consumption is reduced.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flowchart of a warehousing goods space optimization layout method based on order grey correlation analysis according to an embodiment of the present invention;
FIG. 2 is a flow chart of clustering based on genetic algorithms according to one embodiment of the present invention;
fig. 3 is a schematic diagram of fitness convergence in the clustering process based on the genetic algorithm according to an embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment provides a warehousing goods space optimization layout method based on order grey correlation analysis, which is characterized in that according to the characteristics of e-commerce orders, the correlation degree among different items is calculated by adopting the improved grey correlation degree, then the item clustering is carried out by adopting a genetic algorithm according to the characteristics of population evolution, the warehousing layout is further optimized, and the purpose of improving the logistics efficiency is achieved. Specifically, the method specifically comprises the following steps:
step 1: acquiring order information, and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order upper items;
step 2: calculating the association degree between the items in the order by adopting the gray association degree according to the order-ex-warehouse frequency matrix;
and step 3: clustering the items based on the association degree among the items;
and 4, step 4: and optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
In the step 1, due to the characteristics of the AVS/RS system, a plurality of commodities of a single item are stored in a single turnover box, and a goods space is allocated. The order quantity of each item in the warehouse is recorded on each order of the B2C E-commerce, and due to the storage form of the product and the operation characteristics of the AVS/RS, under the actual condition, the quantity requirement of most orders can be met by taking the turnover box out of the warehouse once, so that in the research, the order quantity of the items can be ignored, only the order frequency of the items is considered, and the ex-warehouse frequency C epsilon (0, 1) on the order items is considered.
In this embodiment, 1000 pieces of order information of a certain e-commerce warehouse are extracted, and 189 kinds of commodities are included in each order, and each order includes the ex-warehouse information of the 189 kinds of commodities, that is, the order is given as on 1000 and sn 189. And constructing an order-ex-warehouse frequency matrix. It is composed ofIn this embodiment, each row of each order-ex-warehouse frequency matrix corresponds to one order, each column corresponds to one type of commodity, and the ex-warehouse conditions of all commodities in all orders are recorded, where the row number is consistent with the number of orders, and the column number is consistent with the number of commodity types contained in all orders. For each order, if the order contains a certain commodity, the numerical value of the corresponding position in the matrix is recorded as the commodity ex-warehouse frequency 1, and if the order does not contain the commodity, the ex-warehouse frequency is 0. And (3) recording each column in the order-ex-warehouse frequency matrix as an item ex-warehouse information sequence: xi＝(xi(1)，xi(2)，......，xi(n)), wherein xi(k) And representing the ex-warehouse information of the product i in the kth order, and taking the value as 1 or 0.
1000 product orders including 189 products are selected, the product ex-warehouse frequency is used as a source data sequence to perform cluster analysis on E-commerce products, the waste of waiting time of the E-commerce products in and out of the warehouse is reduced, and the intelligent warehouse logistics efficiency is improved.
In the step 2, according to the characteristics of the order data source, a gray correlation analysis model suitable for discrete data is adopted.
Existing grey correlation analysis models include: a Duncus grey correlation degree, a grey correlation degree based on a similarity view angle, a grey correlation degree based on a proximity view angle, and the like. The embodiment sequentially analyzes the applicability of the gray correlation analysis model in the e-commerce product clustering problem.
(1) Degree of Deng grey correlation
Gray correlation coefficient ω:
Figure BDA0002575149070000071
wherein the resolution factor
Figure BDA0002575149070000072
The number of sequence rows i e (1, 2, 3 … sn-1), the number of elements c per sequence.
Gray correlation degree R:
Figure BDA0002575149070000073
as can be seen from the data source characteristics herein, X (k) e { X | X ═ 0 or 1}, so
Figure BDA0002575149070000074
Then
Figure BDA0002575149070000075
Or 1, only two correlation coefficients can be calculated by adopting the Deng grey correlation degree.
Suppose in x0(k) And any one of xi(k) In comparison of sequences (x)0(k) And xi(k) Representing the ex-warehouse frequency conditions of product 0 and product i in 1000 orders respectively), and if n groups of data corresponding to 0 and 1 and (1000-n) groups of data corresponding to 0 and 0 or 1 and 1 exist, the data are processed, and then the product is stored in the storage device
Figure BDA0002575149070000076
It can be seen that the relevance R is only related to n, but in the practical problem studied here, when the data corresponding to 1, 1 appears to represent that two sets of data are related, and when 0, 1 or 0, 0 appears to represent that two products are not related in the order, however, the model cannot distinguish the data distribution of 0, 0 and 1, 1; in addition, the reference sequence needs to be continuously transformed when the model is adopted to calculate the association degree of the E-commerce product, and the calculation is complex. Therefore, the dune grey correlation model is not suitable for product clustering analysis based on the product order condition of the ex-warehouse frequency data.
(2) Grey relevance based on similarity perspective
Set System sequence Xi＝(xi(1)，xi(2)，……，xi(on))，Xj＝(xj(1)，xj(2)，……，xj(on)) the starting point zeroing sequence is:
Figure BDA0002575149070000081
the correlation coefficient of the product j and the product i in the nth order is recorded as
Figure BDA0002575149070000082
Order to
Figure BDA0002575149070000083
When in use
Figure BDA0002575149070000084
And the (C) and (D) are,
Figure BDA0002575149070000085
are equal in length and are 1 time-distance sequences,
Figure BDA0002575149070000086
then degree of association epsilonij：
Figure BDA0002575149070000087
According to the principle of grey correlation degree of the similarity visual angle, the data starting point is zero-ized by the model, the model is suitable for measuring the similarity of geometric shapes among sequences, and the similarity of commodities among orders cannot be reflected, so that the model is not suitable for B2C E-commerce product clustering analysis.
Description of the examples: assuming that the ex-warehouse frequency of the i product in the on orders is 1 every time, the ex-warehouse frequency of the j product in the on orders is 0, the distribution situation is shown as a table, and according to the clustering principle, the association degree of the i product and the j product is 0.
Table 1: frequency distribution diagram for ex-warehouse of i, j products
k 1 2 3 …… on
Frequency of ex warehouse of i product 1 1 1 …… 1
Frequency of product warehouse-out (times) 0 0 0 …… 0
The gray correlation calculation formula according to the similarity visual angle can be known as follows:
Figure BDA0002575149070000091
therefore, it is not only easy to use
Figure BDA0002575149070000092
That is, the result of the obtained gray scale correlation is 1, which is clearly not in accordance with the actual situation.
(3) Grey relevance based on proximity perspective
Set System sequence Xi＝(xj(1)，xi(2)，……，xi(on))，Xj＝(xj(1)，xj(2)，……，xj(on)), the correlation coefficient of the products i and j in the k-th order is recorded as
Figure BDA0002575149070000093
Order to
Figure BDA0002575149070000094
When X is presentiAnd XjAre equal in length and are all 1-time distance sequences,
Figure BDA0002575149070000095
the degree of correlation ρij：
Figure BDA0002575149070000096
The gray relevance based on the proximity visual angle is used for measuring the proximity of a behavior sequence on the same order, and from the characteristics of data samples in the text, the E-commerce product clustering analysis based on the ex-warehouse frequency just needs to compare the proximity of the sequence on the order, but according to the original formula, the model can not present the real relevance of the i product and the j product when the ex-warehouse frequency presents a cross distribution condition.
Description of the examples:
assuming that the ex-warehouse frequency of the first 500 orders is 1 and the ex-warehouse frequency of the second 500 orders is 0 in 1000 orders of i products; and the ex-warehouse frequency of j products in the first 500 orders is 0, the ex-warehouse frequency of the latter 500 orders is 1, the frequency distribution is shown in the table, and according to the clustering principle of the problems researched herein, the association degree of the products i and j should be 0.
Table 2: frequency distribution diagram for ex-warehouse of i, j products
k 1 2 …… 500 501 502 …… 1000
Frequency of ex warehouse of i product 1 1 1 1 0 0 0 0
Frequency of product warehouse-out (times) 0 0 0 0 1 1 1 1
The gray correlation degree calculation formula according to the proximity visual angle can be known as follows:
Figure BDA0002575149070000101
the correlation calculation result is obviously inconsistent with the actual situation. Consider the situation that the model can not present actual correlation degree when two sequences are distributed in a cross way, and can not distinguish the phenomena of 0, 0 corresponding and 1, 1 corresponding. Therefore, in this embodiment, the following correction is performed on the basis of the proximity gray correlation model:
let correlation coefficient
Figure BDA0002575149070000102
(when x)i(k) And xj(k) Not zero at the same time);
if there is an integer k, k ∈ [1, on ]]So that x isi(k)＝xj(k) When the value is equal to 0, then
Figure BDA0002575149070000103
Figure BDA0002575149070000104
The corrected degree of association ρij *：
Figure BDA0002575149070000105
It can be seen that the correlation degree after correction satisfies the even symmetry, proximity and normalization in the gray correlation formula.
The improved grey correlation based on proximity viewing angle has the following properties:
1) correlation degree of gray rhoij *∈(0，1)
2)ρij *＝ρji *Has even symmetry
3) If Xi＝X j0 or XiAround XjSwing and XiAt XjThe area of the upper face is equal to the area of the lower face, then pii *＝0.001≈0
4) When X is presenti＝Xj＝1，ρij *＝1
Based on the above analysis, the present embodiment calculates the degree of association between items using the corrected degree of association of gray based on the proximity perspective.
In the step 3, the system needs to meet the storage requirements of 189 items, so that an AVS/RS is set as a double tunnel, each tunnel is vertically operated by one elevator, and the number of cargo grids of each tunnel is 100; the number of the shelf layers is 4, the height of the single layer is 3 meters, and each layer is provided with a shuttle vehicle to finish horizontal operation; the number of the shelf rows is 25, the row width is 2 meters, and the number of the shelf rows is 200 storage positions. After the goods are delivered out of the warehouse, the goods are conveyed to a picking platform by a conveyor, and the delivery operation pulled by orders is realized by utilizing an order global table.
This embodiment provides two methods for clustering items, which specifically include: a weight clustering method based on genetic algorithm and a two-stage clustering method based on genetic algorithm.
(1) Weight clustering method based on genetic algorithm
Assuming that sn commodities are placed on a rows of shelves a in advance, each row of shelves contains b layers, each row of shelves is provided with a hoist, each layer is provided with an independent shuttle vehicle, the number of categories is placed as uniformly as possible, and the objective function is as follows:
Figure BDA0002575149070000111
wherein, wherein: g ═ a
h＝a*b*(b-1)/2
xiRepresent a differenceDegree of item association between shelves, yjIndicating the degree of item association, C, between internal layers of the shelf1Weight representing degree of association of items between shelves, C2Representing item association weight between internal layers of the shelf. Considering that each row of goods shelves is provided with only one lifting machine and each layer is provided with a shuttle car, the weight of the item association degree among the goods shelves tends to be increased, and C is selected1＝0.8，C2＝0.2。
(2) Two-stage clustering method based on genetic algorithm
The algorithm adopts the inter-shelf unit association coefficient and the intra-shelf unit association coefficient to construct an objective function with fixed weight, and considers that the artificial factors determined by the weight may generate errors to influence the optimal solution, the method adopts two-stage clustering as a comparison group for comparison, and the two-stage clustering comprises the following steps:
1) inputting order information and calculating the degree of association between items
2) First-level clustering: using a genetic algorithm, clustering the items into a class a, an objective function:
Figure BDA0002575149070000121
wherein, costijIndicating the degree of association between the i and j items on two different shelves of the a shelves.
3) And (3) second-level clustering: and (3) respectively carrying out genetic iteration on the items on the a shelves, wherein the objective function is as follows:
Sx＝max(∑costxij)
4) wherein costxij is more than or equal to 0,
Figure BDA0002575149070000122
i＜j，
Figure BDA0002575149070000123
and the x objective functions respectively take fuzzy optimal values.
The evolution process of the organism depends on the evolution of the population genes, and the population genes consist of the gene library of each generation of individuals, so when the gene library of the population is continuously updated in an iterative manner, the evolution of the organism is driven.
For the above weight clustering method based on genetic algorithm and the two-stage clustering method based on genetic algorithm, the genetic algorithm is used to solve the objective function therein, and the solving process is as follows:
1) randomly generating a x b initial populations, wherein each population comprises sn commodities, and coding positions of the sn commodities to form chromosomes;
2) calculating the fitness of each individual to generate a generation population (initial population);
3) judging whether a termination condition is met, if so, outputting a result, and if not, continuing;
4) the roulette is selected for 20 times, and 2 individuals are randomly selected from the 20 selections to serve as iteration objects;
5) judging the cross probability, if the random probability is lower than the cross probability, carrying out cross operation, and if the random probability is higher than the cross probability, skipping;
6) judging the mutation probability, if the random probability is lower than the mutation probability, carrying out mutation operation, and if the random probability is higher than the mutation probability, skipping;
7) and (3) replacing corresponding objects in the original population by the new chromosomes to form a new generation of population, and jumping to 3).
Compared with a secondary clustering model, the clustering model based on the weight realizes primary clustering and can quickly obtain a clustering result; but the weight of the model needs to be adjusted continuously, so that the clustering result is more optimal.
The two clustering modes are realized by utilizing a genetic algorithm, so that the clustering result is ensured to be better, and meanwhile, the clustering can be quickly realized.
In order to simplify the complexity of goods space optimization, the goods space is mainly allocated according to the association degree between the items and the ex-warehouse frequency of the items, influence factors such as gravity are not considered, and the goods space optimization method is only suitable for the goods space optimization of the goods with similar density.
Example two
The present embodiment aims to provide a warehousing goods space optimization layout system based on order gray correlation analysis, the system includes:
the data preprocessing module is used for acquiring a plurality of order information and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order upper items;
the relevancy calculation module is used for calculating the relevancy among the items in the order by adopting gray relevancy according to the order-ex-warehouse frequency matrix;
the item clustering module is used for clustering the items based on the association degree among the items;
and the goods location optimizing module is used for optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
EXAMPLE III
It is an object of this embodiment to provide an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the steps of:
step 1: acquiring order information, and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order upper items;
step 2: calculating the association degree between the items in the order by adopting the gray association degree according to the order-ex-warehouse frequency matrix;
and step 3: clustering the items based on the association degree among the items;
and 4, step 4: and optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
Example four
It is an object of the present embodiments to provide a computer readable storage medium for storing computer instructions which, when executed by a processor, implement the steps of:
step 1: acquiring order information, and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order upper items;
step 2: calculating the association degree between the items in the order by adopting the gray association degree according to the order-ex-warehouse frequency matrix;
and step 3: clustering the items based on the association degree among the items;
and 4, step 4: and optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
The steps involved in the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
And (3) simulation results:
based on an improved grey correlation mining method, the correlation degree among commodities is calculated, the calculated correlation degree is used as initial data to be imported into a program, then a genetic algorithm is adopted for clustering, and the optimizing convergence process of the genetic algorithm is shown in figure 3. As can be seen from the figure, through the genetic algorithm clustering, the fitness is continuously converged, and the association degree of the commodities between the roadway and the goods shelf is finally close to 37, so that the method is obviously improved compared with the initial value. After multiple experiments, the two groups of genetic algorithms achieve the added fitness convergence, and the commodity clustering distribution result is shown in table 3.
Table 3: item clustering result table based on genetic algorithm
Figure BDA0002575149070000141
Figure BDA0002575149070000151
And optimizing the goods location of the item according to the clustering result and the full turnover rate principle. The items of the same-layer goods location in the same roadway are sorted according to the high and low delivery frequency, and the items with the high delivery frequency are closer to the I/O; and in the same lane, counting the ex-warehouse frequency of the item subsets, wherein the item subsets with high ex-warehouse frequency are closer to the I/O. And respectively carrying out goods location optimization on the two clustering results, wherein the results are shown in a table 4.
Table 4: goods position distribution table for goods position after optimization
Figure BDA0002575149070000161
Figure BDA0002575149070000171
Clustering results under two conditions of weight clustering based on the improved grey correlation degree and secondary clustering based on the improved grey correlation degree, and after goods space optimization is carried out according to ex-warehouse frequency, order simulation operation results are shown in table 5.
Table 5: simulation result table
Figure BDA0002575149070000172
In the earlier stage of an author, secondary clustering among the items is carried out based on the K mean value according to the similarity coefficient among the items, and the goods location optimization of AVS/RS is carried out according to the clustering result, so that the research result is used as a benchmark to carry out comparative analysis so as to verify the practicability of the improved gray correlation degree and the clustering method.
First, analysis of task processing time
In terms of task processing time, the operation time of the secondary clustering based on the improved gray relevance degree is shortest, and compared with the secondary clustering based on the K mean value, the task processing time is reduced by about 0.85%, so that the rationality of the improved gray relevance degree for clustering the items is shown, and the longer operation time of the weight clustering indicates that the influence of the weight value on the clustering effect is larger.
Second, analyzing the operation time of the equipment
In terms of the average working time of the equipment, compared with the K-means clustering, in the case of the allocation of the goods space based on the secondary clustering which improves the grey correlation degree, although the working time of the elevator is increased by 2.6%, the working time of the shuttle car is reduced by 1.64%.
Third, equipment idle time analysis
In the aspect of average idle time of equipment, compared with K-means clustering, under the condition of secondary clustering based on improved grey correlation degree, the idle time of a hoist and a shuttle vehicle is obviously reduced, the operation time of the hoist is reduced by 4.43%, but the operation time of the shuttle vehicle is reduced by 1.63%.
The effectiveness of the improved grey correlation model in AVS/RS goods allocation is verified by constructing a simulation model, and experimental results show that compared with K-means clustering, the second-level clustering model based on the improved grey correlation model can compress the job processing time by 0.85% and reduce the equipment idle time.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A warehousing goods space optimization layout method based on order grey correlation analysis is characterized by comprising the following steps:
acquiring a plurality of order information, and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order top items;
calculating the association degree between the items in the order by adopting the gray association degree according to the order-ex-warehouse frequency matrix;
clustering the items based on the association degree among the items;
and optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
2. The warehousing cargo space optimization layout method based on order gray correlation analysis according to claim 1, wherein constructing an order-ex-warehouse frequency matrix according to the ex-warehouse information of the plurality of order upper items comprises:
constructing an order-ex-warehouse frequency matrix, wherein each row of the order-ex-warehouse frequency matrix corresponds to one order, each column corresponds to one item, and the ex-warehouse conditions of the items in all the orders are recorded;
if an order includes an item, the value of the corresponding position in the matrix is marked as 1, and if not, the value is marked as 0.
3. The method as claimed in claim 1, wherein calculating the correlation between the items in the order using the grey correlation comprises:
extracting the ex-warehouse information sequence of each item based on the order-ex-warehouse frequency matrix;
for any two items i and j, the corresponding ex-warehouse information sequences are respectively recorded as: xi＝(xi(1)，xi(2)，……，xi(on))，Xj＝(xj(1)，xj(2)，……，xj(on)) wherein on denotes the number of orders, xi(k) And xj(k) Respectively representing the ex-warehouse information of the item i and the item j in the kth order, the value is 1 or 0, k belongs to [1, on ∈]；
The calculation formula of the association degree between the item i and the item j is as follows:
Figure FDA0002575149060000021
Figure FDA0002575149060000022
wherein, when xi(n) and xjWhen (n) is not zero at the same time, the correlation coefficient
Figure FDA0002575149060000023
When x isi(k)＝xj(k) When the content is equal to 0, the content,
Figure FDA0002575149060000024
4. the warehousing cargo space optimization layout method based on order grey correlation analysis as claimed in claim 1, characterized in that weight clustering method or two-level clustering method is adopted for clustering the items.
5. The warehousing cargo space optimization layout method based on order gray correlation analysis of claim 4, wherein clustering items using a weight clustering method comprises:
supposing that commodities are placed on a row of shelves a in advance, each row of shelves contains b layers, each row of shelves is provided with one elevator, each layer is provided with an independent shuttle vehicle, the number of categories is placed as uniformly as possible, and the objective function is as follows:
Figure FDA0002575149060000025
wherein, wherein: g ═ a, h ═ a ═ b (b-1)/2, xi，yj≥0，xiIndicating degree of association of items between different shelves, yjIndicating the degree of item association, C, between internal layers of the shelf1Weight representing degree of association of items between shelves, C2Representing item association weight between internal layers of the shelf.
6. The warehousing cargo space optimization layout method based on order gray correlation analysis as claimed in claim 4, wherein clustering items using a two-level clustering method comprises:
supposing that commodities are placed on a row a of shelves in advance, each row of shelves contains b layers, each row of shelves is provided with a hoisting machine, each layer is provided with an independent shuttle vehicle, and the quantity of the categories is as uniform as possible;
first, the items are grouped into class a, the objective function:
Figure FDA0002575149060000031
wherein, costijRepresenting the degree of association between the i commodity and the j commodity on two different shelves in the a shelves;
then, for the items on a shelves, genetic iteration is carried out respectively, and the objective function is as follows:
Sx＝max(∑costxij)
wherein, costxij≥0，
Figure FDA0002575149060000032
And the x objective functions respectively take fuzzy optimal values.
7. The warehousing cargo space optimization layout method based on the order gray correlation analysis according to claim 5 or 6, characterized in that the weight clustering method or the two-level clustering method is solved based on a genetic algorithm.
8. A warehousing goods space optimization layout system based on order grey correlation analysis is characterized by comprising:
the data preprocessing module is used for acquiring a plurality of order information and constructing an order-ex-warehouse frequency matrix according to ex-warehouse information of the plurality of order upper items;
the relevancy calculation module is used for calculating the relevancy among the items in the order by adopting gray relevancy according to the order-ex-warehouse frequency matrix;
the item clustering module is used for clustering the items based on the association degree among the items;
and the goods location optimizing module is used for optimizing the goods location of the item according to the clustering result and the full turnover rate principle.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, implement a warehousing goods space optimization layout method based on order grey correlation analysis as claimed in any one of claims 1-7.
10. A computer readable storage medium for storing computer instructions, wherein the computer instructions, when executed by a processor, implement a warehousing goods space optimization layout method based on order grey correlation analysis according to any one of claims 1-7.
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