CN113762563B - Warehouse goods space optimization layout method and system based on order gray correlation analysis - Google Patents

Warehouse goods space optimization layout method and system based on order gray correlation analysis Download PDF

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CN113762563B
CN113762563B CN202010651540.1A CN202010651540A CN113762563B CN 113762563 B CN113762563 B CN 113762563B CN 202010651540 A CN202010651540 A CN 202010651540A CN 113762563 B CN113762563 B CN 113762563B
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邹霞
于兴
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Shandong University of Finance and Economics
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Abstract

The invention discloses a warehouse goods space optimizing layout method and system based on order gray correlation analysis, wherein the method 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 items on the orders; calculating the association degree between items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix; clustering the items based on the association degree between the items; and optimizing the goods positions of the items according to the clustering result and the full turnover rate principle.

Description

Warehouse goods space optimization layout method and system based on order gray correlation analysis
Technical Field
The invention belongs to the technical field of automatic warehousing, and particularly relates to a warehouse goods location optimization layout method and system based on order gray 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 electronic commerce platform is continuously improving the logistics speed. Taking the meeting of unique products, jingdong and cuisine birds as an example, the storage is also basically fully automated, the traditional 'people to goods' selection mode is changed, the 'goods to people' selection is realized by adopting different forms of loading and unloading and carrying equipment, only a very short time is required from order placement to commodity delivery, and the problems of order backlog, delivery bottleneck and the like are effectively reduced. Most of the related order picking studies focus on order clustering, item clustering and optimization of picking paths.
In the item clustering research, the commodities with high relevance are most prone to be concentrated together, so that the manual picking efficiency is improved, and the picking path and time are shortened. However, AVS/RS (Autonomous Vehicle Storage and Retrieval System) has the completely opposite principle of cluster analysis, and commodities with high association degree are separately placed, so that the utilization rate of the machine and the order reaction speed can be improved. The method for clustering items by order gray correlation analysis is a heuristic clustering method, has the characteristics of rapidness and easiness in implementation, and an existing gray correlation analysis model comprises the following steps: the dunghill gray correlation, the gray correlation based on the similarity view, the gray correlation based on the proximity view, etc. However, if the method is directly applied to a B2C order, if a Deng's gray correlation analysis method is adopted, the distinction of the correlation is poor, and the calculation is complicated; if the gray correlation analysis method of the similarity view angle or the proximity view angle is adopted, when the ex-warehouse frequency of the i product and the j product presents the cross distribution condition, the model cannot present the real correlation of the two products. That is, the existing gray correlation analysis method is no longer applicable to item clustering in the industry.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a warehouse goods location optimizing layout method and system based on order gray correlation analysis, and the improved gray correlation degree is adopted to calculate the correlation degree between the goods items, so that the correlation of the goods items on the order can be well reflected, the calculated correlation degree accords with the actual correlation degree, and the rationality of the subsequent goods location optimization is ensured.
To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
a warehouse goods space optimizing layout method based on order gray 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 items on the orders;
calculating the association degree between items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
clustering the items based on the association degree between the items;
and optimizing the goods positions of the items according to the clustering result and the full turnover rate principle.
Further, constructing an order-out frequency matrix according to the out-of-stock information of the plurality of items on the orders 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 ex-warehouse conditions of the items in all orders are recorded;
if an item is included in an order, 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 between items in the order using the gray association comprises:
extracting a warehouse-out information sequence of each item based on the order-warehouse-out frequency matrix;
for any two items i and j, the corresponding ex-warehouse information sequences are respectively recorded as follows: x is X i ＝(x i (1)，x i (2)，……，x i (on))，X j ＝(x j (1)，x j (2)，……，x j (on)), wherein on represents the amount of orders, x i (k) And x j (k) The information of the item i and the item j in the k order is respectively represented, the value is 1 or 0, and k is E [1,0n ]]；
The association degree calculation formula between item i and item j is:
wherein, when x i (n) and x j (n) when the correlation coefficient is not zeroWhen x is i (k)＝x j (k) When=0,>
further, the items are clustered by adopting a weight clustering method or a two-stage clustering method.
Further, clustering the items by using the weight clustering method comprises:
assuming that goods are placed on a row of goods shelves in advance, each row of goods shelves contains b layers, each row of goods shelves is provided with a lifting machine, each layer is provided with an independent shuttle, the quantity of goods is placed as evenly as possible, and the objective function is as follows:
wherein, wherein: g=a×a, h=a×b×b (b-1)/2, x i ，y j ≥0，x i Representing the degree of association of items between different shelves, y j Representing item association between inner layers of a shelf, C 1 Weight for representing degree of association of items between shelves C 2 Representing item association weights between inner layers of the shelf.
Further, clustering the items by a two-stage clustering method comprises:
assuming that goods are placed on a row of goods shelves in advance, each row of goods shelves comprises a layer b, each row of goods shelves is provided with a lifting machine, each layer is provided with an independent shuttle, and the quantity of goods is placed as evenly as possible;
first, items are clustered into class a, objective function:
wherein, cost ij Representing the degree of association between i and j items on two different ones of the a shelves;
then, carrying out genetic iteration on the items on the a shelves respectively, wherein the objective function is as follows:
wherein, costx ij ≥0，i＜j，/>The x objective functions are respectively moduloPaste optimum.
Further, the weight clustering method or the two-stage clustering method is solved based on a genetic algorithm.
One or more embodiments provide a warehouse cargo space optimization layout system based on order gray 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 items on the orders;
the association degree calculating module calculates the association degree between items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
the item clustering module clusters the items based on the association degree among the items;
and the goods position optimizing module optimizes the goods positions of the goods 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 running on the processor, which when executed by the processor, implement the order gray correlation analysis based warehouse cargo location optimization layout method.
One or more embodiments provide a computer readable storage medium storing computer instructions that when executed by a processor implement the order gray correlation analysis based warehouse cargo space optimization layout method.
The one or more of the above technical solutions have the following beneficial effects:
by analyzing the characteristics of the B2C E-commerce order, the traditional gray correlation model is improved, and the improved gray correlation model suitable for the B2C E-commerce order is provided, so that the correlation among commodity items on the same order can be well reflected, the calculated correlation degree accords with the actual correlation degree, and the rationality of the subsequent goods space optimization is ensured.
A clustering model and a secondary clustering model based on weights are respectively constructed and applied to clustering of items, and a roadway and a layer where commodities are located are sequentially distributed, so that the utilization rate of equipment is improved as much as possible, the order processing time is shortened, and the energy consumption of the equipment is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a warehouse cargo space optimizing layout method based on order gray correlation analysis according to one embodiment of the invention;
FIG. 2 is a flow chart of clustering based on a genetic algorithm according to one embodiment of the present invention;
fig. 3 is a schematic view of adaptive convergence in a clustering process based on a genetic algorithm according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. 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 present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
The embodiment provides a warehouse goods space optimizing layout method based on order gray correlation analysis, which calculates the correlation degree among different goods items by adopting improved gray correlation degree according to the characteristics of an E-commerce order, and then clustering the goods items by adopting a genetic algorithm in combination with the characteristics of population evolution, so as to optimize warehouse layout and achieve the aim of improving logistics efficiency. 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 items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
step 3: clustering the items based on the association degree between the items;
step 4: and optimizing the goods positions of the items according to the clustering result and the full turnover rate principle.
In the step 1, due to the characteristics of an AVS/RS system, a plurality of commodities of a single item are stored in a single turnover box, and a cargo space is allocated. The order quantity of each item in the warehouse is recorded on each order of the B2C electronic commerce, and the storage form of the product and the operation characteristics of the AVS/RS can meet the requirement of the item quantity of most orders in actual conditions when the turnover box is taken out of the warehouse once, so that in the research, the order quantity of the item can be ignored, only the ordered frequency of the item is considered, and the delivery frequency C epsilon (0, 1) on the ordered item is considered.
In this embodiment, 1000 orders of an e-commerce warehouse are extracted, which contains 189 kinds of commodities in total, and each order contains the information of the leave of the 189 kinds of commodities, namely, let on=1000 and sn=189. And constructing an order-ex-warehouse frequency matrix. Each row of each order-ex-warehouse frequency matrix corresponds to one order, each column corresponds to one type of commodity, ex-warehouse conditions of all commodities in all orders are recorded, the number of rows is consistent with the number of orders, the number of columns is consistent with the number of commodity types contained in all orders, and in the embodiment, the order-ex-warehouse frequency matrix is 1000×189. For each order, if the order contains a certain commodity, the numerical value of the corresponding position in the matrix is marked as the commodity delivery frequency 1, and if the commodity is not contained, the delivery frequency is 0. Each column in the order-out frequency matrix is marked as an item out information sequence: x is X i ＝(x i (1)，x i (2)，......，x i (n)), where x i (k) Indicating the ex-warehouse information of the product i in the kth order, and the value is 1 or 0.
1000 product orders are selected, 189 products are contained, the product ex-warehouse frequency is used as a source data sequence to perform cluster analysis on the E-commerce products, the purpose of reducing the E-commerce product ex-warehouse waiting time waste is achieved, 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.
The existing gray correlation analysis model comprises: the dunghill gray correlation, the gray correlation based on the similarity view, the gray correlation based on the proximity view, etc. The applicability of the gray correlation analysis model in the e-commerce product clustering problem is sequentially analyzed.
(1) Dengshi gray correlation
Gray correlation coefficient ω:
wherein the resolution coefficientThe number of sequence lines i epsilon (1, 2,3 … sn-1), the number of elements c in each sequence.
Gray correlation degree R:
from the data source characteristics herein, X (k) ∈ { X|X=0 or 1}, soThen->Or 1, so only two correlation coefficients can be calculated by adopting the Deng gray correlation degree.
Let it be assumed that at x 0 (k) And any x i (k) In the comparison of sequences (x 0 (k) And x i (k) Representing the ex-warehouse frequency of product 0 and product i in 1000 orders, respectively), there are n sets of data corresponding to 0,1 and (1000-n) sets of data corresponding to 0,0 or 1, respectively, then
It can be seen that the degree of correlation R is related to n only, but in the practical problem studied here, when the data corresponding to 1,1 appears to represent that there is a correlation between the two sets of data, and when 0,1 or 0,0 appears to represent that there is no correlation between the two products in the order, however the model does not distinguish between the 0,0 and 1,1 data distributions; in addition, the reference sequence is required to be continuously transformed by adopting the model to calculate the relevance of the e-commerce products, and the calculation is complicated. The dunnii gray correlation model is not suitable for product cluster analysis based on the product order condition of the ex-warehouse frequency data.
(2) Gray correlation degree based on similarity view angle
Set system sequence X i ＝(x i (1)，x i (2)，……，x i (on))，X j ＝(x j (1)，x j (2)，……，x j (on)) is:
the correlation coefficient of the product j and the product i in the nth order is recorded asOrder theWhen->And (2) is->Is equal in length, and is a 1-time-distance sequence,
then the degree of association epsilon ij ：
According to the principle of gray correlation of similarity visual angles, the model is used for zeroing the data starting points, is suitable for measuring the similarity of geometric shapes among sequences, cannot reflect the proximity degree of commodities among orders, and is not suitable for being applied to B2C E-commerce product cluster analysis.
The calculation illustrates: assuming that the frequency of each delivery of the i product in on orders is 1, and the frequency of each delivery of the j product in on orders is 0, the distribution situation is shown in the table, and the association degree of the i product and the j product is 0 according to the clustering principle.
Table 1: i, j product ex-warehouse frequency distribution schematic table
k 1 2 3 …… on
i productFrequency of delivery (secondary) 1 1 1 …… 1
j product frequency of delivery (secondary) 0 0 0 …… 0
The gray correlation calculation formula according to the similarity view angle can be known as follows:
therefore->That is, the result of the gray correlation degree is 1, which obviously does not match the actual situation.
(3) Gray correlation based on proximity view angle
Set system sequence X i ＝(x j (1)，x i (2)，……，x i (on))，X j ＝(x j (1)，x j (2)，……，x j (on)) and remembers the correlation coefficient of products i and j in the kth order as
Order the
When X is i And X is j Are equal in length and are all 1-at the time of the time-distance sequence,
then the degree of association ρ ij ：
The gray correlation based on the proximity view angle is used for measuring the proximity of the behavior sequence on the same order, and from the data sample characteristics of the text, the e-commerce product cluster 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, we can find that when the ex-warehouse frequency of the i product and the j product presents the cross distribution condition, the model cannot present the real correlation of the two products.
The calculation illustrates:
assuming that the ex-warehouse frequency of the first 500 orders is 1 and the ex-warehouse frequency of the last 500 orders is 0 in 1000 orders of the i product; and the ex-warehouse frequency of the j products in the first 500 orders is 0, the ex-warehouse frequency of the j products in the last 500 orders is 1, the frequency distribution situation is shown in the table, and the association degree of the products i and j is 0 according to the clustering principle of the problems researched in the table.
Table 2: i, j product ex-warehouse frequency distribution schematic table
k 1 2 …… 500 501 502 …… 1000
i product frequency of delivery (secondary) 1 1 1 1 0 0 0 0
j product frequency of delivery (secondary) 0 0 0 0 1 1 1 1
The gray correlation calculation formula according to the proximity view angle can be as follows:
the correlation calculation result obviously does not accord with the actual situation. Consider the case where the model cannot exhibit an actual degree of association when the two sequences are distributed across, and cannot distinguish between the 0,0 correspondence and the 1,1 correspondence. Therefore, the present embodiment makes the following corrections on the basis of the proximity gray correlation model:
let the correlation coefficient(when x i (k) And x j (k) Not zero at the same time);
if an integer k exists, k is E [1, on]So that x is i (k)＝x j (k) =0, then
The corrected degree of association ρ ij * ：
It can be seen that the corrected association satisfies even symmetry, proximity and normalization in the gray association routine.
The improved grey correlation based on the proximity perspective has the following properties:
1) Gray correlation degree ρ ij * ∈(0，1)
2)ρ ij * ＝ρ ji * With even symmetry
3) If X i ＝X j =0 or X i Around X j Swing and X i At X j The upper area is equal to the lower area, ρ ii * ＝0.001≈0
4) When X is i ＝X j ＝1，ρ ij * ＝1
Based on the above analysis, the present embodiment calculates the degree of association between items using the corrected gray degree of association based on the proximity view angle.
In the step 3, the system needs to meet 189 item storage requirements, so that AVS/RS is set as double roadways, each roadway is responsible for vertical operation by one elevator, and the number of goods grids in each roadway is 100; the number of layers of the goods shelf is 4, the height of a single layer is 3 meters, and each layer is provided with a shuttle car to finish horizontal operation; the number of shelf columns is 25, the column width is 2 meters, and the total number of the shelf columns is 200 storage positions. After the goods are delivered out of the warehouse, the goods are delivered to a picking platform by a conveyor, and delivery operation pulled by orders is realized by utilizing an order global table.
The embodiment provides two methods for clustering items, which specifically are as follows: a weight clustering method based on a genetic algorithm and a two-stage clustering method based on the genetic algorithm.
(1) Weight clustering method based on genetic algorithm
Assuming that sn commodities are placed on a row of shelves in advance, each row of shelves contains b layers, each row of shelves is provided with a lifting machine, each layer is provided with an independent shuttle, the number of the commodities is placed as evenly as possible, and the objective function is as follows:
wherein, wherein: g=a×a
h＝a*b*(b-1)/2
x i Representing the degree of association of items between different shelves, y j Representing item association between inner layers of a shelf, C 1 Weight for representing degree of association of items between shelves C 2 Representing item association weights between inner layers of the shelf. Considering that only one elevator is arranged on each row of goods shelves, and each layer is provided with a shuttle, the weight of the relevance of the goods items among the goods shelves tends to be increased, and C is selected herein 1 ＝0.8，C 2 ＝0.2。
(2) Two-stage clustering method based on genetic algorithm
The algorithm adopts the unit association coefficient between shelves and the unit association coefficient weight in the shelves to construct an objective function without changing, and considers that the artificial factors determined by the weights possibly generate errors to influence the optimal solution, the algorithm adopts two-stage clustering as a comparison group for comparison, and the two-stage clustering comprises the following steps:
1) Inputting order information, calculating correlation between items
2) First-stage clustering: adopting a genetic algorithm to gather items into a class a, and adopting an objective function:
wherein, cost ij Representing the degree of association between i and j items on two different ones of the a shelves.
3) Second-stage clustering: and (3) carrying out genetic iteration on the items on the a shelves respectively, wherein the objective function is as follows:
Sx＝max(∑costx ij )
4) Wherein, costxij is more than or equal to 0,i＜j，/>the x objective functions respectively take fuzzy optimal values.
The evolution process of the organism depends on the evolution of population genes, and the population genes consist of gene libraries of each generation of individuals, so that when the gene libraries of the population are continuously and iteratively updated, the evolution of the organism is driven.
For the weight clustering method based on the genetic algorithm and the two-stage clustering method based on the genetic algorithm, the genetic algorithm is adopted to solve the objective function, and the solving process is as follows:
1) Randomly generating a.b initial populations, each population comprising sn commodity products, encoding their positions to form chromosomes;
2) Calculating fitness of each individual to generate a first generation population (initial population);
3) Judging whether a termination condition is met, if so, outputting a result, and if not, continuing;
4) Selecting 20 times of roulette, and randomly selecting 2 individuals from the 20 times of selections to be used as iteration objects;
5) Judging the cross probability, if the random probability is lower than the cross probability, performing cross operation, and if the random probability is higher than the cross probability, skipping;
6) Judging the variation probability, if the random probability is lower than the variation probability, performing variation operation, and if the random probability is higher than the variation probability, skipping;
7) The new chromosome replaces the corresponding object in the original population to form a new generation population, and jumps 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; however, the model needs to continuously adjust the weight, so that the clustering result is more optimized.
Two clustering modes are realized by utilizing a genetic algorithm, so that a better clustering result can be ensured, and meanwhile, the clustering can be realized rapidly.
In order to simplify the complexity of goods space optimization, the goods space is allocated mainly according to the degree of correlation among the goods items and the ex-warehouse frequency of the goods items, and influence factors such as gravity are not considered, so that the goods space optimization method is only suitable for goods space optimization of similar density goods.
Example two
An object of the present embodiment is to provide a warehouse cargo space optimizing layout system based on order gray correlation analysis, the system including:
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 items on the orders;
the association degree calculating module calculates the association degree between items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
the item clustering module clusters the items based on the association degree among the items;
and the goods position optimizing module optimizes the goods positions of the goods according to the clustering result and the full turnover rate principle.
Example III
It is an object of the present embodiment to provide an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor perform 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 items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
step 3: clustering the items based on the association degree between the items;
step 4: and optimizing the goods positions of the items according to the clustering result and the full turnover rate principle.
Example IV
It is an object of the present embodiment to provide a computer readable storage medium storing computer instructions which, when executed by a processor, perform 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 items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
step 3: clustering the items based on the association degree between the items;
step 4: and optimizing the goods positions of the items according to the clustering result and the full turnover rate principle.
The steps involved in the second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description of the second embodiment refers to the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
Simulation results:
based on the improved grey correlation mining method, the correlation degree between commodities is calculated, the calculated correlation degree is used as an initial data importing program, then clustering is carried out by adopting a genetic algorithm, and the optimizing and converging process of the genetic algorithm is shown in figure 3. The graph shows that the adaptability is continuously converged through the clustering of the genetic algorithm, the association degree of commodities between the roadway and the goods shelf is finally close to 37, and the association degree is obviously improved compared with an initial value. Through multiple experiments, both groups of genetic algorithms achieve the convergence of the added fitness, and the commodity clustering distribution results are shown in table 3.
Table 3: item clustering result table based on genetic algorithm
And optimizing the goods positions of the items according to the clustering result and the full turnover rate principle. Items with the same layer of goods in the same roadway are ordered according to the height of the ex-warehouse frequency, and items with high ex-warehouse frequency are closer to I/O; in the same roadway, counting the ex-warehouse frequency of the item subsets, and counting the item subsets with high ex-warehouse frequency to be closer to I/O. And respectively carrying out cargo space optimization on the two clustering results, wherein the results are shown in table 4.
Table 4: goods position distribution table for goods items after goods position optimization
The clustering results under the two conditions of weight clustering based on the improved grey correlation degree and secondary clustering based on the improved grey correlation degree are shown in table 5 after goods space optimization is carried out according to the ex-warehouse frequency.
Table 5: simulation result table
The authors perform secondary clustering among items based on K-means according to similarity coefficients among items, and perform cargo space optimization of AVS/RS according to clustering results, so that research results are used as targets for comparison analysis to verify and improve grey relevance and practicability of a clustering method.
1. Task processing time analysis
In terms of task processing time, the operation time of the secondary clustering based on the improved gray correlation degree is shortest, compared with the K-means secondary clustering, the task processing time is reduced by about 0.85%, the rationality of improving the gray correlation degree for item clustering is demonstrated, and the longer weight clustering operation time is demonstrated that the influence of the weight value on the clustering effect is larger.
2. Equipment operation time analysis
In terms of equipment average working time, compared with K-means clustering, in the case of cargo space allocation based on secondary clustering for improving gray correlation, the working time of a shuttle is reduced by 1.64% although the working time of a hoist is increased by 2.6%.
3. Device idle time analysis
In the aspect of equipment average idle time, compared with K-means clustering, under the condition of two-level clustering based on improved gray correlation, the idle time of a lifter and the shuttle is obviously reduced, the operation time of the lifter is reduced by 4.43%, but the operation time of the shuttle is reduced by 1.63%.
By constructing the simulation model, the effectiveness of the improved gray correlation model in AVS/RS cargo allocation is verified, and experimental results show that compared with K-means clustering, the two-level clustering model based on the improved gray correlation model can compress the operation processing time by 0.85%, and meanwhile the equipment idle time is reduced.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.

Claims (8)

1. The warehouse goods space optimizing layout method based on the order gray correlation analysis is characterized by comprising 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 items on the orders;
calculating the association degree between items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
clustering the items based on the association degree between the items;
optimizing the goods positions of the items according to the clustering result and the full turnover rate principle;
the calculating of the association degree between the items in the order by using the gray association degree comprises the following steps:
extracting a warehouse-out information sequence of each item based on the order-warehouse-out frequency matrix;
for any two items i and j, the corresponding ex-warehouse information sequences are respectively recorded as follows: x is X i -(x i (1)，x i (2)，……，x i (on))，x j -(x j (1)，x j (2)，……，x j (on)), wherein on represents the amount of orders, x i (k) And x j (k) The information of the item i and the item j in the k order is respectively represented, the value is 1 or 0, and k is E [1, on)]；
The association degree calculation formula between item i and item j is:
wherein, when x i (n) and x j (n) when the correlation coefficient is not zeroWhen x is i (k)＝x j (k) When=0,>
and clustering the items by adopting a weight clustering method or a two-stage clustering method.
2. The method for optimizing layout of warehouse cargo space based on order gray correlation analysis as claimed in claim 1, wherein constructing an order-out frequency matrix based on out-of-stock information of items on the plurality of orders 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 ex-warehouse conditions of the items in all orders are recorded;
if an item is included in an order, 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 for optimizing layout of warehouse goods locations based on order gray correlation analysis as claimed in claim 1, wherein the clustering of the items using the weight clustering method comprises:
assuming that goods are placed on a row of goods shelves in advance, each row of goods shelves contains b layers, each row of goods shelves is provided with a lifting machine, each layer is provided with an independent shuttle, the quantity of goods is placed as evenly as possible, and the objective function is as follows:
wherein, wherein: g=a×a, h=a×b×b (b-1)/2, x i ，y j ≥0，x i Representing the degree of association of items between different shelves, y j Representing item association between inner layers of a shelf, C 1 Weight for representing degree of association of items between shelves C 2 Representing item association weights between inner layers of the shelf.
4. The method for optimizing layout of warehouse goods locations based on order gray correlation analysis as claimed in claim 1, wherein the clustering of the items using the two-stage clustering method comprises:
assuming that goods are placed on a row of goods shelves in advance, each row of goods shelves comprises a layer b, each row of goods shelves is provided with a lifting machine, each layer is provided with an independent shuttle, and the quantity of goods is placed as evenly as possible;
first, items are clustered into class a, objective function:
wherein, cost ij Representing the degree of association between i and j items on two different ones of the a shelves;
then, carrying out genetic iteration on the items on the a shelves respectively, wherein the objective function is as follows:
Sx＝max(∑costx ij )
wherein, costx ij ≥0，i＜j，/>The x objective functions respectively take fuzzy optimal values.
5. The warehouse cargo space optimizing layout method based on the order gray correlation analysis as claimed in claim 3 or 4, wherein the weight clustering method or the two-stage clustering method is solved based on a genetic algorithm.
6. A storage goods space optimizing layout system based on order gray correlation analysis, which realizes the storage goods space optimizing layout method based on order gray correlation analysis as set forth in any one of claims 1-5, 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 items on the orders;
the association degree calculating module calculates the association degree between items in the order by adopting gray association degree according to the order-ex-warehouse frequency matrix;
the item clustering module clusters the items based on the association degree among the items;
and the goods position optimizing module optimizes the goods positions of the goods according to the clustering result and the full turnover rate principle.
7. An electronic device comprising a memory and a processor and computer instructions stored on the memory and run on the processor, wherein the computer instructions when executed by the processor implement the warehouse cargo space optimization layout method based on order gray correlation analysis of any of claims 1-5.
8. A computer readable storage medium storing computer instructions which, when executed by a processor, implement a warehouse cargo space optimizing layout method based on order gray correlation analysis as claimed in any one of claims 1-5.
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